N = 134, frequency range 0.0007−0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals ( ARG-Needle is a method to infer genome-wide genealogies from large-scale genotyping data that can be used in association analyses. Applied to UK Biobank data, genealogy-based testing finds more trait associations than using imputed genotypes. Due to the very large number of genealogical relationships that may give rise to observed genomic variation, data-driven inference of these relationships is computationally difficult5. For this reason, available methods for the inference of genealogies rely on strategies that trade model simplification for computational scalability, such as the use of approximate probabilistic models11, scalable heuristics16 or combinations of the two18. Recent advances enabled efficient estimation of the genealogical distance between genomic regions from ascertained genotype data11, rapid genealogical approximations for hundreds of thousands of samples15 and improved scalability of probabilistic inference17. However, available methods do not simultaneously offer all these features, so that scalable and accurate genealogical inference in modern biobanks remains challenging. In addition, these datasets contain extensive phenotypic information, but applications of inferred genealogies have primarily focused on evolutionary analyses. Early work suggested that genealogical data may be used to improve association and fine-mapping19, but the connections between genealogical inference and modern methodology for complex trait analysis22 remain under-explored.Modeling genealogical relationships between individuals plays a key role in a wide range of analyses, including the study of natural selection23 (ARG) for large collections of genotyping or sequencing samples. We demonstrate that the ARG of a sample may be used within a linear mixed model (LMM) framework to increase association power, detect association to unobserved genomic variants, infer narrow sense heritability and perform polygenic prediction. Using ARG-Needle, we infer the ARG for 337,464 UK Biobank samples and perform a genealogy-wide association scan for seven complex traits. We show that despite being inferred using only array data, the ARG detects more independent associations to rare and ultra-rare variants  < 0.1%) than imputation based on a reference panel of ~65,000 sequenced haplotypes of matched ancestry. We use 138,039 exome sequencing samples to confirm that these signals correspond to unobserved sequencing variants, which are strongly enriched for loss-of-function and other protein-altering variation and overlap with likely causal associations detected using within-cohort exome sequencing imputation. Using the ARG, we detect associations to variants as rare as MAF ≈ 4 × 10−6 and independent higher frequency variation that is not captured using imputation.We introduce a new algorithm, ARG-Needle, to accurately infer the ancestral recombination graph9 one haploid sample at a time, as depicted in Fig. The ARG is a graph in which nodes represent the genomes of individuals or their ancestors and edges represent genealogical connections  algorithm11 to estimate the TMRCA between the new sample and each of these individuals at each site, threading to the closest individual. When all samples have been included, ARG-Needle uses a fast postprocessing step, which we call ARG normalization, to refine the estimated node times. ARG-Needle builds the ARG in time approximately linear in sample size (see below).To compute the threading instruction of a sample, ARG-Needle first performs genotype hashing11, called ASMC-clust, that builds genome-wide genealogies by forming a tree at each site using hierarchical clustering on pairwise TMRCAs output by ASMC. This approach scales quadratically with sample size but yields improved accuracy compared with ARG-Needle in certain simulated scenarios (see below). ARG-Needle and ASMC-clust efficiently represent and store ARGs using a graph data structure, which is an adaptation of the representation used within the ARGON simulator26. Additional details, theoretical guarantees and properties for the ARG-Needle and ASMC-clust algorithms are described in the We also introduce a simple extension of ASMC17, tsinfer and a variant of tsinfer designed for sparse datasets we refer to as ‘tsinfer-sparse’15. We considered several metrics to compare ARGs, including: the Robinson–Foulds distance27, which reflects dissimilarities between the mutations that may be generated by two ARGs; the root mean squared error (RMSE) between true and inferred pairwise TMRCAs, which captures the accuracy in predicting allele sharing between individuals; and the Kendall–Colijn (KC) topology-only distance28. We found that the KC distance is systematically lower for trees containing polytomies , which are not output by Relate, ASMC-clust or ARG-Needle . Finally, we assessed the similarity of ARGs inferred using different algorithms, observing highest similarities between ASMC-clust and ARG-Needle, as well as between these methods and, in decreasing order, Relate, tsinfer-sparse and tsinfer , comprising ~65,000 haplotypes. We focused on rare (0.01% ≤ MAF < 0.1%) and ultra-rare (MAF < 0.01%) genomic variants. We used resampling-based testing42 to establish genome-wide significance thresholds of P < 4.8 × 10−11 for ARG variants (sampled with mutation rate μ = 10−5) and P < 1.06 × 10−9 for imputed variants  for ARG-derived variants (P < 5 × 10−8) in the smaller WES-138K dataset, a proportion that dropped to 59% for ultra-rare variants. Variants detected using genealogy-wide association had a larger average phenotypic effect than those detected via imputation . For ultra-rare variants, ARG-derived signals performed better than imputed variants for both precision (bootstrap P = 0.01) and recall (bootstrap P = 0.002). Similarly, ARG-derived and imputed rare variants provided comparable tagging for their WES partners . Compared with ARG-derived variants, genotype imputation has the advantage that associated variants that are sequenced in the reference panel may be directly localized in the genome. We found that for 21 of 52 rare and 2 of 12 ultra-rare independent imputation signals the WES partner had been imputed, while the remaining signals likely provide indirect tagging for underlying variants. ARG-derived and imputed variants, however, had similar distributions for the distance to their WES partners  in ref. 43 WES-138K = 6, MACWES-50K = 1).We compared our results with those of a recent study that leveraged exome sequencing data from a subset of ~50,000 participants  to perform genotype imputation for ~459,000 samplesene Fig.  and fourIn summary, genealogy-wide association using an ARG inferred from common SNPs revealed more rare and ultra-rare signals than genotype imputation based on ~65,000 reference haplotypes, and detected ultra-rare variants that were not associated using within-cohort imputation based on ~50,000 exome-sequenced participants. ARG-derived associations accurately predicted effect sizes for underlying sequencing variants, as well as the subset of carrier individuals.NARG = 103, Nimp = 100; Supplementary Tables P = 0.026; average |βARG| = 0.32, |βimp| = 0.27) but provided lower tagging to WES partners , reflecting the large fraction (42 of 100) of imputation WES partners that were directly imputed.Lastly, we performed genealogy-wide association for low- (0.1% ≤ MAF < 1%) and high- (MAF ≥ 1%) frequency variants, which are more easily imputed using reference panels that are not necessarily large and population-specific. Consistent with this, extending our previous analysis to low-frequency variants yielded a similar number of independent associations for ARG-derived and HRC + UK10K-imputed variants : 2.2 × 10−9, 9.6 × 10−9). To facilitate direct comparison, we selected ARG-MLMA parameters  and adopted a threshold of 3 × 10−9 for all downstream analyses.We hypothesized that, although imputation of higher frequency variants is generally more accurate, branches in the marginal trees of the ARG may in some cases complement available markers by providing improved tagging of underlying variation. This may be the case, for instance, for short insertions/deletions or structural variantsP < 3 × 10−9) for genotype array, imputed or ARG-derived variants. We found that ARG-MLMA detected 98.9% of regions found by both SNP array and imputation, as well as 71% of regions found by imputation but not array data and an additional 8% of regions not found using either imputation or array data  of regions identified using the ARG but not imputation contained associations (P < 3 × 10−9) in a larger meta-analysis by the Genetic Investigation of ANthropometric Traits (GIANT) consortium46  comprising the UK Biobank and additional cohorts. Inspecting associated loci, we observed that ARG-MLMA captures association peaks and haplotype structure found using imputation but not array data  regions that contain an association  association analyses . The fraction of COJO-associated array markers was reduced by the inclusion of ARG-derived or imputed variants, which resulted in comparable proportions of associations when jointly analyzed  COJO signals in the GIANT meta-analysis46  implementation of UPGMA60, we achieve a runtime and memory complexity of O(N2M). We also implement an extension that achieves O(NM) memory but increased runtime  clustering algorithm24. We divide up the sites present in the genetic data into nonoverlapping ‘words’ of S sites and store hash tables mapping from the possible values of the ith word to the samples that carry that word. We use this approach to rapidly detect samples already in the ARG that share words with the target sample and return the top K samples with the most consecutive matches. A tolerance parameter T controls the number of mismatches allowed in an otherwise consecutive stretch. We also allow the top K samples to vary across the genome due to recombination events, by partitioning the genome into regions of genetic distance L. Assuming this results in R regions, the hashing step outputs a matrix of R × K sample identities (IDs) containing the predicted top K related samples for each region. We note, however, that the hashing step can look arbitrarily far beyond the boundaries of each region to select the K samples.ARG-Needle starts with an empty ARG and repeats three steps to add additional samples to the ARG: 1) detecting a set of closest genetic relatives via hashing, (2) running ASMC and (3) ‘threading’ the new sample into the ARG  for all simulation experiments unless otherwise stated and 1.0 cM in real data inference for greater efficiency.The sample IDs output by step 1 inform step 2, in which ASMC is run over pairs of samples. In each of the K candidates at each site of the genome. Note that both the use of a posterior mean estimator with a pairwise demographic prior and the selection of a minimum among K estimated values lead to bias in the final TMRCA estimates , further utilizing the demographic prior provided in input. We compute quantile distributions of node times in the inferred ARG as well as in 1,000 independent trees simulated using the demographic model provided in input under the single-locus coalescent. We match the two quantile distributions and use this to rewrite all nodes in the inferred ARG to new corresponding times  1,000 Genomes samples10. These simulations also output the simulated genealogies, which we refer to as ‘ground-truth ARGs’ or ‘true ARGs’. To obtain realistic SNP data, we subsampled the simulated sequence sites to match the genotype density and allele frequency spectrum of UK Biobank SNP array markers . When running ASMC, we used decoding quantities precomputed for version 1.1, which were obtained using a European demographic model and UK Biobank SNP array allele frequencies, setting two haploid individuals for pairwise TMRCA inference as ‘distinguished’ and sampling 298 haploid individuals as ‘undistinguished’11. ASMC and the hashing step of ARG-Needle also require a genetic map, which we computed based on the recombination rate used in simulations.We used the msprime coalescent simulatorρ ∈ {6 × 10−9, 2.4 × 10−8} per base pair per generation. Second, we used a constant demographic model of 15,000 diploid individuals, for which we generated new decoding quantities to run ASMC. Third, we inferred ARGs using sequencing data, running ASMC in sequencing mode. Fourth, we introduced genotyping errors into the array data. After sampling the array SNPs, we flipped each haploid genotype per SNP and individual with probability p  and ARG-Needle, tsinfer and tsinfer-sparse in up to N = 32,000 haploid samples. All analyses used Intel Skylake 2.6 GHz nodes on the Oxford Biomedical Research Computing cluster.We ran Relate with the mutation rate, recombination rate and demographic model used in simulations. We kept Relate’s default option which limits the memory used for storing pairwise matrices to 5 GB. Because the branch lengths output by tsinfer and tsinfer-sparse are not calibrated, we omitted these methods in comparisons for metrics involving branch lengths. For each choice of sample size, we generated genetic data using five random seeds , performance substantially improved when we selected inferred branches at random and collapsed them to create polytomies , detecting a significant association on the ARG for chromosome 1. For ARG-MLMA UK Biobank analyses we adopted μ = 10−5, also adding variants sampled with μ = 10−3 to locus-specific Manhattan plots to gain further insights. For additional details on our ARG-MLMA methods, including the determination of significance thresholds, see Supplementary Note For simulation experiments Fig.  we testeN haploid individuals, M sites and genotypes xik for individual i at site k, where variant k has mean pk. Under an infinitesimal genetic architecture, the parameter α captures the strength of negative selection66, with a trait’s genetic component given by gi = ∑kβkxik where Var(βk) = (pk(1 − pk))α. Using available markers, a common estimator for the ij-th entry of the N × N GRM21 isConsider α = 0. We discuss the more general case and provide further derivations in Supplementary Note Given an ARG, we compute the ARG-GRM as the expectation of the marker-based GRM that would be obtained using sequencing data, assuming that mutations are sampled uniformly over the area of the ARG. When sequencing data are unavailable but an ARG can be estimated from an incomplete set of markers, the ARG-GRM may provide a good estimate for the sequence-based GRM. We briefly describe how ARG-GRMs are derived from the ARG for the special case of α = 0, equation (1) is equivalent . This may be expressed asL base pairs in the genome and a constant mutation rate per base pair of μ, and let tijk denote the TMRCA of i and j at base pair k. The ij-th entry of the ARG-GRM is equivalent to the expected number of mutations carried by only one of the two individuals, which is proportional to the sum of the pairwise TMRCAs across the genome  to build the ARG-GRM using these mutations. We used simulations to verify that Monte Carlo ARG-GRMs converge to exactly computed ARG-GRMs for large mutation rates, saturating at h2 and α. We varied the number of haploid samples N but fixed the ratio L/N throughout experiments, where L is the genetic length of the simulated region. For heritability and polygenic prediction experiments, we adopted L/N = 5 × 10−3 Mb per individual. For association experiments, we simulated a polygenic phenotype from 22 chromosomes, with each chromosome consisting of equal length L/22 and L/N = 5.5 × 10−3 Mb per individual. Mixed-model prediction r2 and association power may be roughly estimated as a function of h2 and the ratio N/M, where M is the number of markers70. We thus selected values of M and L such that the N/M ratio is kept close to that of the UK Biobank .We simulated polygenic traits from haploid sequencing samples for various values of 38) within-cohort imputation) and sequencing data, and performed complex trait analyses using GCTA21. Polygenic prediction used cvBLUP71 leave-one-out prediction within GCTA. ARG-GRM association experiments  compared with linear regression. We observed that MAF-stratification for ARG-GRMs of true ARGs enabled robust heritability estimation and polygenic prediction if α is unknown  and extracted the unrelated white British ancestry subset defined in ref. 38, yielding 337,464 samples. We built the ARG of these samples using ARG-Needle, using parameters described above. We parallelized the ARG inference by splitting phased genotypes into 749 nonoverlapping ‘chunks’ of approximately equal numbers of variants. We added 50 variants on either side of each chunk to provide additional context for inference and independently applied ARG normalization on each chunk. For our brief comparison of ARG inference methods in real data , we removed 135 samples  and 57,126 variants (missingness > 10%). We performed reference-free phasing of the remaining variants and samples using Beagle 5.1 . For association of imputed data (including SNP array) we restricted to variants with Hardy–Weinberg equilibrium P > 10−12, missingness < 0.05 and info score > 0.3 (matching the filtering criteria adopted in ref. 38). For all analyses we did not test variants with an MAC < 5 and used MAF thresholds detailed below.To process phenotypes /high-density lipoprotein (HDL) cholesterol, mean platelet volume and total bilirubin) we first stratified by sex and performed quantile normalization. We then regressed out age, age squared, genotyping array, assessment center and the first 20 genetic principal components computed in ref. P < 4.8 × 10−11  for ARG and P < 1.06 × 10−9  for imputation ; Supplementary Table −9 , obtained for μ = 10−5, MAF ≥ 1%; and 4 × 10−9 , obtained for μ = 10−6, MAF ≥ 0.1%. We selected the former set of parameters, as a low sampling rate of μ = 10−6 leads to worse signal-to-noise and lower association power. We thus used a significance threshold of P < 3 × 10−9 for all analyses of higher frequency variants. We used PLINK75 (v.1.90b6.21 and v.2.00a3LM) and GCTA21 (v.1.93.2) to detect approximately independent associations using COJO47, retaining results with COJO P < 3 × 10−9  who did not have genotype missingness > 10% and had not withdrawn from the UK Biobank at the time of our analysis. To further explore our findings, we selected the 138,039 of these individuals who were exome sequenced in the 200,000 UK Biobank whole-exome sequencing release.For real data analysis in the UK Biobank, we included all 337,464 individuals of white British ancestry  from association of quantitative traits in 337 K UK Biobank individuals, along with the best partner according to LD in whole-exome sequencing (WES) data of 138 K UK Biobank individuals. Table 3. Approximately independent imputation signals with MAF < 0.1% (N = 64) from association of quantitative traits in 337 K UK Biobank individuals, along with the best partner according to LD in whole-exome sequencing (WES) data of 138 K UK Biobank individuals. Table 4. Approximately independent ARG signals with 0.1% ≤ MAF < 1% (N = 103) from association of quantitative traits in 337 K UK Biobank individuals, along with the best partner according to LD in whole-exome sequencing (WES) data of 138 K UK Biobank individuals. Table 5. Approximately independent imputation signals with 0.1% ≤ MAF < 1% (N = 100) from association of quantitative traits in 337 K UK Biobank individuals, along with the best partner according to LD in whole-exome sequencing (WES) data of 138 K UK Biobank individuals.Table 1. Resampling-based tests of subsets of HRC-imputed and ARG-Needle-inferred ARG variants against random phenotypes. Table 2. Approximately independent ARG signals with MAF < 0.1% (


Haploview” and “HaploBlocker”. The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no “best” method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software “ With the advance of sequencing technology nowadays, genomic data is used to replace the pedigree relationship with realized relationships calculated from dense marker maps. Furthermore, with the inclusion of genetic markers, information about linkage disequilibrium and cosegregation is available for genomic prediction , powdery mildew resistance, protein content, protein yield sedimentation value spike m-2, stripe rust and thousand kernel weight (TKW). All lines were genotyped with the Illumina 15k wheat SNP array described in The datasets examined in this study are all publicly available. The canola dataset is from a spring-type canola hybrid breeding program . BrieflyNP array . The maiNP array . The wheWith the exception of the maize dataset, all phenotypic data represented adjusted trait means per genotype. The published field data from the maize population was adjusted following methods used for phenotypic data analyses from the original publication.2.2Brassica napus Express 617 genome , while the latter three are implemented in the software “Haploview” v4.1  and gamete frequency. They aim to model historical recombination hotspots and generate meaningful blocks within populations. However, these blocks do not necessarily represent functional groups. Therefore, we also included methods based on fixed windows to assess blocks that would not be constructed based on population-based measures alone. Additionally, while most methods consider block borders across the entire population, it is important to note that subpopulations or genotypes may have different recombination patterns. To account for this, we utilized the method “HaploBlocker” described in We considered seven haplotype block construction methods based on (i) pre-determined LD thresholds, (ii) fixed windows of adjacent markers, (iii) fixed windows of adjacent base pairs, (iv) Blocker” , (v) theD method . The firew” v4.1 . The dif2.3.12r  described by The 2.3.7Solid Spine of LD method (SPI), introduced by the developers of “Haploview”  and inbred line  performance, respectively. The models represent two variations of the GBLUP and two models implemented in a Bayesian framework. The frequentist models were GBLUP  and exteIn the GBLUP and EGBLUP the underlying model is assumed to be:where It is assumed thatwhere The additive genomic relationship matrix was calculated following i) for genotype HiHi, (1-2pi) for genotype HiHj and (2-2pi) for genotype HjHj, where Hj is the haplotype (treated as a single marker) within a haplotype block, Hi is any other haplotype within that haplotype block except Hi, and ith haplotype in a haplotype block. Haplotype blocks with only two haplotypes were treated like standard biallelic markers. For the canola dataset, prior to construction of the genomic relationship matrix, parental genotypes were crossed in silico to derive hybrid genotypes, as described by with the elements of According to sommer  between observed and predicted phenotypic values of the validation population was used as a measure of prediction accuracy.33.1In all the datasets analyzed, haplotypes of varying sizes were examined. The haplotype blocks had average physical sizes ranging from 1.02 kbp to 47453.13 kbp, 379625.06 kbp, 1073741.82 kbp, and 47453.13 kbp, respectively, for canola, maize, wheat and soybean. A summary of the average size distributions can be found in “Haploview” , the number of haplotype blocks was consistently lower than the number of total SNPs  increased the number of haplotypes compared to the number of SNPs  or very rare (Frequency ≤ 0.01)  with little variation observed across LD thresholds. Only at very low LD thresholds, this correlation was slightly lower, while it was slightly higher for very high thresholds (nKB = 67108.86)  consistently outperformed by the other SNP-based models  models that consider epistasis and those that do not, (ii) frequentist and models implemented in a Bayesian framework, and (iii) combinations of (i) and (ii) S7. Howe-1 with EGBLUP, spike m-2 with RKHS and EGBLUP, and stripe rust resistance with GBLUP, did the prediction accuracy decrease compared to SNP-based prediction when using haplotype blocks , fixed window sizes of adjacent marker or base pairs as well as the three algorithms implemented in the software “Haploview” and the method “Haploblocker”. The 2r measurement of LD between markers , prompting the inclusion of Bayesian LASSO, which allows for marker or haplotype-specific shrinkage of effects towards zero. This is beneficial in scenarios where not all markers or haplotypes have an impact on the trait. Given the assumption that haplotypes capture local epistatic effects , EGBLUP Generally, genomic prediction accuracies based on SNPs were similar to those reported in the literature across all datasets. In the canola dataset, accuracies closely matched Genomic prediction with LD-based haplotype blocks in canola resulted in the highest accuracy improvements for most model/trait combinations. Variation in prediction accuracy across LD thresholds was minimal. The optimal threshold varied significantly by trait and model, ranging from very low (0.01) to high (0.89). In wheat, LD-based haplotype blocks were superior to the other haplotyping methods for 20 out of 60 model/trait combinations, but accuracy didn’t always improve compared to SNP-based prediction. Similar low variation across LD thresholds was observed in soybean. For soybean’s oil content, the ideal LD threshold for accuracy estimates across all models was 0.24-0.26. In maize, only the Bayesian LASSO and RKHS models achieved the highest improvements with LD based haplotype blocks with a threshold of 1, effectively removing redundant information. In this scenario, only markers in complete LD were grouped into a block, effectively removing redundant information. This process, is similar to LD pruning, which has been demonstrated to enhance prediction accuracy . Intrigu-1. This improvement can likely be attributed to introgression breeding in wheat, where large chromosome segments are introgressed and preserved due to restricted recombination  based haplotyping. KV-F and MF supervised the statistical analysis. SW wrote the manuscript. RS and KV-F revised the manuscript. All authors contributed to the article and approved the submitted version.


The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.The online version contains supplementary material available at 10.1186/s12284-023-00643-2. Rice is a staple crop feeding more than half of the human population  the potential of using genomic prediction enhanced sparse phenotyping to establish a cost-efficient multi-environment training set, and 2) whether using the haplotype-based genomic prediction models based on LD-derived haplotype blocks could improve the prediction accuracy relative to the marker-based approaches in rice.The first population is from Spindel et al.  includinThe second population comes from Liang et al.  incorporIndica lines. These RILs were phenotypically evaluated for DTH and PH in DS and WS of year 2014 in International Rice Research Institute (IRRI) and two locations in China, namely Jiangxi (JX) and Shenzhen (SZ), in year 2016. The combination of season/location and year was regarded as the environment, thereupon phenotypic data of four environments was available for the 1048 RILs. These RILs were fingerprinted by a customized rice 55 K SNP array  of genetic effects of all lines. Specifically, in the first stage, the spatial adjustment of field trial data was conducted in each environment by fitting the model: To infer the haplotypes, the genotypic data after quality control was specifically imputed and phased using SHAPEIT software  and haplotypes (haplotype-based model). Following variables were identically defined in all models: n et al. . In markThe first model considers no genotype-by-environment interaction formulated asThe second model explicitly portrays genotype-by-environment interaction asth environment. We assumed th environment, th environment. In our study, we specified t = 1 (one latent factor) in all the three populations.The third model is the factorial analytic (FA) model  with 5 folds in Burgueño et al.  to asses with 5 fThe 5-fold cross-validation was repeated 10 times, yielding 50 divisions of training and test sets. Finally, there were totally five hundred times (5 × 10 × 10) calibrations and predictions for each training set missing rate (10–90%). Considering the test set size in the cross-validations is just 1/5 of the size of total population, we combined the environment-specific genomic predicted genetic values of the lines in the five test sets per repeat of cross-validation and used the Pearson correlation coefficient between the combined genomic predicted genetic values and corresponding environment-specific BLUEs to assess the genomic prediction accuracy. To statistically compare the prediction accuracies of different genomic prediction models, we firstly transformed the accuracies (correlations) using Fisher's z transformation i.e., z = 0.5 × ln((1 + r)/(1 − r)) where r is the correlation. The Student’s t-test was used to test the difference based on the transformed correlations.The repeatability estimates of DTH in each environment of both the first and the second populations were all higher than 0.9  higher than those of marker-based model disregarding the trait and phenotyping intensity of training set. There was no benefit of segmenting the haplotype blocks into small fragments with two or three SNPs despite the VG and VGR models using small haplotype blocks occasionally outperformed their marker-based counterparts in PH  superior to the marker-based models in DTH except for a high phenotypic missing rate in the training set. As compared, the image in PH was conversed that the haplotype-based methods were basically inferior to the marker-based model especially for the FA model  held the superiority of using environmental covariates to depict environments and their relationships.Modelling the covariances between environments in multi-environment genomic prediction was overall advantageous in our study Fig.  found usn et al.  comparedn et al.  revealedn et al.  in a japWang et al.  proposedThe multi-environment genomic prediction can help to discover the elite rice varieties resilient to diverse environments or particularly suited to a specific environment. To improve the efficiency of multi-environment genomic prediction, sparse phenotyping can be used to establish a multi-environment training set. We demonstrated that a 30% phenotyping intensity in the multi-environment training set is sufficient to provide a comparable prediction accuracy to high phenotyping intensities for traits like PH and DTH. Basing on LD to identify haplotype blocks and accordingly making haplotypes for genomic prediction could capture local epistatic effects more reasonably compared to a fixed length of haplotypes. We demonstrated that the haplotype-based models are worth to be implemented in the prediction of DTH and segmenting the haplotype blocks into small fragments with two or three SNPs could maintain the predictive ability of haplotype-based models in large populations. Modelling the covariances between environments improves multi-environment genomic prediction accuracy irrespective of capitalizing on marker genotypes or haplotypes.Additional file 1: Table S1. Heritabilitiesand repeatabilities in each environment for days to headingand plant heightin the three rice populations.Additional file 2: Fig. S1. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the first population. The title of each barplot indicates the environment. Fig. S2. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the second population. The title of each barplot indicates the environment. Fig. S3. Distribution of best linear unbased estimatesof genetic values of lines fordays to headingandplant heightin each environmentof the third population. The title of each barplot indicates the environment. Fig. S4. Genetic diversity of thefirst,second, andthird populations described by Euclidean distance between lines based on SNP genotypic scores. The average clustering method was used to order the lines.Additional file 3: Table S2. Genomic prediction accuraciesof days to headingand plant heightin the three rice populations using three prediction modelswith different missing rates of environment-specific best linear unbiased estimatesof genetic effects of lines in the training set. The genetic effect was respectively described by SNP genotypes, haplotype using complete blocks, and short haplotypes containing two SNPsand three SNPswithin a haplotype block. The missing rate zeroindicates the training set holds complete environment-specific BLUEs. The asterisk and pound sign indicate the prediction accuracies of haplotype-based approaches were statistically significantlyhigher and lower than those of the marker-based approach.


Elaeidobius kamerunicus is the most efficient pollinator of oil palm. Wolbachia is an endosymbiotic bacteria associated with E. kamerunicus that has a potential to affect the fecundity and fitness of the E. kamerunicus. Despite their importance, no studies have been conducted to investigate its prevalence in E. kamerunicus. The objectives of this study were to detect and characterise Wolbachia in E. kamerunicus and determine the phylogenetic relationship of Wolbachia strains that infect E. kamerunicus by using three genetic markers namely Filamenting temperature-sensitive mutant Z (ftsZ), Chaperonin folding protein (groEL), and Citrate Synthase Coding Gene (gltA). DNA was extracted from 210 individuals of E. kamerunicus and the Wolbachia infections were detected using the wsp marker. The infected samples  were then sequenced using ftsZ, gltA and groEL markers for strain characterization. In this study, a combination of four markers was used to construct the phylogeny of Wolbachia. Similar topologies were shown in all trees; Neighbour-Joining (NJ), Maximum Parsimony (MP), and Bayesian Inference (BI), which showed the mixing of individuals that harbor Wolbachia between populations. Interestingly, Wolbachia on E. kamerunicus was claded together with the species Drosophila simulans under supergroup B. This is the first report of Wolbachia infecting E. kamerunicus which is very valuable and significant as one of the parameters to evaluate the quality of the E. kamerunicus population for sustaining its function as a great pollinator for oil palm. Wolbachia species.All sequences were confirmed using BLAST search tools, which reflected an identical percentage ranging from 98% to 100% for the Wolbachia between populations.The similar topologies in Neighbour-Joining (NJ), Maximum Parsimony (MP), and Bayesian Inference (BI) trees showed the mixing of individuals that harbour Wolbachia in E. kamerunicus and Drosophila simulans (supergroup B) were approximately 0.02 and 0.03, respectively.Based on the genetic distance analysis, the genetic distance values between Elaeidobius kamerunicus Faust (Coleoptera: Curculionoidea) is identified as the most efficient insect pollinator of the oil palm  yields had tremendously declined, causing several billion dollars in losses annually  (gltA) (groEL) , Chaperonin folding protein (groEL), and Citrate Synthase Coding Gene (gltA).The filamenting temperature-sensitive mutant Z  from seven different locations: Supa, Sarawak and Lahad Datu, Sabah representing the Borneo region . Peninsular Malaysia has been represented by Keratong, Pahang ; Banggol Rashid Sedim, Kedah ; Kluang, Johor ; Jengka, Pahang ; and Teluk Intan . The random sampling has covered 1 hectare (100 m × 100 m), which a total of 30 trees  selected for the molecular work. The variety of locations was aimed to elicit the differences in the presence of Wolbachia in the E. kamerunicus samples. The samples of E. kamerunicus were collected from male flowers when the oil palm was in the anthesis stage. The flowers were gently cut in the morning between 8.00 a.m. and 9.00 a.m. (which was not within the weevils’ active periods) for accessible collection. Insects were placed into plastic containers (24.5 cm × 13.5 cm × 13.0 cm). The spikelet of the male flowers was returned to the Cytogenetic laboratory in the Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM). The adult samples of E. kamerunicus were preserved in 90% ethanol prior to DNA extraction.A total of 210 individuals of E. kamerunicus was used in the extraction procedure to obtain a sufficient sample for the analysis. The extraction procedure required 40 μL MG buffer added with 10 μL of liquid proteinase K into the sample column and agitated for 20 min. Then, 600 μL of MG buffer was added and spun at 11,000 revolutions per minute (rpm). Before cleaning the lid and cell debris around the column wall, a high rotation speed was required. The solution was then transferred into a new collecting column using a filter collection tube to wash the silica membrane procedure. BW solutions were used for the first wash after the 11,000 rpm spin, followed by B5 solution for the second spin. In each step, all flow through in the collecting tube was discarded. Finally, 100 μL of elution buffer was added to the collection tube to obtain a highly pure DNA sample.DNA extraction was performed using Nucleospin  extraction kit, following the standard operating procedure stated by the supplier. The whole body of wsp gene marker was used to detect the presence of Wolbachia. The sequences used to amplify DNA fragments were 81F 5′ TGG TCC AAT AAG TGA TGA AGA AAC-3′ and 691R 5′-AAA AAT TAA ACG CTA CTC CA-3′. The DNA fragment′s length produced was approximately 600 bp  for species confirmation.All the PCR techniques were detected by amplifying the expected results. The PCR products were run in 1.5% agarose gel and observed under ultraviolet light. Only samples with a positive result from groeL, the phylogeny using NJ based on the Kimura-2 parameter was constructed to determine the supergroup of EK Wolbachia. Representatives of Wolbachia from three localities that were detected harbouring Wolbachia  were used along with other four individuals from different groups of insects obtained from the Genbank: termite , pteromalid wasp (Nasonia giraulti), fruit fly (Drosophila simulans), and flea  were used to constructing the phylogeny tree and determine the supergroup of Wolbachia in EK. Genetic distance between species was measured using MEGA 3.1 , Maximum Parsimony (MP), and Bayesian Inference (BI) were implemented to construct the phylogenetic trees. All sequences were combined and analysed using Incongruence Length Differences (ILD) in PAUP 4.0b . The NJ E. kamerunicus were screened using wsp. Only 25 samples were detected with Wolbachia infection, represented by 10 males and 15 females (12%)  . All seqgroEL marker was used to support information of the wsp gene marker that the Wolbachia strain infected E. kamerunicus was from a supergroup B. This was executed by constructing the phylogeny tree using the groEL gene information. Kalotermes flavicollis was selected as the outgroup for the phylogeny tree  were approximately 0.02 and 0.03, respectively. In comparison, the genetic distance between E. kamerunicus and Nasonia giraulti (supergroup A) was 0.9, while that of Ctenocephalides felix (supergroup D) was 1.0. Therefore, the small genetic distance between E. kamerunicus and D. simulans (supergroup B) supports the idea that the Wolbachia strain in this study was grouped with supergroup B  value of individual parts was supported between 0.75 p.p and 0.98 p.p  were suitable and approved for the combination of marker genes. The distance criteria had been set to construct the NJ tree on the combination tree The NJ tree depicts that one clade has formed even though an individual he clade . Small ghe clade . The boo0.98 p.p .ftsZ, gltA and groEL has shown the form of soft polytomy trees. All the tree topologies are similar, which separated two individuals, male EK  and female EK , from the rest of the infected samples in MP and BI trees . The effectiveness of the wsp to make detection on Wolbachia in several hosts was supported by the study of Bactrocera spp. (Drosophila auraria (Diptera: Drosophilidae) by wsp marker is also compatible with investigating the relationship of evolution in multiple strains of Wolbachia in Drosophila species that identifies the effects of cytoplasmic incompatibility on the progenies  had shown their abilities to prove the divergence between the Wolbachia strains infecting a wide variety of species by constructing a phylogeny tree  were used to study the Wolbachia in galling aphid species (Hemiptera: Aphidoidea) by All three markers  (Coleoptera: Tenebrionidae)  (Lepidoptera: Crambidae)   and Ostrambidae) .Wolbachia in the E. kamerunicus is specifically from supergroup B, which resembled that of the Wolbachia found in the model moth species, Eurema hecabe. They are probably similar and transform genetic males into females, consequently increasing the number of individuals in the population   and species that are present in other populations. Population genetic and demographic analyses were vital in this study to understand the diversity of this strain according to different locations. Both analyses confirmed the relationship between Tamarixia radiata parasitoid (Hymenoptera: Eulophidae) and Wolbachia in the population and between different regions   . According to specific agriculture companies, the source of oil palm samplings could also contribute to the ability of gene displacement happening between plantations of different regions.Though the isolation of the population was expected to form according to the respective population in the early stage or hypothesis, several things may happen due to higher gene flow between E. kamerunicus’ ability to fly, high activity in the rainy season, and breed are better than other Elaeidobius spp. , thus allowing Wolbachia to spread at a higher rate   should be conducted in order to obtain more extensive information on Wolbachia strains, the relationship between strains, and the effects of infection. Through MLST, information related to the studied species could produce more specific results such as the diversity of strains, group isolation according to molecule information, and the relationship between strains studied. Such vital information will assist in enhancing sustainable oil palm production, thus boosting the production of one of the leading natural commodities of Malaysia.The ability of


Copris umbilicatus. Moderate to excellent levels of repeatability were found in all behavioural traits considered. Results suggest the existence of a behavioural syndrome involving thanatosis and activity, with ‘bolder’ individuals exhibiting shorter thanatosis and higher locomotor activity, in contrast with ‘fearful’ individuals which display longer thanatosis and poor locomotor activity. Noticeable differences between individuals, which were not due to differences in sex or body size, could be attributable to differences in personality among individuals.Not only vertebrates but also invertebrates can display personalities and behavioural syndromes. Here, three multiple behaviours  were investigated in Copris umbilicatus, which is a dung beetle species showing complex sub-social behaviour. We analysed three behaviours  by measuring seven distinct behavioural traits . We found moderate to high levels of individual repeatability in all behavioural traits considered. The duration of thanatosis was inversely correlated with two activity traits, hinting a behavioural syndrome for thanatosis and activity, with bolder individuals exhibiting shorter thanatosis and higher locomotor activity in contrast with fearful individuals, which display longer thanatosis and poor locomotor activity. No relationships were found between the behavioural traits and body size or sex. Results of the principal component analysis (PCA) suggested personality differences among individuals. Dung beetles provide an impressive variety of ecosystem services. Since the provision of these services may depend on the personalities represented in local populations and communities, studies on the ecology of personality in dung beetles should be encouraged in future research.Although personality studies have primarily focused on vertebrates, the evidence showing invertebrates to be capable of displaying personalities has been steadily growing in recent years. In this study, we investigated the behavioural repeatability (repetition of a behaviour over time) and behavioural syndromes (a set of correlated behaviours) in  In animals, a widely applied proxy for “personality” consists of inter-individual behaviour variations which should be repeatable over time and across ecological contexts ,2,3. AccA behavioural syndrome is a set of correlated behaviours expressed either within a given behavioural context or across different contexts . It essePersonality studies have generally focused on vertebrates ; howeverLocomotor behaviour, commonly referred to as ‘activity’, is an ecologically relevant behaviour often positively correlated with aggressiveness and boldness . PersonaThanatosis, also known as ‘death-feigning’ or ‘tonic immobility’, is a commonplace response to external stimuli in insects ,21,22, aSound emission has been broadly studied in insects  since stDung beetles  are a large group, comprising around 6200 species divided among 267 genera, with almost global distribution . These bCopris umbilicatus Abeille de Perrin, 1901  personality, by assessing the repeatability of different behaviours over time ; (ii) beCopris umbilicatus were collected by hand in the spring of 2021 from pastures belonging to the Spedaletto Farm in Cantagallo, Tuscany, Italy, . Dung beetles were reared kept in the vivarium facilities of the Department of Life Sciences and Systems Biology (DBIOS) at the University of Turin. All specimens were housed in separate plastic terrariums , at constant temperature (23 °C), from May through to the end of the experiment (June). A unique distinctive alphanumeric code was assigned to each individual to create the database of measures.Fifteen females and ten males of To measure the repeatability of a behaviour over time in response to a stimulus, subjects should be tested several times, with short time intervals between each test ,6. On thC. umbilicatus. The arena was divided into eight radial sectors of equal length by drawing lines on the back of the larger Petri dish which intersected at its centre.We assessed the locomotor activity of individuals using an annulus-shaped arena in accordance with previous studies ,14. All We measured the following three behavioural traits: (1) ‘distance moved’, quantified as the total number of radial sectors traversed in a single run; (2) ‘locomotory speed’, calculated as the distance travelled per unit time (mm/s) to cross four radial sectors; and (3) ‘movement duration’, calculated as the overall time (expressed in seconds) spent moving during the run, i.e., the moments of immobility were deducted from the 3-min run. Some individuals reversed the direction of travel during the run. We continued counting the number of sectors and measuring the other behavioural traits as they travelled in the opposite direction.At the end of each run, before positioning the next individual, the arena was cleaned to remove droppings or possible pheromonal trails on the route, which could influence the activity of the next individual . Each ruWe measured thanatosis using a single trait: the time taken for each individual to emerge from stasis after a disturbance—the act of being picked up by hand (thus removed from the soil) and placed in a Petri dish in a supine position. We considered any clear signs of movement, such as attempts to roll over to return to the prone position, as the end of the death-feigning behaviour. If a beetle did not fall into thanatosis, the duration of death-feigning was recorded as zero. Copris lunaris [Individuals were positioned 1 cm away from a sound level meter model 2235)  whilst gently holding them by the front legs using two fingers to allow free movement of the abdomen. The sound level meter was calibrated with a 1000 Hz sound produced by a Brüel & Kjær 4230 acoustic calibrator. The sampling rate of the sound recording instrument was set to 48 kHz/16 bit, as it is suitable for detecting stridulations in  lunaris . Each reWe used the sound analysis software Avisoft-SAS Lab Pro v5.2.13 (2019)  to identify and categorise the sound recordings. All emitted sounds used in our analysis were quantifiable as acoustic distress signals ,35, consM function. A recording with an oscillogram containing only a few stridulations will have a value close to 0, whereas a recording with an oscillogram characterised by many stridulations will have a value close to 1. (2) ‘Frequency’ is calculated with the function meanspec as the ratio of the mean frequency to the frequency at the 97.5 quantile of all stridulations in the recording. The frequency at the 97.5 quantile was considered a good approximation of the maximum frequency, which we decided not to use to avoid problems of incorporating sounds not emitted by the insect, namely occasional accidental sounds such as those caused by the operator’s finger or an insect’s leg hitting the sound level meter. (3) ‘Spectral flatness’ is defined as the ratio of the geometric mean to the arithmetic mean of a power spectrum [Using the ‘seewave’ package  of the Rspectrum ,65. Specspectrum . We can spectrum . IndividIn addition, we considered another complementary parameter that may indicate individual personality in sound emission, namely the tendency to stridulate. This parameter was calculated by considering the number of recordings which were needed to obtain at least 25 complete stridulations  to define personality. We then looked for correlations between behaviours to identify behavioural syndromes. Finally, to analyse behavioural differences among individuals, we tested for the contemporaneous effect of non-behavioural traits .ICC of the R package ‘psych’ [ICC function relies on a mixed effect model, we checked model assumption by fitting the same model by using the lmer function of the ‘lme4’ package [We assessed behavioural consistency  by quantifying the repeatability coefficient  for the seven behavioural traits  using the intraclass correlation coefficient (ICC), defining moderate to excellent levels of repeatability  between ‘psych’ . Given t package .To investigate the presence of associations between the different behavioural traits, we calculated Spearman’s rank correlation coefficients. Correlation analyses were conducted in two stages: the first between the traits related to the same behaviour and the second between traits related to different behaviours.The relationships between each behavioural trait and body size, sex and their interactions were tested by linear regression.2), representing the proportion of variance explained by these two variables, was calculated to evaluate the relative importance of body size and traits in explaining the PCA results. The PCA was calculated with the rda function of the R package ‘vegan’ [Principal Component Analysis (PCA) was performed on activity and distress call behaviours (three traits for each) separately to look for any trends in the data . PCA was also performed on all three behaviours combined . Vectors and factor averages were fitted on the PCA results and tested for significance using permutation tests to search for a relationship between the first two PCA coordinates, body size and sex. Furthermore, the squared correlation coefficient (R ‘vegan’ . Data we ‘vegan’ .Repeatability estimates  were notp < 0.001), between distance moved and movement duration  and between locomotor speed and movement duration ; in relation to ‘sound emission’, we found negative and statistically significant correlations between the median amplitude envelope and spectral flatness  and between spectral flatness and frequency . The correlation between median amplitude envelope and frequency was not significant . The tendency to stridulate correlated positively with the median amplitude envelope  and frequency , whereas it correlated negatively with spectral flatness .Concerning the correlations between traits pertaining to the same behaviour, in relation to ‘activity’, we detected strong positive and statistically significant correlations between distance moved and locomotory speed  and locomotory speed , which thus suggested the existence of a syndrome. No other significant correlations were found between behavioural traits belonging to different behaviours: there were no significant correlations between sound-related traits and activity, and there were no significant correlations between thanatosis duration and sound-related traits.With regard to correlations between traits pertaining to the different behaviours, thanatosis duration was negatively correlated with distance moved .To investigate the possibility that behavioural differences between individuals might depend on sex and/or body size, we tested the relationship between each behavioural trait and body size, sex and their interactions by means of linear regressions. No relationship was found between the investigated traits and body size, sex and their interactions , except We then focused on behavioural differences between individuals, considering one behaviour at a time. PCA analyses performed on the separate activity and distress call traits did not reveal any clear behavioural groups , and no significant relationship was found between PCA results and body size or sex .Duration of thanatosis varied from individual to individual. The distribution was right-skewed, with 56% of the individuals showing values below 50 s .Finally, we tested for behavioural differences between individuals by considering all three multiple behaviours  together.In this all-inclusive analysis, the first two PCA axes accounted for 38.8% and 27.8% of the variability in the data for a total of 66.6% .2 = 5.20%, p-value = 0.569) or sex , providing strong evidence that these two parameters are not responsible for the inter-individual behavioural differences found.The first PCA axis was positively related to activity  and negatively related to thanatosis . This reThe present study clearly revealed behavioural differences among individuals, such that the mix of behavioural traits displayed by any one individual distinguished it from all others, contextually showing that neither sex nor body size could account for these differences.Copris umbilicatus expresses personality, keeping with what has been found in other studies on personality related to locomotory activity in Callosobruchus maculatus [Tribolium castaneum [Nebria brevicollis [Tribolium confusum [Phaedon cochleariae [Tenebrio molitor [Onthophagus furcatus and O. ruficapillus [All the behavioural traits measured were repeatable, confirming the consistency of behaviour over time. The present finding suggested that the dung beetle aculatus , Triboliastaneum  and Nebrvicollis , and thaconfusum , Phaedonhleariae , Tenebri molitor , Onthophcapillus .The high repeatability values of the behavioural traits related to the emission of distress calls are utmost interesting, since sound emissions are commonly regarded as a consequence of stress or intraspecific communication rather than a possible personality-defining trait. Although it has been suggested that certain sound parameters may depend on the morphology of the stridulatory organs ,28, the Significant correlations were obtained between traits belonging to the same behaviour, with high levels of correlation between traits related to distress signals as well as between traits related to locomotor activity. The presence of intra-behavioural correlations suggests that these traits must be considered together because they contribute to describe behaviours as locomotory activity and distress calls as formerly defined ,15,19.C. umbilicatus, it is foreseeable that a bold individual exhibits shorter thanatosis and higher locomotor activity, whereas a fearful individual displays longer thanatosis and poor locomotor activity.We also highlighted a significant correlation between traits belonging to different behaviours, namely thanatosis duration and two activity parameters. Likely, individuals feigning death for a longer timespan were also those which run for shorter distances and at lower locomotory speed; the opposite was also true as those feigning death for a shorter timespan tended to run longer distances at a higher locomotory speed. These results suggest the existence of a behavioural syndrome linking thanatosis and activity as previously detected in mustard leaf beetles , ground C. umbilicatus may be related to exploratory behaviour, the latter one being directed at acquiring information about the environment [Thanatosis and locomotor activity can be considered alternative and opposite anti-predatory strategies ,24,25. Aironment . This inCopris umbilicatus individuals were not correlated with either thanatosis or activity. In many insects, stridulations have been identified to be an anti-predatory strategy [Garrulus glandarius) may use stridulations to locate and catch their prey, such as cicadas, Cicada orni [Copris species, namely, Copris lunaris, females were seen to emit stridulations to repel experimentally introduced unfamiliar conspecifics away from their nesting areas [Stridulations emitted by strategy ,81,82. Hada orni . It shoung areas ,84 and, ng areas . In shorAll of the above considered, further ad hoc studies are needed to ascertain and understand the possible ecological implications of thanatosis, activity and stridulations in dung beetles.C. umbilicatus, a significant, albeit weak, interaction was revealed. Opposite trends were found in the two sexes about the body size/frequency relationship: being male was associated with higher frequencies for larger body sizes, whereas the opposite was true in females, with frequencies decreasing as body size increased. The results of PCA also sustained that the behavioural characteristics do not depend on sex or body size. Discordant results have been obtained in insects, with some studies suggesting that sex does not significantly influence personality [We had expected that behavioural differences among individuals could depend on sex and/or body size; except for stridulation frequency, no relationships between the investigated traits and body size, sex and their interactions were found using linear models. Regarding the frequency of the stridulation sounds emitted by sonality ,19,24, asonality ,86 or bosonality .The most relevant result of the PCA analyses concerns the ordination of individuals, which were highly scattered in the bi-dimensional plane, particularly in the analysis performed on multiple behaviours. The scattered distribution reflects the expression of different behaviours by different individuals; in other words, each individual displays behavioural characteristics that distinguish it from all the others, which is in accordance with the results of several previous studies ,87,88. OIncidentally (and interestingly), PCA also confirmed and reinforced the results of the correlation analyses: locomotor behaviour and thanatosis were located on the same axis, but the direction of the correlations was opposite (in keeping with the negative correlation between the two behaviours), whereas the traits relating to distress calls were located on different axes .Given the importance of dung beetles to ecosystem functionality, the study of their personality could be useful for gaining a better understanding of the ecosystem services that these beetles offer. Through the manipulation of livestock faeces for their feeding and nesting processes, dung beetles contribute, first and foremost, to dung removal, but also to bioturbation, nutrient cycling, mineralization processes, plant nutrient uptake and plant growth enhancement ,89, all


Following this, crosshap is equipped with visualization tools for choosing optimal clustering parameters (ɛ) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotype, and metadata traits.Genome-wide association studies (GWAS) excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present crosshap package is freely available under the MIT license and can be downloaded directly from CRAN with R >4.0.0. The development version is available on GitHub alongside issue support (https://github.com/jacobimarsh/crosshap). Tutorial vignettes and documentation are available (https://jacobimarsh.github.io/crosshap/).The  Rapidly accumulating genomic sequence information presents opportunities to identify and characterize the effects of variants conferring beneficial adaptations. However, with increasing population sizes, the dimensions of variant information increase exponentially, raising interpretability issues when analysing large genomic datasets. Genome-wide association studies (GWAS) offer a scalable approach to identify candidate regions responsible for influencing a phenotype, though they do not provide insight into the complex local landscape of linkage that contains and, in many cases, has been shaped by selection for the causal variant(s).Statistical fine-mapping is the standard approach for visualizing phenotypic associations for local genomic variants surrounding a GWAS result  and producing intuitive crosshap figures that present information on the complex relationships between linked variants, haplotype combinations, and phenotype and metadata traits of individuals.Here, we present crosshap workflow is outlined in crosshap requires genomic variant information in VCF format, delimited to a region of interest (see https://jacobimarsh.github.io/crosshap/articles/Delimiting_region.html), along with a square matrix reporting a pairwise LD statistic (e.g. R2) between all loci. Phenotype and metadata are two additional input classes describing features of the individuals in the VCF that aid visualization and interpretation of haplotyping results. Phenotype supports any numerical description of individuals, such as yield or time to maturity. Metadata, which is optional input, supports any categorical description of individuals, such as subspecies or region collected.The crosshap is clustering of SNPs into representative ‘Marker Groups’ (MGs) using density-based spatial clustering of applications with noise (DBSCAN)  .run_haplotyping returns a haplotype object containing results for several user-provided epsilon values.Following the assignment of Marker Group allelic states for each individual, the frequency of each unique allelic combination is reported. Haplotype combinations with a minimum frequency of individuals (default minHap = 9) are assigned a unique label (A–Z) and kept for downstream analysis. Marker Groups that are invariant across the haplotype combinations are removed, and the remaining Marker Groups are provided with the prefix ‘MG’ and an identifying number (e.g. ‘MG1’). Individuals sharing MG allele combinations are assigned to a haplotype group . run_hapcrosshap provides a clustree  for DBSCAN clustering of variants, clustree  wrapper clustree . The clucrosshap visualization provides users with a comprehensive dashboard incorporating several plots to intuitively connect MG features with haplotype combination attributes (The tributes . This alcrosshap is anchored around a central matrix that depicts the MG allelic combinations defining each haplotype group (isolate_groups option.Local haplotype visualization with pe group , modellepe group . Across pe group , with adplot_left and plot_right switches, which can be used to instead display the relative genomic positions of SNPs within each MG across the region of interest, and a boxplot that represents the extent and sphericity of intra-MG linkage. Additional tabular summaries are provided to supplement the plots with haplotype information in the bottom-left (Attributes of SNPs in each MG are displayed horizontally in the left and right plots . The fretom-left  and SNP tom-left .crosshap, and build a UMAP GIF visualization to aid in conceptualizing the variation captured by local haplotyping at https://jacobimarsh.github.io/crosshap/.Detailed vignettes are provided alongside documentation to enable users to prepare inputs, learn the core functionality of crosshap is as a follow-up to GWAS or QTL mapping, to elucidate patterns of inheritance in a trait-associated interval of interest. Visualization with crosshap aids in characterizing genomic variation in the region and is valuable for uncovering novel features and relationships between haplotypes. Following this, clusters of SNPs defined in MGs and the haplotype assignments of individuals become useful for enabling downstream population genetics analysis between relevant groups.The primary use-case of crosshap represents a major advance in the accessibility of reproducible local haplotyping tools. With a focus on guiding parameter optimization and intuitive visualizations that connect genomic variation to individual-level traits, crosshap makes robust local haplotyping accessible to users with limited domain knowledge, equipping users with the tools necessary to develop a deep understanding of linkage patterns in a genomic region of interest.The development of crosshap.As trait mapping and association analysis moves towards the widespread use of unsupervised learning approaches with ‘black box’ issues of interpretability, it will be increasingly necessary to follow up on these results by effectively visualizing patterns of local genomic variation to translate the results to the user. Capturing complex linkage structures and local haplotype combinations is an essential component for understanding the inheritance of variants in a genomic region of interest, a task that is made more accessible with the development of btad518_Supplementary_DataClick here for additional data file.


In recent years, significant strides have been made in the field of genomics, with the commencement of large-scale studies aimed at collecting host mutational profiles and microbiome data. The amalgamation of host gene mutational profiles in both healthy and diseased subjects with microbial abundance data holds immense promise in providing insights into several crucial research questions, including the development and progression of diseases, as well as individual responses to therapeutic interventions. With the advent of sequencing methods such as 16s ribosomal RNA (rRNA) sequencing and whole genome sequencing, there is increasing evidence of interplay of human genetics and microbial communities. Quantitative trait loci associated with microbial abundance (mbQTLs), are genetic variants that influence the abundance of microbial populations within the host.Here, we introduce mbQTL, the first R package integrating 16S ribosomal RNA (rRNA) sequencing and single-nucleotide variation (SNV) and single-nucleotide polymorphism (SNP) data. We describe various statistical methods implemented for the identification of microbe–SNV pairs, relevant statistical measures, and plot functionality for interpretation.https://bioconductor.org/packages/mbQTL/.mbQTL is available on bioconductor at  With the advent of sequencing methods such as 16s ribosomal RNA (rRNA) sequencing and whole genome sequencing, there is increasing evidence of the interplay between human genetics and microbial communities  is that there exists co-abundant bacterial operational taxonomic units (OTUs) associated with particular genotypes. As an example, Bifidobacterium and Acinetobacterioum genera. Finally, our third approach (C) assumes particular SNVs are associated with the presence or absence of specific taxa. Implemented are three approaches for microbe–host interaction testing. Our first approach (A) is based on the hypothesis that a particular SNV is associated with increases or decreases of bacterial abundance. Assuming abundance of every taxa is similar to gene expression quantitatively, the abundance of a taxa can be associated with the presence of a SNV. As an example, The first approach (A) employs multiple linear regression to examine the relationship between bacterial abundance and host SNVs. The user can design their own model matrix for each microbe–SNV pair to control for phenotypes of interest .x, y, by the goodness of fit estimates (The second approach (B) is a correlation-based method, scaling the correlation (The correlation-based approach (approach B) allows us to demonstrate the relationships between SNVs and various microbes, as well as identify SNVs that are in linkage disequilibrium with each other and their expression patterns affect similar microbes as seen in In our final approach (approach C), we assume a binary relationship between each microbe and SNV pair and use a logistic regression model to determine the significant microbe–SNV relationships (User input includes three objects: (i) microbial abundance in the form of a MRexperiment object, (ii) a SNV matrix, which should be in the heterozygous format , and lastly (iii) a phenotype matrix to include covariates for multiple regression adjustment. For logistic regression, abundance thresholds are required.P-values for the linear model, in addition to FDR and Bonferroni P reporting. In approach B, as the method reports on the strength of the SNV-taxa and taxa-taxa relationships, we provide functions for flexible cutoff selection for the highest probability correlation estimates. This enables users to evaluate the method’s performance using various cutoffs, allowing to correct for the occurrence of false positives. The package is on the Bioconductor platform to allow extended use and maximum reproducibility of results from various labs that use this package in the research community.Main functions for linear and logistic regression analysis are linearTaxaSnp, histPvalueLm, qqPlotLm and logRegSnpsTaxa, logitPlotSnpTaxa. For the correlation based analysis the following functions are implemented, coringTaxa, allToAllProduct, taxaSnpCor, and mbQtlCorHeatmap for easy visualization of SNVs in linkage disqeuilibrium, e.g. chr15 in the package example dataset.mbQTL users can evaluate the performance of their analysis in approach A and C using the qqplot function, which compares expected versus observed Finally, the mbQTL package allows for the explanation of linear, correlative and binary investigations of microbe–SNVs estimation.We present mbQTL, an R package designed to provide a convenient method for microbe-SNV association analysis and visualization. Our approach incorporates three different models to identify significant microbe–SNV relationships: linear regression, correlation estimation (


The Avoidant Restrictive Food Intake Disorder Genes and Environment (ARFID-GEN) study is a study of genetic and environmental factors that contribute to risk for developing ARFID in children and adults.A total of 3,000 children and adults with ARFID from the United States will be included. Parents/guardians and their children with ARFID (ages 7 to 17) and adults with ARFID (ages 18+) will complete comprehensive online consent, parent verification of child assent (when applicable), and phenotyping. Enrolled participants with ARFID will submit a saliva sample for genotyping. A genome-wide association study of ARFID will be conducted.ARFID-GEN, a large-scale genetic study of ARFID, is designed to rapidly advance the study of the genetics of eating disorders. We will explicate the genetic architecture of ARFID relative to other eating disorders and to other psychiatric, neurodevelopmental, and metabolic disorders and traits. Our goal is for ARFID to deliver “actionable” findings that can be transformed into clinically meaningful insights.clinicaltrials.govNCT05605067ARFID-GEN is a registered clinical trial:  Avoidant Restrictive Food Intake Disorder Genes and Environment (ARFID-GEN) study, which is designed to expand the discovery of genetic and environmental contributions to ARFID risk. ARFID-GEN builds on previous and ongoing genome-wide association studies (GWAS) by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) as part of a global effort to fully characterize the genetic architecture of all eating disorders (EDs) and explore their relation to each other and to other psychiatric, neurodevelopmental, and metabolic/anthropometric traits.We describe the ARFID-GEN will ascertain, phenotype, and genotype a large sample of children and adults with ARFID. We will apply advanced analytic strategies to test and refine an etiological model of ARFID, explicate heterogeneity, and simultaneously document environmental risk factors for ARFID.ARFID is associated with high personal and family emotional and financial cost. ARFID, present in 2–5% of the population , is mark2twin] = .79; CI:.71, .86), with the remaining variance attributable to nonshared environmental factors  and is the most common reason for referral . When phLeveraging the existing Eating Disorders Genetics Initiative (EDGI) infrastrWe will interview parents/guardians of 25 children with ARFID and 25 adults with ARFID with the Pica, ARFID and Rumination Disorder Interview (PARDI)  to furthgs), polygenic risk scores (PRS), standard post-GWAS analyses of the non-mutually exclusive ARFID presentations, and rare copy number variants (CNVs) and CNV burden. Hypotheses: We will identify genome-wide significant loci for ARFID, informative rgs, implicated CNVs, and environmental precipitants.We will conduct comprehensive phenotypic and genomic analyses: single nucleotide polymorphism (SNP)-based heritability, GWAS , genetic correlations (rgs and conduct Mendelian Randomization (MR), multi-trait conditional and joint analysis (mtCOJO)  cross-disorder GWAS meta-analysis to identify loci with pleiotropic effects, (b) if indicated, calculate r(mtCOJO) , disorde(mtCOJO) , and, (c(mtCOJO)  to examiDeliverables: (a) dissection of converging and diverging relations among ARFID and other traits informing and refining its etiology; (b) genetic assessment of ARFID’s relation to other phenotypes, informing nosology.To test a conceptualization that ARFID has specific genetic associations with psychiatric, metabolic/anthropometric, and neurodevelopmental phenotypes we will apply: (a) approaches as in Aim 3b, and (b) GSEM to examine genetic, psychiatric, neurodevelopmental, and metabolic/anthropometric factors associated with ARFID. We predict that high sensory sensitivity will be related to autism spectrum disorder (ASD), high levels of phobic avoidance to anxiety and obsessive-compulsive disorder (OCD), and high levels of low interest/appetite to AN. We will engage the infrastructure utilized in EDGI  to ascerSupplement and Inclusion criteria. Individuals ages 7 + who meet DSM-5 criteria for ARFID. An age 7 + cut-off allows adequate phenotyping with a range of valid instruments available for relevant constructs for this age and older. We anticipate most cases will be pediatric as ARFID typically onsets in childhood. Following a brief online eligibility pre-screen, participants/parents/guardians will be screened for eligibility  and for diagnostic purposes using the questionnaires listed in We will use a multi-pronged recruitment approach including: outreach to ED clinicians and programs across the country, traditional media (press releases and newspaper announcements), and social media and other online platforms . This includes use of social media ads on Facebook and Instagram, which have proven successful for other similar studies . We enroSupplement.arfidgen.org) and select “Take Our Survey” with the child available. The first step requires the parent/guardian to answer prescreen questions and to consent to the study which includes answering questionnaires and having their child provide a saliva sample for DNA extraction. Parents also have the option to consent to be recontacted for future research. To determine eligibility of the child, parents/guardians then complete a parent report version of the Nine Item ARFID Screen (NIAS-PR) (Parents/guardians of children with ARFID who are interested in participating in the study will visit the website (NIAS-PR) ; a parenNIAS-PR) ; and VerNIAS-PR) , developNIAS-PR) . If the Children ages 7–13 then complete the ChEDE-Q8 , a childarfidgen.org) and click the “Take Our Survey” link. Participants complete a brief pre-screen, providing online informed consent for the entire study, have the option to consent to be recontacted for future studies, and provide contact information. Next, they complete the NIAS and the adult version of the PARDI-AR-Q to confirm the presence of ARFID, the EDE-Qv6 to rule out other current eating disorder symptoms, and the ED100Kv3. Surveys are presented in REDCap  Biorepository at UNC.Parents and adult participants are sent a gift card ($25) once all required questionnaires are complete and their spit kit is received by the study team.DNA extraction and GWAS genotyping are standard. We will use the most contemporary chip appropriate for diverse ancestry populations when genotyping occurs.We will conduct descriptive analyses to characterize the sample stratified by pediatric and adult cases. We will report demographics, symptom patterns, onset, course of illness, comorbid psychiatric conditions, neurodevelopmental characteristics, environmental exposures, and health-related quality of life in child (parent/guardian and child report) and adult ARFID cases.Diagnoses obtained with the PARDI interview will be compared with those obtained with the online ARFID-GEN battery. We will calculate positive predictive value (PPV) to confirm diagnostic properties of our ARFID-GEN battery. Data on weight, height, body mass index (BMI), and BMI percentile (for those < 18) will be verified during remote interviews.gs, and PRS, and standard post-GWAS analyses of ARFID and the non-mutually exclusive ARFID presentations, and rare CNVs and CNV burden. The following sections briefly summarize the Aim 2 analyses; a more detailed description can be found in the Supplement.We will conduct a comprehensive set of genomic analyses including SNP-based heritability, GWAS, ra)Supplement, we will use “Ricopili” software Supplement.The following outlines standard analytic strategy for post-GWAS analysis to maximize information yield and interpretability. The field and methodology evolve rapidly. Below represents what we would do today; however, novel proven methods may emerge before analyses are conducted. Greater detail can be found in the X chromosome (chrX) variants in the pseudo-autosomal regions will be handled separately. SNPs with MAF > 0.01 and INFO > 0.70 will be retained.We will conduct secondary GWAS analysis separately on females and males to determine similarity of the results to the primary combined GWAS.P< 5×10−8 will be identified and “clumping” will be used to convert significant SNPs to regions.GWAS results implicate genomic regions (“loci”). To define a locus, SNPs with Conditional and joint analysis will be conducted using genome-wide complex trait analysis-conditional and joint analysis (GCTA-COJO) . GCTA-COWe now routinely use functional genomic results from CommonMind, PsychENCODE, and other efforts to understand GWAS results . Much ofLinkage disequilibrium score regression (LDSC) will be used to estimate SNP-based heritabilities for ARFID and related presentations , 37.PRS aggregate risk alleles across the genome weighted by effect sizes. We will use both the classical p-value thresholding method and a summary statistics version of Bayesian multiple regression (SBayesR) .ARFID presentations are overlapping. We will examine the association between ARFID PRS and: a) PARDI-AR-Q subscale scores  as continuous scores; and b) a variable categorizing individuals as having a predominant ARFID presentation  defined as highest subscale score.Multi-marker analysis of genomic annotation (MAGMA)  will be h2) across functional annotation categories.Partitioned heritability will be investigated using stratified LDSC , which eWe will investigate whether ARFID heritability is enriched in tissue/cell type specifically expressed genes using publicly available gene expression data .We will predict differential gene expression using S-PrediXcan v1.0  and genoWe will evaluate whether genes associated with ARFID are enriched in specific pathways, tissues, or cell types. To do this, we will use FUMA to annotate SNPs, identify independent loci, perform pathway analysis, and integrate with a wide array of functional genomic data including gene expression, single cell gene expression, and all available brain epigenetic information.c)The PGC CNV working group is actively optimizing CNV calling using global screening array (GSA) data and we will follow these developments closely. We will maximize comparability between cases and controls by applying rigorous quality control (QC) –44. FollSupplement.Greater detail on Aim 3 analyses can be found in the We will conduct disorder-specific GWAS for ARFID  combining ARFID-GEN with EDGI and existing PGC-ED data using imputed variant dosages and an additive model. We anticipate aggregate sample sizes for the PGC-ED by ~ 2025 (when our collection would be complete) of: . GWAS meta-analysis will be conducted with ARFID-GEN and any other ARFID samples that may be introduced to the PGC-ED by time of analysis.Combining ARFID-GEN with PGC-ED GWAS of AN, BN, and BED , conduct GWAS meta-analysis of all EDs and of component behaviors that cross-cut disorders , increasing power to identify genetic risk factors that are common across the four disorders.b)rg) measure the extent to which two traits or disorders share common genetic variation. We will calculate SNP-rg for ARFID, and selected traits using GWAS summary statistics via an analytical extension of LDSC  and test the correlation in effect sizes in a second trait (the outcome). Under pleiotropy, there is an expectation that the mean effect size in the outcome trait is different from zero, but under causality a directional relationship in effect sizes is expected. Different versions of MR analysis are highly related; at the time of analysis, we will implement best practices for MR.Guided by the a) and b) will inform these analyses. For example, we expect to detect genetic correlations of BMI with EDs, and MR analyses will aid interpretation of correlation by causality. We will conduct conditional GWAS analyses to determine if the detected SNP associations for EDs can be explained through their relationship with correlated traits. We will perform a multi-trait-based conditional and joint analysis (GCTA-mtCOJO) (a above) provides insights into the forces shaping the shared genetic relationships between disorders.Results from -mtCOJO)  using an-mtCOJO) . This meA key question in ED research is to understand the differences as well as the similarities among the EDs. This question is very similar to the one posed by PGC colleagues interested in understanding the similarity/differences between SCZ and bipolar disorder (BIP) . Guided ARFID-GEN with EDGI and existing AN, BN, and BED cohorts in the PGC-ED samples should yield a sample size of ~ 95,000 ED cases and > 500,000 controls. We will increase power by using MPS to combine the predictive power of several PRS in one regression model. Training data will combine the best available GWAS summary statistics on psychiatric, metabolic, BMI, anthropometric, personality, physical activity, and educational phenotypes. These PRS will be used as genetic predictors in models of ARFID, AN, BN, BED, and more granular phenotypes. This approach is ideal for our overarching intentions of predicting outcomes rather than discovering their etiology . MPS is valuable when trait prediction is a priority.To gain insight into factors underlying ED heterogeneity, we will use the largest available GWAS summary statistics for psychiatric and somatic disorders/traits and combine derived PRS into MPS to predict target outcomes , and then more granular phenotypes . Combining c)We will use lifetime ARFID, AN, BN, and BED GWAS summary statistics. We will employ GSEM  to identgs, we will follow methods applied in the Anorexia Nervosa Genetics Initiative-PGC-ED (ANGI-PGC-ED) Freeze 2 analysis  How do genetic and environmental exposures act and co-act to influence risk for ARFID? 2) If detected, how will carriers of CNVs differ on PRS and environmental exposures? 3) Can we identify genetic factors that influence course of illness ? 4) Can we answer precision-medicine questions regarding identification of optimal interventions informed by genotype and environmental exposures? Given the paucity of effective interventions for ARFID (and other EDs) , ultimat