Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical  or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.Fusarium head blight (FHB) incited by  Hordeum vulgare L.) is one of the most ancient grains, and is currently the fourth-most produced cereal globally measured both in area harvested and yield  Petch], is a devastating disease of barley. Primary concern of the disease is due to associated trichothecene mycotoxins such as deoxynivalenol (DON), which are potent inhibitors of protein synthesis . Significant undertakings have been made in genetic studies of biparental populations to identify quantitative trait loci (QTL) for breeding resistant barley cultivars  are characterized by their large genomes, with frequent repetitive elements  and variants  tasks such as classification, next sentence prediction and topic identification  compare the accuracy of the GPTransformer model to existing genomic prediction methods, (ii) study the outcomes of the model if categorical encoding was used or marker frequency-based encoding was used, and (iii) investigate the effect of feature selection on genomic prediction using mutual information and examine the biological relevance of the top markers identified by the mutual information method. The Transformer network is trained using a graphical processing unit (GPU). As the internal mechanism of the Transformer creates a four dimensional matrix of size  at certain point, which requires a large amount of GPU memory, the feature selection process also helps us to solve the GPU memory issue of the Transformer.The seed for a genetic panel was collected for a total of 400 spring habit two-row barley genotypes of mixed usage types of malt (171) and feed (229). Pure seed was provided by the Crop Development Center, University of Saskatchewan, Saskatoon, Canada (CDC) for a diversity panel of barley (92) breeding lines tested in the Western Canadian Cooperative Two-Row Barley Registration Trials (WCTBRT) 1994–2006  using a custom iSelect-50 K SNP microarray . Plots were sown with approximately 30–40 seeds and consisted of 0.9 m rows, 30 cm row spacing  or 1 m rows, 34 cm row spacing (Carman). Two inoculation methods were used. Brandon and Carberry experiments were inoculated by the grain spawn method, where maize kernels infected with 2 isolates each of 3ADON- and 15ADON-producing strains of F. graminearum were spread on the soil surface at 5 gm−2 at flag leaf then weekly for 3 total applications. Irrigation was applied after first inoculum application until all plots were rated. Experiments at Carman were sprayed with a macroconidia suspension of 3ADON and 15ADON isolates in equal proportions and standardized to 5 × 104 spores ml−1. Plots were misted and sprayed at 75% spike emergence and then again 2 days following.FHB nurseries were grown in 2014 and 2015 at 3 locations: Brandon, Manitoba (49°51'56.0”N 99°58'57.7”W); Carman, Manitoba (49°29'52.9”N 98°02'19.2”W) and Carberry, Manitoba (49°54'16.6”N 99°21'19.0”W). The experiments followed a randomized complete block design at all locations (Plots were rated at the soft dough (Zadoks - Z85) stage. A visual scale (0–5) was used to evaluate a composite measure of incidence and severity , where 0 = no infection. 1 = incidence low, up to 5% of spikes; severity low, 1 or 2 kernels per spike affected (up to 7% of head). 2 = incidence low to moderate, 5–15% of spikes infected; severity low to moderate, 1–4 kernels (up to 15% of head). 3 = incidence moderate, 15–30% of heads; severity moderate, 2–8 kernels (up to 25% of head). 4 = incidence moderate to high, 30–50% of spikes infected; severity moderate to high, 4–12 kernels (up to 40% of head). 5 = incidence high, 50% or more spikes affected; severity high, 5 to 15+ kernels (up to 50%+ of head diseased). Additional data were collected on days to heading (date 50% of row headed minus seeding date) at Brandon and Carberry and plant height (distance of soil surface to tip of spike excluding awns) at all locations.Ⓡ 5/5  as per kit protocol (limit of detection = 0.1 mg kg-1). Samples were each tested in sub-sample pairs, where samples deviating by > 10% were repeated.Grains were harvested at maturity, using a stationary research combine with low wind speed setting, then dried in a high capacity drier for a few days. Grains were cleaned using an SLN3 sample cleaner . A 20 g subsample was removed, cleaned free of debris and/or chaff then ground using a Perten 3610 lab mill with fine particle disc set . Deoxynivalenol content was analyzed by enzyme-linked immunosorbent assay (ELISA) technique using VeratoxA can have two alleles, A and a and three genotypes AA, Aa, and aa. We replaced the classical representation of genotype of a marker  with genotypic frequency by applying Hardy-Weinberg equilibrium .a is q=1 − p. After calculating the allele frequency of a genetic marker, the expected genotypic frequencies of genotype AA, Aa and aa for a specific marker is obtained from p2, 2pq, and q2. These expected genotypic frequencies are used as marker values for the machine learning algorithms.As the barley population is diploid, a locus Acquaah, . The freIn this paper, we examine the application of Transformer, a variety of neural networks. Deep neural networks have been applied to GS in the past. See the survey of Montesinos-López et al.  for detan = 8-dimensional expanded representation of markers. In this work, a feed-forward neural network is applied as the embedding layer. The input of the embedding layer is m markers and the output is an m × n-dimensional vector. The vector is then reshaped to an -matrix to obtain m expanded representation of the markers. The output of the embedding layer is then passed to a multi-head attention network.The purpose of the embedding layer is to obtain an q, a key vector k and a value vector v by applying a linear transformation on the embedding. The query vector is the candidate expanded representation with respect to which the attention is measured while the keys are the set of expanded representations where the importance scores are assigned. The attention score is calculated as:Q is a matrix of all the queries, K is a matrix of all the keys and V is a matrix of all the values and dk is the dimension of keys. The last step is to generate a summation of the previous step, which produces the self-attention layer's output. In a multi-head attention setting, the Transformer model creates h independent linear representation from queries, keys and values. These h representations are then concatenated and passed through a linear projection layer to obtain the final output.The multi-head attention network is based on the self-attention mechanism. The input of this layer is the expanded representation of the markers obtained in the embedding layer. The main building block of the multi-head-attention is the self-attention mechanism that calculates the attention score for all other expanded representation of markers with respect to a specific expanded representation. To calculate the self-attention, at first, each embedded marker creates three vectors: a query vector N in N times and the output of the Nth encoder block will be the input of the feed-forward layer that predicts the phenotypes.In M markers as the input and performs a linear transformation to produce an n-dimensional hidden representation of the markers. This hidden representation is the input of the batch-normalization layer. The batch-normalization layer normalizes the current batch by its mean and standard deviation. As the feed-forward network and batch normalization performs a linear operation on the input data, we apply the activation function ReLU to the batch-normalization layer's output. ReLU will return 0 if the input is ≤ 0; otherwise, it will return x where x is the input and thus, introduces the non-linearity. The output of ReLU is going to be the input of the next feed-forward network. In N times. The output layer is the feed-forward network that predicts the phenotypes.We now describe our second deep learning model which is based on feed-forward network with residual connection. In general terms, a residual neural network is a neural network that has one or more residual connections. Residual connections allow skipping of layers in a neural network. In our implementation, the first layer of the Residual Fully Connected Neural Network (RFCNN) is a feed-forward neural network that takes n × p matrix of encoded genotypes and a n-dimensional vector of known corresponding phenotype responses were supplied. At each node, splits based on the SNP markers are considered and the highest quality split is chosen; in this case, the quality measure of the split is the mean squared error. This process continues recursively, splitting the data into subsets of instances at each internal node, until the branches terminate with leaf node and produce a response value. The values at the leaf nodes are the arithmetic mean of the known associated response variables to the instances that are present in the adjoining edge. With a fitted model, genotypes with unknown phenotype response can be predicted by iteratively testing the SNP marker values against the trees decision procedure until a leaf node is reached.Decision Trees (DTs) are common statistical and machine learning methods used for predictive modeling. The baseline DT regressor models used in this analysis were built with the Scikit-learn  for each of n input genotypes. X is the n × p matrix (n genotypes and p markers in each genotype), β is a p-dimensional column vector of unknown coefficient parameters, and ε is the n-dimensional unknown random error column vector. The linear regression model tries to learn β to make phenotype prediction.Like the DT models, the linear regression (LR) models used in this analysis were built with the Scikit learn Python module using default settings. LR fits a linear model by calculating coefficients on the independent terms that minimize the sum squared error between the known observations responses and the approximated predictions. The following is the form of the linear models:y is the n-dimensional column vector containing response variables (phenotype) for each of n genotypes, F is the n × q matrix of known fixed-effects, δ is a q-dimensional column vector of unknown fixed-effects parameters, Z is the n × p matrix of known random-effects (n encoded genotypes), γ is the p-dimensional column vector of unknown random-effects parameters, finally, ε is the n-dimensional unknown random error column vector. It is assumed that the residuals ε and random-effects γ are normally distributed, I is the identity matrix and The ridge regression (RR) models were built using JMP Genomics 9 . This time, in each split, the test set also changes along with the training and validation set.To further investigate the reliability of the proposed GPTransformer model, we again perform Feature selection methods identify features that contribute to a specific expression. In our work, the purpose of the feature selection is to identify those genetic markers that contribute toward low FHB or low DON levels. We applied mutual information feature selection, a filter based method, where the input is the genetic markers and the phenotypes and the output is a mutual information score (ranging 0-1). Discretization was performed where we divide the genotypes into three bins based on phenotypes . The categories are the labels and the genetic markers are the features of mutual information algorithm which produces a mutual information score for each marker. The final mutual information score for each marker is the average mutual information score over the three training sets. Markers with average mutual information of ≥ 0.02 were selected.n = 8). Thus, if each genotype contains m genetic markers , the input embedding layer's output will be an  matrix. This  matrix is the input of the multi-head attention. As the multi-head attention computes pairwise attention between each marker, the operation will result in an  matrix where h is the number of heads. This operation has significant memory requirements for the GPU. For instance, on this dataset, it requires over 48 GB of memory.The input embedding of the Transformer network converts each marker to a eight-dimensional vector  dimensional matrix where f is the number of markers. This  matrix will be the input of the Transformer encoder. Our Transformer neural network contains two Transformer encoder blocks (N = 2). We use two heads (h = 2) for each multi-head attention layer and each feed-forward block inside the Transformer encoder contains 256 hidden neurons. The final Transformer encoder's output is also an  matrix which is flattened to create a vector that contains f × n elements. This vector is the input of the last feed-forward network which contains one output neuron. We use the mean square error (MSE) loss function along with the Adam optimizer. The learning rate of the optimizer is 1e − 5. If there is no improvement in MSE loss in the validation set for ten consecutive epochs, we stopped the training.To circumvent memory limitations, only selected features of mutual information are taken as the Transformer network input. We also pass only one genotype at each batch for training. Thus, the input of the embedding layer is all the markers of a genotype selected by the mutual information algorithm. The output is an  or f markers (feature selected) as an input and produces a 512-dimensional hidden representation. Each subsequent feed-forward layer takes the previous layer's input and produces a 512-dimensional hidden representation. The last feed-forward layer contains only one output neuron. We stack five residual blocks (N = 5) one after another. We also use MSE as the loss function and Adam as the optimizer with a learning rate of 1e − 5.The first feed-forward layer takes The Pearson Correlation Coefficient (PCC) was calculated to measure the performance. The PCC calculates the linear relation between the true output and predicted output. The PCC value ranges from −1 to 1 where 1 indicates a perfect linear relation between the predicted phenotypes and the true phenotypes whereas −1 indicates the opposite relationship between the true and predicted phenotypes.In the remainder of the paper, especially in the figure, we will denote genotype based encoding as HW, categorical encoding as CAT, Decision Tree algorithm as DT, Linear Regression as LR and Residual Fully Connected Neural Network as RFCNN.-1 (13.96 ± 0.21)  and DON  indicated a degree departure from normality. Departures from normality were most obvious in tail regions of the distributions.The distribution of FHB and DON is shown in  ± 0.21) . For botp < 0.0001). r = −0.18, P < 0.004;0.18, P < 0.0003) and height .Though from AA for gene X and allele AA for gene Y may appear in different frequencies and will have different Hardy-Weinberg values. Thus, the embedding layer does not suffer from multiplying-by-zero problems and improves the performance of the Transformer.Two encoding techniques were implemented: i) categorical encoding  and ii) genotype frequency-based encoding that follows Hardy-Weinberg equilibrium. No single molecular marker dominantly explained significant portions of the variation for FHB or DON. However, specific markers could be identified as “top features” which may be associated with genes of interest which may operate closer to oligogenic vs. polygenic fashion . BiologiIn most of the experiments, when the machine learning or statistical methods are trained using all the markers, they performed better. In Though there is a significant increase in correlation score when all the markers are used for machine learning models, none of the BLUP models show any significant difference in performance when all markers or selected markers are used. Due to GPU memory limits, it was not possible to use all the features for Transformer architectures.Overall, BLUP and Transformer models that use genotype frequency as features obtained better correlation scores than other models. p-value for DON is 0.576 and for FHB is 0.639.To understand how much reliable our GPTransformer model, we train and test the model in three separate traning-validation-test splits. With the GPTransformer model, the average PCC between the target and predicted phenotype for DON is 0.748 and for FHB is 0.703. The BLUP model obtained PCC of 0.789 and 0.681 for DON and FHB, respectively. The PCC we obtained with different splits using GPTransformer and BLUP models are not statisticallly significant as the Transgressive segregation is common for FHB and DON in barley, where it is typical for offspring to deviate from mid-parent value. A greater degree of more susceptible genotypes was observed, which suggests that unique configuration of alleles over multiple loci may be responsible for resistance  and response factor (HORVU7Hr1G033820), where this plant hormone has been associated with FHB severity and yield loss in barley  only indicates the zygosity and remains the same for all the genetic markers. As each allele of a genetic marker is represented by its fixed frequency value, the frequency-based encoding provides us the information of the frequency as well as the zygosity. Though the frequency value remains the same within the same genetic marker for a specific allele, it may differ between different genetic markers. Thus, when the GPTransformer is combined with the frequency-based encoding, it performs better than the traditional categorical encoding-based model. The frequency-based representation is in the range of 0-1 and minimizes issues of vanishing gradient that may occur when training the GPTransformer or other neural network models.The stability of the proposed model is tested with three different training and test data and the result shows the standard deviation of PCC on the test data for FHB is 0.04 and for DON is 0.09. The standard deviation of the BLUP model for FHB and DON for the same three different training and test is 0.008 and 0.04, respectively. The deviation from the average PCC for three different runs is higher for other machine learning methods we experimented with. This shows that the GPTransformer model is stable compared to other machine learning methods and as good as the popular BLUP model.While the time commitment is higher and taking up to an hour to train the GPTransformer, it only took 24 epochs to complete training. The time complexity of the RFCNN is much lower than the Transformer though it took approximately 200 epochs to train. The most expensive task in the Transformer is the self-attention that requires substantial time and memory to complete. We ran both machine learning models on Intel Xeon E5-2690 v4 processor and NVIDIA Tesla P40 GPU, which contains 24 GB memory. With our Transformer architecture, we are able to fit all the markers that have mutual information ≥ 0.02. To fit all the genetic markers, a larger memory or multi-GPU instance is needed.Though the performance of most of the machine learning methods improves when all the markers are used for prediction, the Transformer architecture outperforms other methods with selected markers. To the best of our knowledge, this is the first method that uses Transformer architecture for genomic prediction. This work showed that this method could outperform existing machine learning methods with fewer data and obtain state-of-the-art performance. Based on the performance in the language model domain, it is expected that with an increased amount of data, the performance of the Transformer model will also increase.Our work shows the potential of the Transformer-based method for genomic prediction. Though Transformer generally performed well with a large amount of data in other fields, in this work, we showed that when trained on a small dataset, the Transformer encoder performs equally or better compared to the existing machine learning and statistical methods. As the genotype data generally contains many markers, calculating self-attention in a GPU will require a large amount of GPU memory that may not be available. Our feature selection step in the model addresses the memory issue of the Transformer method. This step reduces the number of markers and identifies the biologically relevant markers for a specific phenotype. We also applied genotype frequency-based encoding for each genotype. This encoding performs better when combined with the Transformer. If a large amount of data is available, the number of Transformer encoder blocks can be increased which may increase the overall performance.2021 by Her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada; This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.GSE188791 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188791).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI's Gene Expression Omnibus; SJ, JT, AB, WF, and MD: conceptualization and methodology. SJ, JT, and NH: software and data analysis. SJ and JT: writing—original draft preparation. AB, SJ, JT, MD, and NH: writing—review and editing. CH: microarray genotyping. AB, WF, and MD: supervision. AB, WF, and JT: project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.This research was funded by the Brewing and Malting Barley Research Institutes (BMBRI), Manitoba Crop Alliance, Growing Innovation: Agri-Food Research and Development Initiative (ARDI) project to WF and AB. Support was also delivered through part of the Barley Cluster project led by Alberta Barley with funding from the Western Grains Research Foundation and Agriculture & Agri-Food Canada (AAFC) under the Growing Forward 2 program.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild.Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we establish a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner, and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, drug development, and fundamental biology.Here we present a detailed characterization of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.The online version contains supplementary material available at 10.1186/s13059-022-02623-z. Arabidopsis thaliana and Drosophila melanogaster, genome-wide association studies (GWAS) and specific crosses have been used to examine complex genetic traits, bridging population association models to more traditional controlled-cross strategies [The relationship between natural genetic variation and the variance of quantitative traits in different species is one of the founding questions in genetics , 2 and hrategies , 10. GWArategies . Howeverrategies , 13. To Mus musculus) is an established vertebrate model for human genetics, and researchers have created panels of recombinant inbred lines (RILs), such as the Collaborative Cross (CC), Diversity Outbred cross (DO), and the BXD cross [The mouse  is a long-established model organism that combines a number of important features [The teleost medaka , which is sufficient and necessary to induce male sex determination [Oryzias curvinotus but not in Oryzias luzonesis. Furthermore, other Oryzias species have a female heterogametic ZZ-ZW sex determination system [Oryzias shows a high divergence between evolutionary closely related species. This renders the genus Oryzias ideal for comparative studies of the evolution of vertebrate sex determination and the genetic cascade of downstream genes.Also with respect to sex determination, medaka is a valuable genetic model. Sex determination and the evolution of sex chromosomes are very active fields of research and are studied in vertebrate and invertebrate model systems . Medaka mination . Interesn system . Thus, sImportantly, medaka is highly tolerant to inbreeding from the wild . IsogeniWe have previously identified a polymorphic wild population, sampled from Kiyosu, Japan . By singr2 reaching a minimum of ~ 0.12 at a distance of approximately 12.5 kb.We have previously reported the identification of a wild medaka population that satisfies critical conditions for the establishment of a vertebrate near-isogenic panel . In JulyTo commence the inbreeding process, we first established founder families by setting up 115 random crosses of single mating pairs from the wild Kiyosu population. For each founder family, we then set up mostly two, but up to five, single full-sibling-pair inbreeding crosses. This resulted in a total of 253 single full-sibling-pair crosses for the F1 generation, which we used to initiate the inbreeding lines. Lines derived from the same founder family are referred to as “sibling lines.” In the subsequent 8 generations of inbreeding, we only used one mating pair per line. In total, 19 of the F1 crosses did not result in productive mating, so we proceeded to inbreed the F2 generation with 234 productive single full-sibling-pair crosses.During the first 9 generations of inbreeding (using only one mating pair per line), to avoid selecting for specific traits, we intentionally did not consider body size, generation time, fecundity, fertilization rate, survival rate, or sex ratio. We continued inbreeding with all lines independent of these traits up to the 9th generation, or until the strain became extinct Fig. A. The onFlavobacterium columnare. Antibiotic treatment stopped mortality within 24 h, indicating that the bacterial infection was the main cause of death. Outbred Kiyosu fish were not affected; we therefore assume that inbreeding led to a higher susceptibility to bacterial infections. In addition, 13 lines were lost due to extreme shifts in the sex ratio. We also observed unproductive crosses in 10 lines, and we terminated the lines when 4 alternative sibling crosses from the line were all unproductive. At the time of submission, the MIKK panel generation F18 comprised a total of 80 near-isogenic inbred lines , medaka mate every day . This mahttps://www.ebi.ac.uk/birney-srv/medaka-ref-panel/). The resulting 462 images were used for quantitative phenotyping. We applied image segmentation using modern machine learning techniques (Methods) allowing the extraction of relative eye diameter  as a measure of broad sense heritability (H2). The amount of variance explained by MIKK panel lines overall and thus broad sense heritability (H2) is 0.68. We performed similar analyses for the relative distance between eyes and female abdominal size parameters, observing 0.51 and 0.39 levels of heritability for these traits respectively , which produced a relative measure that corrects for the variance of body size. We performed one way analysis of variance (ANOVA) tests, calculating effect size  to this reference using a standard pipeline (Methods). We calculated the level of homozygosity across the medaka genome for each MIKK panel line using the number of heterozygous SNP calls across the genome with a total of 6.3% of all SNP genotypes across all panel lines  being called as heterozygous. The medaka sex chromosome, LG1, was excluded in this heterozygosity estimation since heterogametic XY males were used for whole-genome sequencing. However, as expected, this heterozygosity is not distributed evenly across the genome in each line. To explore this, we split the genome up into 10 kilobase (kb) non-overlapping windows and counted the number of heterozygous SNPs in each window. We then trained a two-state Hidden Markov Model (HMM) on all 10-kb windows from all MIKK panel lines jointly, using the expectation maximization (EM) algorithm to separate out true homozygous regions from regions of residual heterozygosity (Methods). We then used the Viterbi algorithm to find the most likely sequence of hidden states (Viterbi path) for each panel strain separately. We performed a similar procedure on the classic inbred strains (iCab and HO5) and wild-caught medaka from the same founding population as the MIKK panel [Medaka has a XX/XY sex determination system, similar to mammals , 42, 43.KK panel . Below aiCab and HO5) which have been inbred for more than 70 generations. Both classical inbred strains and the MIKK panel show a clear difference in the number of homozygous regions genome-wide relative to wild Kiyosu samples   were either synonymous or intergenic  across the MIKK panel compared to the n 70% 2,2,228 wereAs expected, these high-confidence LoF variants were more likely to be found in a heterozygous state , consistent with successful homozygosity being prevented in some cases due to a deleterious LoF allele. However, 63% had confident homozygous calls, with 34% of LoF SNPs showing no evidence of heterozygosity in any of the individual lines. Unsurprisingly, we see the expected increase in the number of LoF variants in heterozygous blocks for chromosome 1 due the sex determination region, and interestingly, both chromosomes 2 and 18 show consistently higher numbers of LoF variation for all LoF variant classes  which has been widely adopted in human studies . For mosiCab and HO5) and MIKK panel lines. As in previous studies, overall IBD is tracked via the identity-by-state (IBS) of SNPs. We first sought to assess the overall relationship between the classical inbred strains and the MIKK panel using IBS measures  analysis across the panel, including both classical inbred strains . Therefore, these sibling lines can be expected to share 25% of the genome from their parents. At the time of sequencing, there were 44 MIKK panel lines with a remaining productive sibling line (22 sibling-line pairs) . FST is a well-established parameter of population differentiation [FST indicates that allele distributions between populations are divergent, whereas low FST indicates they are similar.The MIKK panel was created from a single southern Japanese population of wild medaka from Kiyosu, Japan. We sought to determine whether there had been any large-scale selection or allele skew on panel genotypes from the original wild founding population during the inbreeding process. To search for allele skew, we used the parents from eight medaka trio datasets collected from the same founding population  to measuntiation . It provntiation ; high FSFST across all shared genotype positions between 16 wild medaka sequences [FST genome-wide, we defined 10-kb non-overlapping windows and took the mean FST for each window across the medaka genome , HNI (northern Japan), and HSOK (Korea), in order to orientate each SNP to its ancestral state . We used this combined dataset to calculate d [iCab, HNI, or HSOK. For this purpose, we used HdrR as the MIKK panel’s most closely related P1 population, the MIKK panel as the P2 focal population, either iCab, HNI, or HSOK as the P3 introgressing population, and the most recent common ancestor as the outgroup (O)  (1KG) in human [We analyzed the MIKK panel’s allele frequency distribution and linkage disequilibrium (LD) structure to assess their likely effects on genetic mapping. To remove allele frequency biases introduced by the presence of sibling lines in the MIKK panel, we first filtered the Illumina-based variant calls to include only one inbred line from each pair of sibling lines, leaving 63 non-sibling inbred lines. Figure in human . As exper2 between all pairs of biallelic SNPs with a minor allele frequency (MAF) greater than 0.10, within 10 kb of one another (Methods). We then grouped the paired SNPs by distance from one another into non-overlapping 100-bp windows and calculated the mean r2 for each window in order to represent how LD decays with distance between loci. Figure r2 of around 0.2–0.35 at a distance of 10 kb, whereas the MIKK panel reaches this level within 1 kb, with a mean r2 of 0.3–0.4 at a distance of ~ 100 bp. This implies that when a causal variant is present in at least two lines in the MIKK panel, one may be able to map causal variants at a higher resolution than in humans. We note that LD decays faster in chromosome 2 of the MIKK panel relative to the other chromosomes. This suggests that it has a much higher recombination rate, which is consistent with the linkage map described in [To review the MIKK panel’s LD structure, for each autosome in humans (chromosomes 1-22) and each chromosome in medaka (chromosomes 1-24), we calculated ribed in  showing 1% FDR) in 3795 transcripts corresponding to 3543 genes —a gene likely involved in progesterone metabolism  using liver tissue from 12-month-old females of 50 MIKK panel lines that were kept under 14/10 LD summer conditions  Fig. D. This iWe have established a panel of 80 near-isogenic inbred lines of medaka from a carefully selected wild population without prior genetic domestication of founder animals. In addition to the number of inbred lines, the MIKK panel is unique among vertebrate inbred panels as it has been derived from a wild population. It is well known that the domestication of wild animals often has severe consequences for the genetic diversity of a population , 63. In H2) measure of 0.68 for eye size as an extractable trait. Second, we performed RNA-Seq on female liver tissues. We detected 622 significant cis expression quantitative trait loci (cis-eQTLs) in 541 protein-coding genes, providing a proof of concept as the basis for future extended analyses. The location of cis-eQTLs with respect to gene structure shows the expected enrichment towards transcriptional start sites, although less pronounced than in previous studies involving larger sample sizes [We confirmed that the panel has captured phenotypic variability by exploring two phenotypes. First, for a morphological trait by image analysis, we observed a broad sense heritability  and will be interesting for studying aspects of the medaka sex determination system in the future.High-depth Illumina sequencing revealed a high degree of homozygosity across all panel genomes. We see a striking reduction in genome-wide heterozygosity with isogenic levels similar to that of classical inbred strains that have been inbred for more than 70 generations. The distribution of homozygosity is largely consistent across the genome, with the exception of the sex-determining locus on chromosome 1 and specific regions on several other chromosomes that are likely to have been caused by the presence of certain structural variations. As expected, we detected the expected drop in homozygosity around the sex-determining locus. Interestingly, 3 male fish showed no heterozygosity across this region in the X chromosome. This is consistent with the known XX males observed in medaka fish  and is aThe MIKK panel originates from a single population of wild medaka caught in Kiyosu, Japan. When comparing the genome-wide allele frequency distribution between the inbred lines and wild Kiyosu medaka, we observed similar allele frequencies and no large regions with distortions. This suggests that the genetic variation in the MIKK panel is a good representation of wild genetic variation, making the panel an excellent resource for genome-wide association studies (GWAS) using natural standing variation. To assess the resolution for genetic association mapping, we calculated the linkage disequilibrium (LD) across MIKK panel lines. We compared the LD decay to that observed in human cohorts (1000 Genomes Project Phase 3) and observed a marked increase in LD decay, indicating that when a causal variant is present in more than one MIKK panel line, the mapping resolution may be higher than in humans. We note the faster decay of LD in chromosome 2, which is consistent with the known high recombination rate seen across this chromosome relative to its physical distance (3.36 cM/Mb in chromosome 2 vs 1.02-2.76 cM/Mb for the other chromosomes) , possiblOne interesting aspect of the panel design is the fact that some lines have a “sibling” line (originating from the same founder F1 cross). This special genetic structure across the panel can be exploited for specific research applications, for example to explore the aggregate impact of epistatic (GxG) interactions without locus-level resolution. This genetic structure also acted as a definitive control for us during this analysis. When we look at measures of relatedness across the panel, the IBD analysis showed that “sibling” lines cluster closely together, indicating that they are indeed more related to each than other panel lines. This finding not only shows that the panel displays the favorable genetic structure that was designed from the outset, but also that all the crossing procedures during the inbreeding process were robust.iCab), northern Japanese (HNI), and Korean (HSOK) medaka strains. As expected, the MIKK panel shows the most admixture with the southern strain iCab, as Kiyosu is in southern Japan. However, there is a slight increase in admixture proportion for HSOK compared to HNI. Although the proportional difference is small, this supports the general finding that northern and southern Japanese medaka strains show low levels of interbreeding that may be a result of geographical isolation or genome divergence [To place the MIKK panel in context of medaka population genetics around Japan, we performed ABBA-BABA analysis to compare the level of admixture between the MIKK panel and southern Japanese  variants . Small homozygous LoF variants (SNPs and INDELs) show a slight enrichment in regions of residual heterozygosity in the MIKK panel lines and on chromosomes that show higher levels of larger structural variation overall. This may indicate a purging of LoF variation in essential dosage-sensitive genes, although the observed enrichment is slight, which is likely due to the low chance of LoF variation occurring in essential genes across the 9 generations of inbreeding, given the very low rate of spontaneous mutations. There are, however, many rare genetic variants that occur in genes that are predicted to have a functional impact or an intolerance to dosage. This is, of course, not unexpected but it does highlight one of the powerful applications of genetic screening using the MIKK panel, where specific F2-cross designs can be used to balance the distribution of rare impactful variants across an F2 population, allowing a functional analysis of rare variants during trait association analyses. Exploration of the phenotypes of these cases, or mapping of potential modifier mutations that might compensate for such loss of function variation, will be a valuable line of research for both the medaka and human disease communities.Within the vertebrate clade, teleosts including medaka diverged from the tetrapod lineage about 400 million years ago , 69. DueIt is important to note that the value of medaka and the MIKK panel is not only the biomedical relevance to model human disease and complex genetic traits. This economical teleost combines several advantages that render it an important model for basic research and thus an important and valuable addition to existing vertebrate and invertebrate population genetic models for basic research. The MIKK panel of inbred lines was established directly from a wild population without prior domestication and thus represents the genetic variation of the original population in a reproducible form. Furthermore, the possibility to sample from the wild and validate experimental data obtained with the MIKK inbred panel provides options to study population genetics that are unique for a vertebrate laboratory model. The MIKK inbred panel will prove to be an important additional model to establish tools for association studies and validation including functional testing of genetic variantsHere we provide a detailed analysis of some of the sources of variation present across the MIKK panel lines, and put in place important resources to allow the MIKK panel to be fully exploited during further research studies. Further sequencing of the MIKK panel lines, using both long- and short-read technologies, is planned in the future as well as extensive molecular, organ function and organismal phenotyping across the panel. In a separate analysis , we lookAn unstructured polymorphic wild population of medaka was sampled in the irrigation canals of Kiyosu near Toyohashi as described . In totaFull-sibling-pair inbreeding crosses  were setflavobacterium columnare infection that occurred while inbreeding generations 4-6 was treated with Baytril . Thereafter, the inbreeding lines were kept at 23 °C to reduce the microbial load in the system water. The notebook for generating Fig. https://birneylab.github.io/MIKK_genome_main_paper/01_Fecundity.html.MIKK panel fish at the medaka facility of KIT were kept as described in , in Müllhttps://github.com/divamgupta/image-segmentation-keras) machine learning framework with Tensorflow [The 6- to 9-month-old adults were sacrificed by hypothermic shock. Lateral and dorsal images were taken with a Canon EOS 250D camera and a Sigma 18-250mm lens. Using a subset of the MIKK panel images, a number of machine learning models were trained to detect and measure phenotypes from the images of the entire panel of fish. The code for this was developed in Python using an existing implementation of semantic image segmentation in the Keras ,Rotate images by − 45 to + 45°,Shear images by − 16 to +16°.A subset of the panel images were segmented using the Labelbox platform  to generNose to tail length in pixels,Maximum width of fish in pixels,Fish area in pixels,Eye area in pixels,Length of abdominal region in pixels,Length of caudal region in pixels,Ratio between lengths of the abdominal and caudal region,Eye diameter in pixels,Distance from the top of the eye to the top of the head,Distance from the bottom of the eye to the bottom of the head.After training the models, they were used to predict the chosen features in the remaining unseen images and the following parameters were extracted from these segmentations.https://birneylab.github.io/MIKK_genome_main_paper/01_Fecundity.html .The notebook for generating Fig. For whole-genome sequencing, brains were dissected from 6-month-old male medaka adults. Fish were sacrificed by hypothermic shock. The brain was dissected and shock frozen in liquid nitrogen. For RNA-Seq analysis, 12-month-old adults that were kept at either 14 light:10 dark (summer condition) or 10 light:14 dark (winter condition) light cycles respectively were sacrificed by hypothermic shock and the organs after dissection were shock frozen in liquid nitrogen. Fish were acclimatized for at least 1 month to the 10 light:14 dark (winter condition) prior to tissue dissection.DNA was extracted from medaka brains in 2-ml Eppendorf tubes using the Qiasymphony DSP DNA Mini kit (Cat. No. 937236). Tissue_HC_200_v7 Protocol, 100 μl elution volume. Pre-treatment samples were homogenized with 5-mm stainless steel beads in 220 μl buffer ATL at 30 Hz for 20 s on the TissueLyser. Then, 20 μl proteinase K was added, and samples were incubated for 1 h at 56 °C, 900 rpm on the Qiacube . RNase treatment for 2 min was done at RT. Following DNA extraction from whole brain samples, each line of the MIKK panel was whole genome sequenced at greater than 30× coverage using Illumina X10 instrumentation at the Wellcome Trust Sanger Institute. Library preparation was performed following the standard PCR-free Illumina protocol . In totaRNA extraction from 52 liver samples was performed on a Qiagen automated extraction platform using QIAsymphony RNA Kits, where polyA RNA was extracted from liver samples for paired end RNA-Seq analysis. Samples were prepared for Illumina RNA sequencing using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina and sequenced on a Hiseq 4000 sequencing platform following the manufacturer’s instructions. We obtained data passing the facility quality control threshold for 50 samples out of the 52 initially processed.https://github.com/birneylab/MIKK_genome_main_paper [Raw sequencing data can be retrieved from ENA linked to the following project ID set out in the “Availability of data and materials” section. All the scripts and metadata used for this study are extensively described in the associated github repository available at in_paper .HdrR reference genome from ENSEMBL (release 94) using the Burrow-Wheeler Aligner for short-read alignment (BWA) [After sequencing, the MIKK panel genomic data was transferred securely from Sanger compute storage to EBI where alignment and variant calling was performed. All whole-genome sequence (WGS) datasets were aligned against the latest medaka nt (BWA) . For varnt (BWA) . Brieflynt (BWA)  to make https://github.com/a-slide/pycoSnake/tree/0.0.3.1) [HdrR ASM223467v1, https://www.ensembl.org/Oryzias_latipes/Info/Index). For each of the 50 datasets  . All theanosnake .We estimated transcript abundances with Salmon  and ran https://github.com/birneylab/MIKK_genome_main_paper/tree/master/eQTL [https://birneylab.github.io/MIKK_genome_main_paper/eQTL/code/LIMIX_eQTL_analysis.html.For the eQTL analysis, we used the transcript abundance estimates obtained from Salmon and applied a pre-filtering step to retain transcripts with a minimal count of 5 reads and a minimal TPM (transcript per million) of 1 in at least half of the samples (30/60). For valid transcripts , we then normalized the TPM values using a quantile Gaussian normalization. SNP genotypes were cleaned up and preprocessed with PLINK . We perfter/eQTL , with thF) from the MIKK panel genotype calls. Additionally, we used PLINK to call runs of homozygosity across each of the MIKK panel lines across the medaka genome, as well as calculating identity by descent (IBD) of all panel lines. To estimate an accurate level of homozygosity and create a homozygous block call set across the entire medaka genome, we developed a Hidden Markov Model (HMM) to call the allelic status across all MIKK panel lines using 10 kilobase (kb) resolution across the medaka genome. Briefly, the number of heterozygous SNP genotype calls was counted across 10-kb non-overlapping windows in the medaka genome for each MIKK panel line separately. These genotype counts were used to train a 2-state HMM with a Poisson distribution using the expectation maximization (EM) algorithm, followed by the Viterbi (forward/backward) algorithm to define the most likely state space path. Following this process, for each MIKK panel line, we have 10-kb regions of the medaka genome that were called as either heterozygous or homozygous.To assess the level of homozygosity in the MIK panel, we applied a number of different approaches. Firstly, we used the PLINK software  to calcuhttps://birneylab.github.io/MIKK_genome_main_paper/03_Nucleotide_diversity.html [https://birneylab.github.io/MIKK_genome_main_paper/06_Structural_variation.html [We calculated nucleotide diversity in 500-kb windows across the MIKK panel based on 63 “non-sibling” MIKK panel lines  derived from the reference proteome project at Uniprot [To map medaka genes to human orthologs, we used the PANTHER resource  which in Uniprot . Briefly Uniprot .FST) using genotype information from 7 wild Kiyosu lines, comparing the allele frequencies against those in the MIKK panel.First, we wanted to test the genotypes contained in the MIKK panel lines against the distribution of alleles from the original wild population of Kiyosu medaka. To do this, we calculated the fixation index (iCab), northern Japan (HNI), and Korea (HSOK). We used the 50-fish multiple alignment from Ensembl release 102 [HdrR, HNI, and HSOK, as well as the most recent common ancestor of all three strains. Using the phylogenetic tree provided with the dataset, and the ape R package version 5.4.1 [HdrR, we assigned the allele in that ancestral sequence as the “ancestral” allele, and the alternative allele as the “derived” allele, and then combined that dataset with the Illumina VCF containing SNPs called in the MIKK panel lines and iCab strain. We then calculated d in sliding windows as described in [https://github.com/simonhmartin/genomics_general). Data processing and analyses were carried out with R version 4.0.2 [ape [circlize [https://birneylab.github.io/MIKK_genome_main_paper/04_Introgression.html [In light of previous findings , we sougease 102  to obtaion 5.4.1 , we deteribed in , using ton 4.0.2 , and theon 4.0.2 . Figure 0.2 [ape  and circcirclize . All codion.html .ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/), then merged them into a single VCF using GATK [The MIKK Illumina callset contained SNP and INDEL calls for 79 of the 80 extant MIKK panel lines , and then used R [r2 values for each pair of SNPs within 1 Mb of each other . We then used R and the tidyverse suite of R packages to group the r2 observations for each pair of SNPs based on their distance from one another into non-overlapping bins of 100-bp in length, then calculated the mean r2 in each of those bins and generated the plots in Fig. r2 and left boundary of each bin. All code used for this section can be found here: https://birneylab.github.io/MIKK_genome_main_paper/02_LD_decay.html [We used PLINK , 101 to n used R  and the n used R  to produn used R . We usedRepeatModeler pipeline (v2.0.0) [HdrR genome assembly [LTRharvest [LTR_retriever [The (v2.0.0)  for the assembly . RepeatMRharvest  and LTR_etriever  tools.et al., 1990) to align  the Oryzias latipes proteome (Ensembl v99) and pfam peptide database (v32) against the RepeatMasker peptide library. Finally, a nucleotide BLAST was used to remove any RepeatModeler repeats that aligned  against the corresponding transcripts.The RepeatModeler library of repeats was filtered to remove non-TE protein-coding sequences by using a protein BLAST (Altschul HdrR assembly against the filtered RepeatModeler library of consensus repeats and the existing RepeatMasker repeat families.RepeatMasker (v4.1.0)  was usedExonerate  was used to align the two subtypes of the Teratorn mobile element found in the Oryzias latipes genome against the HdrR reference .https://birneylab.github.io/MIKK_genome_main_paper/05_Repeats.html [Additional code used to generate Additional file ats.html .Additional file 1: Table S1.MIKK panel lines. Table setting out all MIKK panel lines and the specific analyses each was included in.Additional file 2: Table S2.Fecundity data. Table with MIKK panel fecundity data.Additional file 3: Table S3.Fish imaging parameters. Table with MIKK panel morphometric data.Additional file 4: Figures S1-S8.Supplementary figures. Various supplementary figures.Additional file 5: Table S4.Variant types. Counts of short variants in each class based on predicted variant effect.Additional file 6: Table S5.High confidence LoF variants. Table of homozygous high-confidence loss-of-function variants in the MIKK panel orthologous to a human dosage-sensitive gene.Additional file 7: Table S6.eQTL SNPs. Significant eQTL SNPs with annotations.Additional file 8: Figure S9.Copenhagen plot. Plot showing genomic positions of significant eQTL SNPs.Additional file 9: Figure S10.Haploview plots. Linkage disequilibrium (LD) plots generated with Haploview for each of the 24 medaka chromosomes, showing r2 values in the MIKK panel for each pair of SNPs within 1 Mb of one another.Additional file 10. Review history.


Robinia pseudoacacia L. germplasm resources for production, 13 phenotypes and three physiological indicators of 214 seedlings from 20 provenances were systematically evaluated and analyzed. The leaf phenotypic and physiological coefficients of variation among the genotypes ranged from 3.741% to 19.599% and from 8.260% to 42.363%, respectively. The Kentucky provenance had the largest coefficient of variation (18.541%). The average differentiation coefficients between and within provenances were 34.161% and 38.756%, respectively. These close percentages showed that R. pseudoacacia presented high genetic variation among and within provenances, which can be useful for assisted migration and breeding programs. Furthermore, based on the results of correlations, principal component analysis and cluster analysis, breeding improvements targeting R. pseudoacacia’s ornamental value, food value, and stress resistance of were performed. Forty and 30 excellent individuals, accounting for 18.692% and 14.019%, respectively, of the total resources. They were ultimately screened, after comprehensively taking into considering leaf phenotypic traits including compound leaf length, leaflet number and leaflet area and physiological characteristics including proline and soluble protein contents. These selected individuals could provide a base material for improved variety conservation and selection.To select elite  In situ preservation and ex situ preservation are the two standard methods for the protection of germplasm resources. Although in situ conservation maintains the original ecosystem and natural habitat of plants better than ex situ preservation, it requires a large cultivation area and large investments of labor, materials, finances, and time for administration and management. Ex situ preservation acts as a backup for certain aspects of diversity that might otherwise be lost in human-dominant ecosystems and in nature . In. In43]. Two hundred and fourteen well-grown black locusts of different provenances were selected as experimental material from the Mengjin Forest Farm in August 2017. Each specimen was chosen by selecting those whose annual branches were free of pests and disease at the same height in four directions .http://www.njjcbio.com/).In this study, 16 quantitative traits of black locust trees were evaluated. They included 13 leaf traits: compound leaf length (CLL), compound leaf width (CLW), compound leaf length/width (CLL/CLW), compound petiole length (CPL), leaflet length (LL), leaflet width (LW), leaflet length/width (LL/LW), leaflet area (LA), leaflet perimeter (LPM), leaflet circularity (LC), leaflet pairs (LP), leaflet number (LN), and petiole angle (PA). The 16 traits also included three physiological traits: chlorophyll content (Chl), total protein content (Spro), and proline (PRO). The 13 leaf traits and Chl were evaluated at maturity in August 2017. The two remaining physiological traits were measured in healthy leaves from April to May 2017 . The leaCV). SPSS version 24 was used to perform analyses of variance (ANOVAs) in conjunction with Duncan’s multiple range tests for multiple comparisons. Principal component analysis was applied to the sixteen traits of the black locust provenances. A p-value for the ANOVA F tests ≤0.05 was considered significant. The formula, Vst (%) = δ2t/s/(δ2t/s+δ2s), was used to calculate the percentage of variance among and within the provenances, where Vst is the differentiation coefficient of the trait, δ2t/s is the variance component between provenances, and δ2s is the variance component within provenances [Microsoft Excel 2016 was used to examine the variation in leaf phenotypic and physiological traits, including the mean value, standard error, amplitude, and coefficient of variation  and 44.649±11.401 μg·g-1 (PRO), respectively. However, the provenances in KS, AR, and OK had the lowest values—31.98±0.54 , 1445.861±899.893 μg·ml-1 (Spro), and 19.257±3.532 μg·g-1 (PRO), respectively.The analysis results  (42.363%) had the highest level, followed by the proline (PRO) (42.356%) and chlorophyll content (Chl) (8.260%); Spro had the highest stability of these parameters, as revealed by the maximum-to-minimum ratios of each indicator—6.364%, 8.735% and 19.178%, respectively. Of the 20 provenances, KY provenance had the highest total average coefficient of variation (52.786%), which was approximately 3.8 times that for the MS provenance, which presented the smallest variation coefficient.In conclusion, the variation richness of the 16 test indexes of the 214 black locust accessions was as follows: Spro (42.363%)>PRO (42.356%)>LA (19.599%)>PA (17.317%)>CLW (12.359%)>CPL (11.179%)>LL (10.942%)>LPM (10.741%)>CLL (10.708%)>CLL/CLW (10.697%)>LP (10.558%)>LW (10.522%)>LN (9.832%)>LL/LW (8.462%)>Chl (8.260%)>LC (3.741%). Similarly, the variation in richness at each provenance level was as follows: KY (18.514%)>WV (18.411%)>VA (17.323%)>OH (17.222%)>TN (17.105%)>CN (16.231%)>PA (16.174%)>IN (15.317%) = IL (15.317%)>GA (15.275%)>KS (14.943%)>MS (14.632%)>IA (14.606%)>MO (14.098%)>MD (13.852%)>OK (13.304%)>AL (13.298%)>AR (13.127%)>MS/AL (10.571%)>NC (10.224%).The differentiation coefficient and variance components among/within provenances ranged from 25.843% (CLW) to 71.655% (PA) at the leaf phenotypic trait level, with an average of 48.829%. The mean leaf phenotypic differentiation coefficient of the nine leaflet traits was 52.259%, lower than that of four compound leaf indicators (41.112%). Thus the differentiation level for the leaflets was slightly higher than for the compound leaves. In terms of single traits, the differentiation coefficients of CPL, LW, LP, LN and PA were larger than those within provenances, indicating that the variation among provenances was higher than within them . FurtherHomoplastically, the variation of the three physiological parameters showed that Spro had the lowest and PRO, the highest differentiation coefficient at—23.956% and 49.137%, respectively. The total mean differentiation coefficient of the three indicators was 40.383%, but it was 59.617% within provenances. These results are consistent with the findings of leaf phenotypic traits, further demonstrating the importance of individual variation in black locust trees.R. pseudoacacia provenances (P<0.05 or P<0.01) (S2 Table in Sixty-one (50.833%) significantly correlated traits were identified by Pearson correlation analyses of the 16 parameters  in 20 Five principal components explained the investigated characteristics with eigenvalues greater than 1.0 , and theR. pseudoacacia provenances using the method of hierarchical cluster analysis between groups, taking sixteen trait parameters as variables , which had the largest CPL (3.875 cm), LW (3.028 cm), LA (15.112 cm2) and LPM (15.312 cm). Group 2 included the NC and MS provenances, which had the highest CLL/CLW (2.736 cm) and LC (77.194%). Group 3 contained WV, OH, KY and VA provenances, with the highest CLW (11.293 cm), LL (6.945 cm), Chl (36.096 SPAD) and PRO (40.042 μg·g-1). Group 4 comprised only one provenance, KS, which presented the highest CLL (29.709 cm), LL/LW (2.783), LP (9.917), LN (20.834), PA (64.192°) and Spro (1445.861 μg·ml-1).Cluster analysis was performed for twenty different ariables . When thIn addition, based on the results of the principal component analysis, systematic clustering analysis was performed on the eigenvectors of all 16 parameters in the four principal components using the same method described above . The resR. pseudoacacia provenances were analyzed. The results are shown in R. pseudoacacia at either leaf phenotypic or physiological levels.The correlations between the euclidean distance and geographical distance of the 13 phenotypic parameters and 3 physiological parameters of the 20 R. pseudoacacia plants for ornamental use and food production. Because of the significant positive correlations between CLL, LA, and LN, these three characteristics could also reflect the quantity and quality of R. pseudoacacia leaves. Therefore, when the above three traits were combined for respect to food-based breeding objectives, 40 elite trees were selected after the 214 accessions were analyzed  were selected. Secondly, based on the evaluation of Spro, eighty-four excellent trees (39.252%) were selected, whose Spro content was approximately 1.5 times that in the original provenances. Lastly, based on the evaluation of PRO and Spro, thirty excellent trees were obtained (14.019%), whose PRO and Spro contents were 1.8 times those of the original provenances, and the other two indicators also increased compared with those of the original provenances, indicating that the stress resistance of these elite trees increased at the original population level  and 5.128% (PRO vs Chl). The large difference between these different physiological indicators may be due to the data obtained by the portable SPAD-502 Plus chlorophyll meter. Factors such as plant variety (genotype), environmental conditions, planting density, and nutrient conditions can affect SPAD values . Using aR. pseudoacacia fine variety bases in Ji, Shanxi Ji, which showed a rich diversity in leaf phenotypic traits among 96 genotypes [It is commonly held that a coefficient of variation of traits greater than 10% represents a large difference between individuals and equates to a rich variation in traits . In our enotypes . In addienotypes , 54.The differentiation coefficient of the tested indexes in our results showed that the main variation of black locust were intra-provenance variation. This is consistent with the results of previous studies on black locust  and otheSalix psammophila [Phoebe bournei [Correlation coefficients can be used to reveal the relationships between measured traits and thus greatly influence selections as part of breeding strategies , 58. Formmophila  and Phoe bournei . MoreoveR. pseudoacacia germplasm in Shanxi and Phoebe bournei. Possible explanations for these results could be the weak correlations between these traits and/or the differences in the number and types of measured parameters [Principal component analysis is a multivariate technique widely used for dimension reduction, that is, analyzing multiple related variables of one or a few comprehensive indicators . In our rameters , 38, 60.R. pseudoacacia leaves. Indicators I, II and III were the preferred test indicators to achieve the breeding objectives for practical production applications, including stress resistance, ornamental value and food production. Our results are consistent with those of many previous studies on morphological variation in Paeonia rockii. Four categories were divided based on 12 fruit traits, and group II was screened to maximize the economic yield per plant [Cluster analysis is a mathematical method used to find similarities between measured indexes/materials used in a group by revealing the real categories of the population and reducing the number of data points . Multipler plant .Prosopis alba [The results of Mantel’s test were similar to the clustering results based on euclidean distance, revealing a nonsignificant geographical variation pattern of phenotypic and physiological parameters. This is consistent with the results of previous studies of not only black locust via molecular markers such SSRs  and ISSRpis alba . PossiblTaxodium distichum by Wang et al. [Phenotypic changes following a change in natural selection are particularly important for undergoing continuous adaptation ; this reg et al. . DiffereR. pseudoacacia.The present study showed the following: (1) LC and LA exhibited opposite variations at the phenotypic level, of which LC had the highest stability; (2) when the differentiation coefficients of four compound leaves, nine leaflets and three physiological traits were compared the differentiation level of leaflet traits was higher than that of the other two types of indexes; (3) the variation of test traits is mainly attributed to differences within provenances, although the variation between provenances could not be ignored; and (4) there was a nonsignificant geographical variation pattern of phenotypic and physiological parameters. Suggestions and elite tree resources for germplasm preservation strategies and efficient breeding are provided to preserve the genetic resources of S1 File(DOCX)Click here for additional data file.


Childhood maltreatment is associated with poor mental and physical health. However, the mechanisms of gene–environment correlations and the potential causal effects of childhood maltreatment on health are unknown. Using genetics, we aimed to delineate the sources of gene–environment correlation for childhood maltreatment and the causal relationship between childhood maltreatment and health.We did a genome-wide association study meta-analysis of childhood maltreatment using data from the UK Biobank (n=143 473), Psychiatric Genomics Consortium (n=26 290), Avon Longitudinal Study of Parents and Children (n=8346), Adolescent Brain Cognitive Development Study (n=5400), and Generation R (n=1905). We included individuals who had phenotypic and genetic data available. We investigated single nucleotide polymorphism heritability and genetic correlations among different subtypes, operationalisations, and reports of childhood maltreatment. Family-based and population-based polygenic score analyses were done to elucidate gene–environment correlation mechanisms. We used genetic correlation and Mendelian randomisation analyses to identify shared genetics and test causal relationships between childhood maltreatment and mental and physical health conditions.Our meta-analysis of genome-wide association studies (N=185 414) identified 14 independent loci associated with childhood maltreatment (13 novel). We identified high genetic overlap (genetic correlations 0·24–1·00) among different maltreatment operationalisations, subtypes, and reporting methods. Within-family analyses provided some support for active and reactive gene–environment correlation but did not show the absence of passive gene–environment correlation. Robust Mendelian randomisation suggested a potential causal role of childhood maltreatment in depression , as well as both schizophrenia and ADHD , but not in physical health conditions  or inflammation (C-reactive protein concentration).Childhood maltreatment has a heritable component, with substantial genetic correlations among different operationalisations, subtypes, and retrospective and prospective reports of childhood maltreatment. Family-based analyses point to a role of active and reactive gene–environment correlation, with equivocal support for passive correlation. Mendelian randomisation supports a  causal role of childhood maltreatment on mental health, but not on physical health conditions. Our study identifies research avenues to inform the prevention of childhood maltreatment and its long-term effects.Wellcome Trust, UK Medical Research Council, Horizon 2020, National Institute of Mental Health, and National Institute for Health Research Biomedical Research Centre. Childhood maltreatment, including abuse and neglect, is a complex global problem, affecting up to 36% of the population worldwide.3Parental experience of childhood maltreatment is an important risk factor for maltreating their own children.The heritable component of childhood maltreatment is not immutable and is thought to manifest through gene–environment correlations: passive , active (children's genes shape their behaviours including risk-taking behaviour), and reactive or evocative .Evidence before this studyChildhood maltreatment is known to be partly heritable but how different mechanisms of gene–environment correlation contribute to childhood maltreatment, and whether childhood maltreatment is causally related to mental and physical health outcomes is unknown. We searched PubMed and Google Scholar for genome-wide association studies and Mendelian randomisation analyses of childhood maltreatment from Jan 1, 1990, to Oct 1, 2020. Search terms were “childhood maltreatment” OR “childhood trauma” AND “GWAS” OR “genome-wide association study” OR “Mendelian Randomization”, restricting the search to articles published in English. We found one genome-wide association study of childhood maltreatment with two identified genetic loci. We did not identify any studies investigating mechanisms of gene–environment correlations or causal effects of childhood maltreatment using Mendelian randomisation.Added value of this studyIn this genome-wide meta-analysis (N=185 414) of childhood maltreatment—the largest to date to our knowledge—we identified 14 independent genetic variants (13 novel) associated with childhood maltreatment. Using within-family polygenic score analyses, we found that this genetic signal is due to multiple gene–environment correlation mechanisms. We found some direct evidence for active and reactive gene–environment correlation, but could not show an absence of passive gene–environment correlation. Finally, in this first Mendelian randomisation analyses of childhood maltreatment, to our knowledge, we identified potentially causal effects of childhood maltreatment on depression. We also identified bidirectional potentially causal effects between childhood maltreatment and both ADHD and schizophrenia. However, we found no evidence for causal effects of childhood maltreatment on physical health or inflammatory markers, or vice versa.Implications of all the available evidenceOur study supports findings from observational epidemiological studies, providing the first evidence using Mendelian randomisation of potential causal relationships between childhood maltreatment and mental health conditions. These findings underscore the need to prevent childhood maltreatment and to identify mediating and moderating factors that can help minimise the effect of childhood maltreatment on mental health. However, in contrast to observational studies, we found no association between childhood maltreatment and physical health. Although childhood maltreatment is only partly heritable, delineating the source of the heritability is important as different gene–environment correlation mechanisms might indicate the need for different intervention strategies. Further research is needed to establish whether these gene–environment correlation mechanisms differ between populations and subtypes of childhood maltreatment.Observational studies link childhood maltreatment with severe and long-lasting mental  meta-analysis of childhood maltreatment to date, to our knowledge, to investigate the underlying mechanisms of childhood maltreatment, focusing on offspring DNA. We aimed to quantify the SNP heritability and genetic correlations across different operationalisations, subtypes, and reporting methods of childhood maltreatment. Finally, we investigated the genetic correlations and causal effects between childhood maltreatment and outcomes related to mental and physical health.In this GWAS meta-analysis, we had access to individual-level data from 159 124 participants from four datasets: 143 473 participants from the UK Biobank (birth year 1936–70)Ethical approval was obtained from the Human Biology Research Ethics Committee, University of Cambridge , and from the local cohort ethics committees.We define childhood maltreatment here as consisting of emotional, sexual, and physical abuse, and emotional and physical neglect. All retrospective reports of childhood maltreatment were self-reported, whereas most prospective reports of childhood maltreatment were reported by a parent or caregiver.Participants in the UK Biobank completed the retrospectively reported five-item Childhood Trauma Screener.vs any); a binary severe maltreatment score, in which scores 2–4 for each individual item were recorded as 1; and a binary severe childhood abuse score, which is the same as above but restricted to the three abuse items only.With the UK Biobank data, we investigated heritability of and genetic correlations among different subtypes and operationalisations of childhood maltreatment . For subFor each GWAS, age and sex were included as covariates. The primary GWAS in the UK Biobank was done using the BOLT-LMM, version 2.3.4, algorithm,retrospective). Next, we meta-analysed Adolescent Brain Cognitive Development Study, ALSPAC, and Generation R datasets to obtain a GWAS of prospectively reported childhood maltreatment (GWASprospective). Finally, we meta-analysed all five datasets to obtain a GWAS of childhood maltreatment .Sample-size weighted meta-analysis was done in METAL, version 2011-03-25.–8, after clumping , using the linkage disequilibrium weights generated from the European subset of the 1000 Genomes phase 3 datasetIndependent significant loci were identified at a GWAS threshold of p<5 × 10Gene identification was done using MAGMA,SNP heritability of and genetic correlations between operationalisations and subtypes of childhood maltreatment were done using GCTA-GREML, version 1.93.2Heritability analyses of the meta-analysed GWAS and other genetic correlations were done using LDSCr2=0·1, 250kb, retrospective (base sample) in a hold-out sample of 12 855 individuals from the UK Biobank (target sample). This process was done by doing a second GWAS of childhood maltreatment in the UK Biobank excluding the hold-out sample  and meta-analysing the results with the PGC_26K. PGSs were generated in 9924 unrelated individuals from this subset of 12 855 individuals at 11 p value thresholds. We included birth year, sex, genotyping batch, and the first 20 genetic principal components as covariates, and additionally Townsend Deprivation Index in a second model. We corrected for multiple testing using Benjamini-Hochberg false discovery rate correction.We investigated the variance explained by polygenic score (PGS) in two cohorts—a hold-out sample from the UK Biobank and ALSPAC. PGSs were calculated using PRSice-2retrospective (base sample) against binarised childhood maltreatment (prospectively reported) at four age groups , with age (in months), sex, and the first ten genetic principal components as ALSPAC (maximum N=7453). We used only ten principal components as ALSPAC is a geographically—and subsequently, genetically—more homogenous cohort than the UK Biobank, and previous research has found poor evidence for population stratification in ALSPAC.In ALSPAC (target sample), we regressed the PGS for GWASWe used three methods to delineate the contribution of different gene–environment correlation mechanisms to childhood maltreatment: (1) comparing between-sibling and between-family effects; (2) polygenic transmission disequilibrium tests in two autism cohorts; and (3) investigating the variance explained by PGS for retrospective childhood maltreatment in ALSPAC after accounting for well known familial risk factors for childhood maltreatment.childhoodmaltreatment)To quantify the variance explained by passive gene–environment correlation and by active and reactive gene–environment correlation combined, we simultaneously investigated between-sibling and between-family effects of PGS (base sample GWASbsib is the between-sibling effect of PGS (representing reactive and active gene–environment correlation combined); βbfam is the between-family effects; and (PGSj) is the family-mean PGS. Covariates included were age, sex, genotyping batch, and 20 genetic principal components. Passive gene–environment correlation is estimated from the difference between βbsib and βbfam. SE was calculated by 10 000 bootstraps. In the UK Biobank, the family mean is the sibling mean.Although between-sibling PGS analysis assumes that a proportion of the familial environment is shared between siblings, this assumption might not always be true. An example is siblings who are discordant for neurodevelopmental conditions such as autism or ADHD. Because of the different support needs of the siblings, parental response to the two siblings will be different. This difference in familial environment between siblings indexes reactive gene–environment correlation, and to an extent active gene–environment correlation. To quantify this difference in gene–environment correlation, we did polygenic transmission disequilibrium testsFinally, we repeated the PGS analyses in ALSPAC, as outlined earlier, after including in separate models four parental risk factorsWe did two-sample, bidirectional Mendelian randomisation analysesScripts and Summary GWAS statistics are available online.The funders of the study had no role in study design, data analysis, data interpretation, or writing of the report.rg) 0·47–1·00, An overview of the study design is provided in rg 0·62–1·00; rg 0·24–1·00; We next investigated the heritability and genetic correlations between subtypes of childhood maltreatment. We identified modest but significant SNP heritability for all five subtypes of childhood maltreatment . Geneticrg=0·64 [SE=0·12]; p=1·15 × 10–7). Meta-analysis of the UK Biobank and PGC_26K (GWASretrospective) identified 257 significant SNPs representing 14 independent loci . Similarly, we found no change in SNP heritability of childhood maltreatment and subtypes after accounting for Townsend deprivation index  by meta-analysing data from three datasets . Genetic correlation between GWASretrospective and GWASprospective was high , similar to that between the two GWAS of retrospectively reported childhood maltreatment (rg=0·64 [SE 0·12]). Confirming this result, PGS for GWASretrospective explained a significant proportion of the variance in prospectively reported childhood maltreatment in ALSPAC across multiple age groups . We identified 277 genome-wide significant SNPs, representing 14 independent loci , with six loci nominally significant (GWASprospective p<0·050) and one significant after Bonferroni correction for the 12 loci . The meta-analysed GWAS of childhood maltreatment had a significant SNP heritability , and no inflation in statistics due to unresolved population stratification was detected (LDSC-based intercept 1·0076 [SE 0·0074]). Of the 14 loci, two were on the X-chromosome. Although our study was underpowered for sex-stratified GWAS, we note significant sex differences in overall childhood maltreatment and subtypes in the datasets in which we had access to individual level data. Notably, in most datasets, female individuals were more likely to report childhood maltreatment. In the UK Biobank, female individuals were more likely to report sexual abuse, while male individuals were more likely to report physical abuse in line with previous research  with mental health problems (six loci), risky behaviour (four loci), smoking and cannabis use (three loci), cardiovascular health, sleep difficulties, and reduced intelligence and educational attainment (two loci each) identified in previous GWASs  in the adult post-mortem brain . We identified a small between-sibling effect , accounting for 58% of the total effect. By contrast, the difference in between-family and between-sibling PGS effects (passive gene–environment correlation) did not reach statistical significance  accounting for approximately 42% of the total effect. The difference between the combined active and reactive gene–environment correlation versus passive gene–environment correlation did not reach statistical significance  with between-family  PGS of GWASchildhoodmaltreatment to autistic children from their parents  but not to their non-autistic siblings  in the Simons Simplex Collection, suggesting that an increased risk for childhood maltreatment in autistic individuals was partly explained by increased active and reactive gene–environment correlation. This finding was replicated in the SPARK dataset  was similar, or shared, between siblings. However, parenting could systematically differ between, for example, autistic individuals and their non-autistic siblings, in which case the proportion of shared familial environment might be smaller than for families without autistic children. In these families, a larger proportion of sibling difference in childhood maltreatment could be attributable to reactive  and active gene–environment correlation  than for siblings with no known neurodevelopmental disorders. Supporting this hypothesis, we found an over-transmission of PGS of GWASretrospective. None of the four risk factors substantially reduced the variance explained by PGS  for childhood maltreatment substantially attenuated the variance explained by PGS for GWASd by PGS .childhoodmaltreatment signal. After Bonferroni correction, we identified significant positive genetic correlations between childhood maltreatment and mental health conditions , anthropometric traits , insomnia, and coronary artery disease. Childhood maltreatment also had modest negative genetic correlations with intelligence, educational attainment, and age of onset traits . Our findings provide some evidence for the role of active and reactive gene–environment correlation in childhood maltreatment, although passive gene–environment correlation could not be excluded. Mendelian randomisation analyses found a unidirectional causal effect of childhood maltreatment on depression, bidirectional effects with ADHD and schizophrenia, but no evidence for unidirectional or bidirectional causal effects on physical health problems.Childhood maltreatment is modestly heritable, with SNP heritability similar to that of depression.Genome-wide analyses identified 14 independent loci for childhood maltreatment. The GWAS signal was enriched for regulatory chromatin marks in brain tissues and for genes that are highly expressed in excitatory neurons. These loci are implicated in mental health disorders, risky behaviours, cardiovascular disease, and intelligence, suggesting considerable locus-level pleiotropy. Although we did not test this, the identified genomic variants might interact with exposure to childhood maltreatment to influence epigenetic patterns, thereby contributing to the risk of developing mental and physical health disorders.Within-family analyses identified significant estimates for active and reactive gene–environment correlation (58%). However, there was no statistical evidence for a difference between passive gene–environment correlation and active and reactive gene–environment correlation. Providing additional evidence for the role of active and reactive gene–environment correlation, childhood maltreatment PGSs are over-transmitted in autistic individuals. It remains unclear which aspects of autism might explain such gene–environment correlation. Importantly, this analysis does not test for the presence or absence of passive gene–environment correlation as all analyses were within-family analyses. Finally, accounting for four known parental risk factors for childhood maltreatment  did not substantially attenuate the variance explained by PGS in prospectively reported childhood maltreatment. These parental risk factors were, however, minimally phenotyped, and we did not have data to test for other parental risk factors (eg, aggressionGiven the complex antecedents and consequences of childhood maltreatment, we investigated genetic correlations between childhood maltreatment and several health-related traits. Childhood maltreatment was genetically correlated with some mental health, reproductive, and physical health traits. Notably, childhood maltreatment was negatively genetically correlated with a number of age-of-onset traits, in line with the life history theory—an evolutionary framework suggesting that childhood maltreatment leads to accelerated development and reproduction, with long-term costs for mental and physical health.Mendelian randomisation analyses found a potential causal effect of childhood maltreatment on major depressive disorder, and bidirectional causal effects on ADHD and schizophrenia, consistent with observational studies.25We did not identify potential causal effects of autism on childhood maltreatment, in contrast to the polygenic transmission disequilibrium test results. This finding might be because the weak instrument used in the autism Mendelian randomisation analysis consisted of only four SNPs or because of the underlying heterogeneity in autism.There are limitations to this study. First, common genetic variants explain a relatively small proportion of the variance in childhood maltreatment, thus non-genetic factors cannot be ignored. Genetic correlations and gene–environment correlation effects must be interpreted while keeping the modest SNP heritability in mind. For example, although there is a high genetic correlation between childhood maltreatment and some mental health conditions, the covariance will be low due to the modest SNP heritability. Second, maltreatment was not uniformly measured across cohorts, increasing variability due to non-harmonised data collection, including age when maltreatment was measured. Third, we do not distinguish between objective childhood maltreatment and subjective valence of childhood maltreatment.The disappointing efficacy of current interventions to curb child maltreatmentdata dictionary. Some of these data were collected using REDCap.The study website contains details of all data available through a fully searchable