In plant sciences, curation and availability of interoperable phenotypic and genomic data is still in its infancy and represents an obstacle to rapid scientific discoveries in this field. To that end, supplementing the efforts being made to generate open access wheat genome, pan wheat genome and other bioinformatic resources, we present the GABI-WHEAT panel of elite European cultivars comprising 358 winter and 14 summer wheat varieties released between 1975 to 2007. The panel has been genotyped with SNP arrays of increasing density to investigate several important agronomic, quality and disease resistance traits. The robustness of investigated traits and interoperability of genomic and phenotypic data was assessed in the current publication with the aim to transform this panel into a public data resource for future genetic research in wheat. Consecutively, the phenotypic data was formatted to comply with FAIR principles and linked to online databases to substantiate panel origin information and quality. Thus, we were able to make a valuable resource available for plant science in a sustainable way.  Triticum aestivum L.) is witness to unprecedented developments owing to the availability of multi-omics data and advances in breeding informatics. These developments fueled the discoveries of marker-trait associations, gene cloning(s)1, targeted gene editing(s), and better understanding into genetic architecture of complex traits. However, the upcoming decade poses new challenges in the face of climate change, evolving consumer food preferences and sociopolitical scenarios between the wheat producing and importing countries. This implies that the conventional wheel of research output has to now turn even faster without compromising on quality and throughput. Current crop growth models driven by climate change scenarios already predict forthcoming changes in temperature, rainfall and spatiotemporal alterations in pathogen pressures across Europe, which if left unchecked, could lead to dwindling yields and massive crop loss2.The research landscape for wheat  augment the wheat research landscape in Europe for fundamental research topics, (2) hasten the translation of scientific learnings into elite variety development, and (3) promote further resource development and sharing.It is reasonable to expect that to support population pressure by 2050, crop production must rise and this would be possible given high throughput quality research. In line with developing public access resources to enable next generations of scientists spend less time on generating data and more time working with as well as building upon curated data, we publish herein the GABI-WHEAT panel including the original phenotypic dataHD), plant height (PH), thousand grain weight (TGW), ear weight (EW), grains per ear (GPE), yield (YIE), specific weight (SW)], six quality  and three disease  traits for GABI-WHEAT panel comprising 358 winter and 14 spring wheat varieties. For the field trials nine checks were added in >1 replications to round total number of genotypes per trial to 4004. Curation of phenotypic data for agronomic and quality traits was done from field experiments randomized according to alpha-lattice designs with two replications. These trials were conducted at up to 5 locations  in up to two years . Investigations of disease resistance traits were done on randomized complete block design with three replications per site at up to 4 locations  in up to two years . Each year and location combination were considered as one environment. The grain moisture content for measurement of traits was standardized to 14%.The phenotypic data corresponds to seven agronomic ), (2.) Name of reference database for the organism [Term Source REF], (3.) Access information of the organism in reference database [Term Accession Number], (4.) Variety evaluated (Characteristics[Variety Name]), (5.) Access information to the variety in the public database [Variety Database 1 (http://wheatpedigree.net/) and 2 (https://www.proplanta.de/)], (6.) Design effects , (7.) Mapping of study file rows to assay file (Sample Name), (8.) Coding used to resolve design effects and connecting to genodata  and other information like location coordinates. The assay file on the other hand records the traits phenotyped in given environment(s) (Harvest year + Location), specifically each row in the assay file connects via ‘Sample Name’, the relevant rows of study file to measurements for phenotypic/quality or disease traits in the assay file.The varieties analyzed herein originate from over 12 European countries wherein they were first registered in the period 1975 to 2007Phenotypic data for agronomic and disease traits were recorded across two seasons (in two years) at up to 5 locations in Germany or France. In Germany, respective locations i.e. Wohlde and Seligenstadt were available in both seasons, whereas in France for season one of the two locations was unavailable due to slug damage. So, to compensate for the loss of a location in season one, three locations  instead of two were used for phenotypic evaluation in season two. Phenotypic data for all 8 environments was available for HD, PH, TKW, YIE, PC and ZEL. The data for EW, STC as well as HAG was only available for German environments, whilst that for GH and SDS was only available for French environments. The data for the two remaining traits viz, SW and GPE was available only for few environments in both Germany and France Table .Table 1DPhenotypic data for disease traits was collected from separate inoculation trials at different locations in Germany albeit in the same two seasons. The phenotypic data for FHB was available for Ahlum and Cecilienkoog for season one and for Ahlum and Halle‐Bodenwerder for season two. The phenotypic data for STB was available only for Cecilienkoog for both seasons, whilst that of DTR was available only for season two at two locations viz. Ahlum and Lafferde Table . The curst of January, when approximately half of the ears per plot were fully visible i.e. at BBCH 59 from the Zadoks growth scale31.Total days from the 132.Average plant height per plot was measured before harvest, in centimeters, without awns33 and expressed in grams.For French environments; 500 grains were counted with a mechanical counter “Contador” and weighted. For German environments; grains in 10 g sample per plot were counted using the mechanical counter “Pfeuffer Contador”. Finally, all weight/grain values were extrapolated to 1000 grains34.Average of 10 ear sample weights per plot. Ear samples were taken before harvest and expressed in grams34.Average number of grains per ear from 10 ear samples per plot. Ear samples were taken before harvest34.Plot yield after combine harvest was extrapolated to an area of one hectare and expressed in quintal per hectare34.A 250-milliliter cylinder was filled up to the top with a clean grain sample from each harvested plot. The weight/volume value of the sample was extrapolated to 100 liters and expressed in kilogram/hectoliter7.A 400 g grains sample per harvested plot was analyzed using an OmegAnalyzer G (Bruins Instruments) with wavelengths of 730–1100 nm. Observations were recorded in percentages35.Eight grain samples per plot, were ground and mixed at rate 6.3 g per sample to 50 ml of distilled water taken in 100 ml graduated cylinder. After proper mixing and shaking, mean sedimentation values were recorded across the eight samples with a 0.5 ml precision. Values were adjusted according to the temperature of sedimentation liquid using AACC standardization tables36.A 250 g of representative seed sample per plot was ground, from which 7 g flour was added to a dry falling number tube and suspended by mixing with 25 ml distilled water at 22 ± 2 °C. Viscometer was then inserted and the combination was immediately (30–60 seconds of mixing) placed in water bath. The timer was started simultaneously. After the viscometer falls the standard threshold distance, the end time was recorded in seconds. Difference between start and end time was reported as falling number37.Four-gram grain sample was ground, sieved and 0.32 g of flour was taken in a 10 ml stoppered graduated cylinder. Five ml of bromophenol blue solution was added to the cylinder. After proper mixing, 5 ml of lactic acid reagent was added and mixing was done again. Cylinder was then put on a stand and sedimentation volume was recorded with a 0.01 ml precision. The obtained micro sedimentation values were transformed to macro sedimentation values using AACC standardization tablesF. graminearum and F. culmorum isolates, respectively, with water volume of 600 L/ha. Three inoculations were done at 10 days interval starting at BBCH 61. Incidence and severity were recorded in 3 assessments 20, 28 and 33 days after the first inoculation. Incidence was visually rated as percentage of infected spikes from 50 infected spikes per plot, whereas severity was visually rated as the percentage of infected area per spike of the infected spikes4. Low values indicate low infection and vice versa.Spray inoculations were done with 50,000 spores per ml using a 1:2 mixture of 6 spores per ml of pycnidiospores using a water volume of 600 L/ha. Two inoculations were done at 10 days interval starting from BBCH 39/41. To augment infection risk, Septoria infested grains were distributed on each plot at BBCH 31/32 at a density of 25 g/m2. Visual assessment of first and flag leaf was performed 32 and 48 days after inoculation38. Low values indicate low infection and vice versa.Spray inoculations were done with 5 × 102 of land. To augment infection risk, additional spring inoculation was done wherein fungus infested grains were distributed on each plot at BBCH 21–25 at a density of 25 g/m2. Visual assessment of first and flag leaf was performed at BBCH 65–69 (70 days after spring inoculation) and BBCH 83 (90 days after spring inoculation). In total, 10 flag and 10 first leaves were evaluated for each assessment and score for a genotype was calculated as the mean infected area for 10 samples for a given leaf and assessment39. Low values indicate low infection and vice versa.Inoculations were made with naturally infected straw, stubble and artificially infected grains . Before sowing in October, the natural inoculants were soil incorporated at density of 1 kg inoculant/mThe marker overlaps between the three arrays Table  are comp4 and shows no trend whatsoever across winter or spring wheat genotypes. For traits with complete and balanced data i.e. YIE, HD, PH, HAG and FHB  across environments for all traitsits Fig.  were norSeveral significant correlations were observed between BLUEs of traits Fig.  both wit34, and STC7 respectively. Entry mean based heritabilities were at par or in most cases higher than plot based heritabilities.The traits considered in the study can be clustered into three broad groups based on number of replications present per environment wherein they were phenotyped into 1. Those with 2 or more replications per environment , 2. Those with one replication per environment  and 3. Those with upto two replications at a given environment . Repeatabilities were evaluated for those environments which had at least two replications and the estimates thereof for respective traits suggests high quality of phenotypic data Fig. . The samThe three marker datasets reported herein were assessed for their fit to 1. BLUEs within environments and 2. BLUEs across environments by estimating their respective broad sense and narrow sense genomic repeatabilities. The estimates of broad sense genomic repeatabilities were consistently higher for a given combination of trait and environment compared to corresponding narrow sense heritabilities Figs.  to 18. T40.The varying marker densities used to predict respective traits herein reveal counter-intuitive results wherein, markers derived from 35k and 90k chip perform at par Fig. . The pre


Accurate estimate of relatedness is important for genetic data analyses, such as heritability estimation and association mapping based on data collected from genome-wide association studies. Inaccurate relatedness estimates may lead to biased heritability estimations and spurious associations. Individual-level genotype data are often used to estimate kinship coefficient between individuals. The commonly used sample correlation-based genomic relationship matrix (scGRM) method estimates kinship coefficient by calculating the average sample correlation coefficient among all single nucleotide polymorphisms (SNPs), where the observed allele frequencies are used to calculate both the expectations and variances of genotypes. Although this method is widely used, a substantial proportion of estimated kinship coefficients are negative, which are difficult to interpret. In this paper, through mathematical derivation, we show that there indeed exists bias in the estimated kinship coefficient using the scGRM method when the observed allele frequencies are regarded as true frequencies. This leads to negative bias for the average estimate of kinship among all individuals, which explains the estimated negative kinship coefficients. Based on this observation, we propose an unbiased estimation method, UKin, which can reduce kinship estimation bias. We justify our improved method with rigorous mathematical proof. We have conducted simulations as well as two real data analyses to compare UKin with scGRM and three other kinship estimating methods: rGRM, tsGRM, and KING. Our results demonstrate that both bias and root mean square error in kinship coefficient estimation could be reduced by using UKin. We further investigated the performance of UKin, KING, and three GRM-based methods in calculating the SNP-based heritability, and show that UKin can improve estimation accuracy for heritability regardless of the scale of SNP panel. Accurate estimation of relatedness among individuals is important in genetic data analysis. For example, in both population-based and family-based genome-wide association studies (GWAS) with uncertain relationships among study subjects, it is critical to appropriately account for cryptic relatedness because incorrect estimates can decrease power and inflate false positive rates of association tests –3. It haIn order to adjust for cryptic relatedness in genetic studies like association mapping and heritability estimation, individual-level genotype data are often used to estimate pairwise kinship coefficients, which is defined as the probability that two homologous alleles drawn from each of two individuals are identical by descent (IBD). The methods can be mainly divided into likelihood estimators and method-of-moments estimators. The likelihood methods are preferable to identify potential relationships , 10, 11.We note that most association mapping and heritability estimation packages use this method as their default setting for calculating GRM, such as GCTA, GEMMA and FaSTLMM , 8, 20. In this paper, through mathematical derivation, we first show that there indeed exists bias in the estimated kinship coefficients using the scGRM method. The bias exists because the observed allele frequencies are regarded as true frequencies. We also prove analytically that the bias essentially results in a negative average for all estimates, which explains the large proportion of negative values. Based on this observation, we propose an improved kinship estimation method, UKin, which can remove bias. We provide a mathematical proof for the unbiasedness of the UKin estimator. Simulations and real data analyses also demonstrate that both bias and standard deviation (SD) can be reduced by replacing the scGRM method with our UKin method. In real data analyses, we apply our method to two studies, young-onset breast cancer (BC) and familial intracranial aneurysm (FIA), which have pedigree information to evaluate our results. For further comparison, we also include another widely used relationship inference method: KING , and twoTo further demonstrate the practicability of UKin, we conducted experiments in estimating trait heritability based on real genotype data collected from the young-onset breast cancer (BC) and familial intracranial aneurysm (FIA) studies. Results from these analyses suggest that compared with KING, scGRM, rGRM and tsGRM, UKin achieved more accurate estimation in trait heritability and its performance was also stable with respect to the size of SNP panel.We prove that the bias exists in the estimation of kinship coefficients using the scGRM method.We analytically show that the bias essentially results in a negative average, explaining the large proportion of negative values in the estimates.We propose an unbiased method for kinship estimation, UKin. We further prove the unbiasedness of the UKin estimator.Simulations and real data applications demonstrate that UKin leads to more accurate estimates compared with the state-of-the-art methods.The paper is organized as follows. In the “We summarize the contribution of our method as follows: a and b, their kinship coefficient, a and b share zero, one and two alleles IBD, respectively. The definition of kinship coefficient indicates that Alleles are said to be identical by descent (IBD) if they are inherited from a common ancestor. To describe the average amount of IBD sharing at the genome level, we often adopt the concept of kinship coefficient . For twomentclass2pt{minimers of 2  for varin individuals, for each person we consider his/her genotypes at m SNP markers respectively. For A) for individual i at SNP marker j. Thus i has, respectively, 0,1, or 2 copies of allele A at marker j.Suppose we have genotype data of i and To simplify the illustration, we denote Property 1. Assume all individuals are sampled from a homogeneous population, then for ple, see . A proofi and Result i. implies that the correlation between A) at SNP j in the whole population. We call Estimating kinship coefficient by calculating the average sample pairwise correlation among all genetic variants has been taken by many methods. Following this principle, a natural estimator of However, as we are going to demonstrate, Property 2. For mentclass2pt{minimEquation  also revmentclass2pt{minimProperty 3. For every SNP marker j, where mentclass2pt{minimRecall that Several GRM estimators have been proposed based on the scGRM method. The robust GRM (rGRM) estimator replaces the equal weights in scGRM with varied weights proportional to timators . Both rGmentclass2pt{minimi and This bias problem makes the scGRM estimator Property 4. For every SNP marker i and mentclass2pt{minimFor ease of presentation, we setj2.Using , we alsoIt is worth noting that there exists some relationship between the scGRM and UKin estimator. Substituting the expression of i′j into , we get5Throughout our above analysis, we make assumptions of no inbreeding, LE and population homogeneity. In the Discussion we analyzed these assumptions in detail.n is the sample size and We start our discussion with a simple but extreme example. In this experiment, we assumed that there were 500 full siblings from the same family. Although unlikely to exist in reality, this example serves as a good illustration of our theoretical derivation. As every two individuals selected from the same family were full siblings, the true value of their kinship coefficient should be 0.25  of genotyped variants were drawn uniformly from .We compare UKin with scGRM, KING, and two other GRM estimators: rGRM and tsGRM. With each estimator, we estimated kinship coefficients between all simulated individual pairs and divided those coefficients into four groups according to their true relationships. Figure Although Fig. We also conducted a small-panel simulation including 4000 subjects and 10,000 SNPs to evaluate the performance of UKin, scGRM and KING when the number of genotyped SNPs is relatively small. The population structure was the same as the previous simulation. Summarized in Table A1 in Additional file dbGaP Study Accession: phs000678.v1.p1). This study recruited families from the US and Puerto Rico with a daughter who was recently diagnosed with breast cancer and another unaffected daughter. For each family, only the diseased daughter and her unaffected full sister were genotyped for analysis. As for data quality control, we removed individuals with more than 10% missing genotypes as well as SNPs with a missing genotype rate greater than 5% or a minor allele frequency less than 5%. After further removing individuals with missing phenotypes, we got 1983 subjects (1458 cases and 525 controls) with 925,685 variants in total. The processed data included 500 pairs of full sisters, with one affected by breast cancer. Based on Table To demonstrate UKin could get more accurate results in estimating kinship coefficients in real applications, we applied it to a family-based study of genes and environment in young-onset BC . This study recruited 400 families with multiple individuals who have an intracranial aneurysm (IA) through 23 (25) referral centers throughout North America, Australia, and New Zealand that represent 35 (40) recruitment sites. After standard quality control and discarding subjects with missing phenotypes, we obtained 990 individuals from 371 families and each of them was genotyped at 5505 SNPs. In this FIA dataset, the confirmed relationships include 137 first-degree relative pairs (including 19 full siblings and 118 parent-child pairs).To further investigate the effectiveness of the UKin method in kinship coefficient estimation, we applied UKin to infer pedigree structure using genotype data from the FIA linkage study , we use GCTA-reml to estimate the heritability and its standard error via one of the three kinship estimation methods. For each true heritability value, we repeated this experiment for 1000 times.We calcualted the average bias and SD of estimated heritability for each true value, with the results summarized in Table For scGRM and rGRM, large bias and small SD of estimated heritability were likely to result in low coverage of the confidence interval (CI). To demonstrate this, we constructed 95% normal CIs with estimated heritability and corresponding standard error for each simulation and calculated the proportion that CIs intervals covered the true value. We display the coverage rate for each group and method in Fig. To further compare the effectiveness of UKin in heritability estimation with the other four methods when the number of SNPs is limited, we also conducted similar experiments based on the familial IA linkage study. For the FIA dataset, we have 5505 SNPs and 990 individuals after the QC procedure. We also set the true heritability to be 0.3, 0.4, 0.5, 0.6, or 0.7 and estimated the heritability under UKin, KING, and three GRM methods, respectively.Biases of heritability estimates and SDs from 1000 simulations are shown in Table Similar to the above experiment based on BC, we also constructed 95% normal CIs with estimated heritability and standard error for each simulation and displayed the coverage rate for each method in Fig. Among many kinship estimation methods, the most commonly applied estimator uses dense SNP genotypes and allele frequencies in samples to calculate average pairwise correlation coefficients among SNPs. Although this method is intuitive and easy to calculate, we have shown in this manuscript that it is actually biased because it treats the observed allele frequencies as true frequencies. Through rigorous derivation, we showed that pairwise kinship coefficients estimated by scGRM add up to be a negative value, which explains the phenomenon that a substantial proportion of kinship coefficient estimates are negative.When conducting large scale estimates of kinship coefficients, the existing bias in scGRM can lead to incorrect inference of relationships, and this problem can be more severe if the subjects in the dataset are closely related. Our method, UKin, solved this issue by incorporating genetic information from the whole population to adjust for the bias in estimated kinship coefficient between every single pair. This unbiased estimator can be expressed as a polynomial of scGRM estimators, and leveraging only information of dense genotypes from the population. As demonstrated by our simulations and applications to the BC and FIA family data, UKin performed better in reducing both estimation biases and SDs. For the two sister study, the results suggest that while scGRM could lead to severe spurious inference of relative pairs, UKin rarely made such mistakes. Even when the number of genotyped SNPs was limited for the FIA study, UKin could reduce statistical bias and SD while avoiding spurious relationship inference.In our theoretical derivations and simulation studies, we made assumptions like linkage equilibrium (LE) and absence of inbreeding, that is, genotypes at different markers are independent. During our derivation, we used the same weights for all SNPs, and our simulated datasets were also generated under this assumption. Although there is linkage disequilibrium (LD) in real data, empirical results from analyses of the BC and FIA family data suggest that UKin could reduce bias in the presence of linkage disequilibrium. To incorporate LD in practice, we can give different weights based on LD among different SNPs. Following the approach of Wang , these LAnother assumption throughout our study is a homogeneous population so that the allele frequencies can be calculated once and applied to all subjects. Some methods have been proposed to estimate kinship coefficients in admixed populations, where the assumption of population homogeneity is untenable , 19, 26.Similar to scGRM and rGRM, UKin also has a quadratic time complexity. When dealing with real data, our python script of UKin took 6 seconds to estimate the kinship matrix for the FIA study, while it took 46.4 minutes to finish the calculation with a single CPU core for the large BC study. To improve the computational speed of UKin, we applied parallel execution to our original python code. When multiprocessing UKin with 12 CPU cores, the required computational time for kinship estimation of BC study was reduced to 11.2 minutes. It is also worth mentioning that although the tsGRM estimator shows the best performance in kinship and heritability estimation among all three GRM methods, it requires much longer running time because tsGRM needs to make optimization based on a pre-calculated kinship matrix. When calculating the kinship matrix for the BC dataset with a single CPU core, tsGRM spent about 46 hours to complete, which is far from satisfying. Implementing our UKin method using GPU will further improve the computational efficiency of the method. In this paper, we mainly focus on the concept of correcting bias of kinship estimation method. We will consider accelerating the algorithm in our future work.Beside to overcome the current limitations of our method mentioned above, further studies need to be conducted for demonstrating how more accurate kinship estimation will benefit downstream analyses based on genetic data.Results show that UKin achieved more stable and accurate estimation of heritability compared with other GRM methods and KING. In addition, more accurate kinship estimation will improve the performance of other genetic analyses such as association mapping. In recent years, GWAS have seen great success in identifying genetic loci contributing to complex human traits , 28. By To demonstrate whether the change of kinship matrix affects the performance of association mapping, we conducted a simulation study to compare the performance of UKin, scGRM, and KING in GWAS. In our experiments, we simulated 4000 samples including 2000 cases and 2000 controls. We included subjects with various pairwise kinship coefficients in both cases and controls. More specifically, we simulated 250 first-degree relative pairs, 250 2nd-degree relative pairs, 250 3rd-degree relative pairs, and 500 unrelated subjects for both cases and controls. The total number of SNPs genotyped for each individual was set to be 10,000 and the MAFs of non-risk SNPs were drawn uniformly from We applied GEMMA , which wWe applied GEMMA to analyze the simulated GWAS dataset and selected all SNPs with P-value below the threshold In this article, we have proved that there exist biases in different scGRM-based methods for estimating kinship relationship among individuals from genetic data. Beside genetic data, we may identify relationship or similarity among individuals using other kinds of data in real life. For example, we may want to infer the kinship among individuals based on human facial images, which is known as facial kinship verification problem in computer vision . Or we mAdditional file 1. Technical details such as mathematical derivations and the results of simulations with 10,000 SNPs.


Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery. The vasination) . There iination)  or diseaination) , among oination) .There is an absence of a benchmark dataset to assess the performance of post-GWAS analysis tools. Therefore, we propose a systematic and objective comparison of the results obtained by different tools when applied to the same datasets. We designed a fine-mapping and colocalization workflow with different tools running alternatively. Fine-mapping analysis identifies the causal GVs and is a necessary step in most post-GWAS analyses. We used the tools Probabilistic Identification of Causal SNPs (PICS)  and TORUThe results of the workflow reveal divergence across tools, pinpointing a relevant issue in post-GWAS analysis derived from the lack of method benchmarking. Our findings demonstrate how critical is the fine-mapping step to subsequent analysis and how colocalization outcomes are in turn highly impacted by the assumptions of each tool. As a consequence, the causal GVs and eGenes identified are different and are involved in different biological processes. Overall, given the lack of agreement among tools, we highlight the need for an objective and unbiased assessment of post-GWAS analysis methods and tools to properly leverage GWAS data.Among the plethora of available methods for post-GWAS analysis and which have been reviewed elsewhere , we focuThe workflow we describe in this manuscript applies alternative tools for post-GWAS analysis to compare their outcomes . We begihttp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST005001-GCST006000/GCST005902/harmonised/29662059-GCST005902-EFO_0003761.h.tsv.gz].We have selected the latest genome-wide association study (GWAS) on major depression (MD) with publicly available full-genome summary statistics (GCST005902) . This GWhttps://www.gtexportal.org/home/datasets] (The Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) dataset contains single-tissue cis-eQTL data with eGene, meaning genes regulated by eQTL, and significant variant-gene associations for 49 tissues [downloaded from: atasets] .http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/]. We computed the z-scores by dividing GVs’ effect size, understood as the effect of the risk allele relative to the reference allele, over the standard error  . The parrd error . We thenBefore applying the Probabilistic Identification of Causal SNPs (PICS) and TORUS fine-mapping tools, we matched GWAS GVs and eQTLs to their corresponding LD blocks using the European 1,000 Genomes LD reference panel .https://pics2.ucsf.edu/pics2-LD.html), which performs LD expansion and fine-mapping. In brief, PICS takes the most significant GV per association locus along with its associated p-value, performs LD expansion and then computes the probabilities by performing empirical permutations per GV. For GWAS, we submitted the data and obtained the computed PICS probabilities for the input GVs and those in LD, from now on linked GVs. As for eQTLs, we downloaded precomputed LD-based PICS for all GTEx best eQTLs per gene per tissue type [downloaded from: https://pics2.ucsf.edu/Downloads/GTEx/].We run PICS by programmatically accessing its web application form. We used LD-based PICS  [downloaded from: The major histocompatibility complex region  was excluded from all datasets for the analysis due to the complex LD structure of GVs, which may lead to inaccurate results .For the colocalization analysis, we implemented two different approaches in the workflow: the Colocalization Posterior Probability (CLPP) approach and the Fast Enrichment Estimation Aided Colocalization Analysis (fastENLOC). We applied these two methods to the fine-mapping results obtained with both PICS and TORUS to identify the genes regulated by causal GVs, also known as eGenes. Both tools consider that there can be more than one causal GV per association locus. CLPP assumes independence between GWAS and eQTL data while fastENLOC does not and computes the enrichment of GWAS on eQTL data using an embedded function. In addition, fastENLOC not only computes SNP colocalization probabilities (SCP) but also regional colocalization probabilities (RCP) to overcome the inability to narrow down to a single causal SNP, common to all tools. Please note that the tools were run following the guidelines and parameters recommended by the authors. We conducted a CLPP approach by computing the product of PICS probabilities for GWAS and eQTL overlapping linked GVs. Based on previous experience in post-GWAS data analysis, we narrowed down the results to the most likely causal GVs  by filtevia SNPnexus  and their biological implications. We examined the results individually and then compared them across tools, with classic approaches (pGenes) and with the results reported in the original publication.This study evaluates and compares the outcomes of different fine-mapping and colocalization tools . To accoThe original genome-wide association study (GWAS) consisted of 7,624,931 harmonised genetic variants (GVs) and after imputation to predict missing Genotype-Tissue expression (GTEx) eQTLs, we obtained 7,947,219 GVs . The estimated imputation quality provided by SSimp was generally good for all chromosomes (r2. pred >0.8) except for chromosome 17.We run linkage disequilibrium (LD)-based PICS by inputting the most significant GWAS GVs per LD block  along with their p-values . After PWe compared PICS and TORUS initial fine-mapping results  and thenThe analysis of PICS and TORUS most likely causal GVs (probability >10%) revealed that both sets are enriched in GVs associated with MD, bipolar disorder and other psychiatric disorders . PICS caAdditionally, we applied classic gene-mapping approaches to PICS and TORUS fine-mapping results, yielding 1,277 and 1,248 proximal genes or pGenes, respectively. Both sets were enriched in genes associated with neurogenesis as well as neuron differentiation and development .The colocalization results from CLPP approach using PICS fine-mapping results yielded 44 GVs and 43 genes regulated by eQTLS, also known as eGenes, affecting 28 tissues , whereasWhen comparing methods, the use of different colocalization tools after fine-mapping with PICS yields the most similar results. When using PICS, all tissues and eGenes identified by fastENLOC are also obtained by CLPP, with differences found at the GV level, and CLPP retrieving additional eGenes compared to fastENLOC . MeanwhiWe compared eGenes from fine-mapping and colocalization workflow to pGenes from PICS and TORUS fine-mapping results. Only 3 genes overlapped between pGenes from PICS fine-mapping and PICS+CLPP eGenes  and one with PICS+fastENLOC (KTN1). There was no overlap with pGenes when comparing to TORUS. The eGenes from PICS+CLPP are enriched in their association with miRNAs and the eGenes from PICS+fastENLOC in RNA Polymerase I Promoter Escape . ConsideWhen we applied traditional gene mapping approaches to the GVs that were found to regulate the expression of those eGenes, we discovered a total of 74 pGenes. The vast majority of eGenes identified do not match pGenes, which holds true across all workflows . In addiFurthermore, we compared the results obtained with the original publication where 14 GVs and 7 pGenes were reported. The latter are functionally associated with synapsis  and 6 ofCurrently, there are a plethora of strategies available for post-GWAS analysis . Here, wOur main premise throughout this analysis has been to use each tool as it was intended by following developers’ recommendations and guidelines as closely as possible. This way we could get the most out of them and compare their optimised outcomes. Furthermore, one of the primary reasons behind the tools’ selection was their ability to work with full-genome summary statistics instead of individual genotype data, which can be difficult to obtain due to privacy concerns. Other criteria for tool selection included the quality of documentation, the maturity of the software, and the availability of developer support.Prior to post-GWAS analysis, the imputation process using SSimp yielded very good quality results except for chromosome 17. One possible explanation is that SSimp provides hg19 1000Genomes phase3 as the reference panel. This version of the genome has some gaps, most of which are found in telomeres and centromeres, having a strong impact on chromosome 17 . We thenFine-mapping is significantly influenced by LD patterns and the used tools, PICS and TORUS, which work under different assumptions (see Methods). Therefore, to have comparable results we considered the probabilities obtained at the LD block level, because the most likely causal GVs may differ or not be discernible due to high LD between GVs. In addition, to account for the difference in the number of GVs which could be driving the observed inverse distribution of probabilities between tools , only thCLPP and fastENLOC colocalization approaches were applied to both fine-mapping results from PICS and TORUS. Following the same logic, given that TORUS computed lower probability values, PICS yielded more colocalization findings . FurtherThe overlap between eGenes and pGenes from PICS and TORUS fine-mapping was very small, with 3 genes in total. Among them, KTN1 has also been associated with MD  and ESYTFine-mapping and colocalization analysis successfully identified eGenes associated with mental disorders  that difBrain regions are of particular interest in MD and as such, we focused the evaluation of our results on them. The brain frontal cortex, hypothalamus, pituitary and brain cerebellar hemisphere have common findings between PICS and both colocalization tools. MD and myclonus-dystonia are usually comorbid, and their association has typically been studied in relation to SGCE mutation and its potential pleiotropic effect . HoweverIn general, both classic gene mapping approaches and colocalization analysis identified genes associated with MD or associated relevant processes. Colocalization analysis can provide insights about the effect of GVs located in non-coding regions of the genome, pinpointing the genes they regulate and the relevant tissues. As it has previously been reported the closest gene may not always be the causal one . These rOur goal was to illustrate the impact of the lack of standards on the selection of the most adequate post-GWAS analysis method using a fine-mapping and colocalization workflow that compared different tools. The results revealed a high divergence between fine-mapping methods due to their assumptions, which in turn highly impacted the next steps. TORUS one causal variant assumption may tip the balance in favour of PICS considering fine-mapping and posterior analytical steps. Colocalization results seem to diverge in the amount of GVs and eGenes identified, with fastENLOC being more stringent by considering the enrichment of GWAS on eQTLs. All in all, despite the potential of combining GWAS data with molecular profiling datasets to guide in the interpretation of the functional impact of GVs located in non-coding regions of the genome, the results of our analysis revealed shortcomings related with the analytic tools. We propose that objective evaluation and benchmarking of post-GWAS analysis tools is required in order to fully leverage GWAS data for precision medicine and drug R&D applications.


Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set.Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups , 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30–50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.The online version contains supplementary material available at 10.1007/s00122-022-04180-2. Oryza sativa) in their daily diet. To secure nutritional requirements of a growing human population, the improvement of grain yield, both in quantity and in nutritional quality, is imperative. This is a significant challenge in the face of climate change and limited cultivable land. Current pace of rice genetic improvement may be too slow to meet these demands  is typically far larger than the number of observations n. In this setting, either variables must be selected or restrictions on the solutions must be imposed, or a combination of both. Methods such as LASSO  is a “large In general, there is no consensus on which GP method is best. A recent review by Reinoso-Peláez et al.  points aIrrespective of the algorithm chosen, single nucleotide polymorphisms (SNPs) are the main class of markers used so far in GP due to their genome-wide abundance and genotyping automatization. SNPs are not, however, the only source of phenotypic variability in the genome. In the last few years, data have accumulated on the importance of presence–absence variation and structural variation as a source of phenotypic variability in plants, including in rice , Indica , Japonica , Aromatic . The accessions that cannot be assigned to a specific rice group are categorized as Admixed . We used the SNP-based group assignment from Sun et al. (The 738 accessions used in this study (Supplementary Table 1) are from the collection conserved at IRRI used for the 3000-rice genome project Jackson  and weren et al.  to identhttps://snp-seek.irri.org/), we chose the 11 traits for which data were available in the 738 accessions selected. Some discrete traits were binned to balance the number of observations per class and time to flowering was log-transformed. Supplementary Table 2 shows basic statistics and transformations applied. Principal component analysis (PCA) for the 11 phenotypes was obtained with the “prcomp” function available in R.4.1.0  are divided in two main classes “copy and paste” (Class I TEs) or “cut and paste” Class II TEs. In rice, the most abundant Class I elements are RLX (LTR retrotransposons) and RIX (Non-LTR retrotransposons), whereas DTX  and MITEs (Miniature Inverted-repeat Transposable Elements) are the most prevalent  and prediction of individuals from different groups.N = 48) using the rest of accessions, including non-improved Indica accessions. Accessions from IRRI core collection are classified as “improved,” “breeding and inbred lines,” and “traditional” varieties. We used this passport information to identify this subset of improved varieties. “Improved” Indica varieties correspond to most modern and commercial lines available at IRRI collection. In this scenario, performance to be predicted is from highly related accessions to those in the training set.In the first scenario, we measured accuracy when predicting performance of improved Indica varieties (N = 29) and Aromatic  accessions using the rest of groups. In this case, performance to be predicted is from accessions that may not be too related to accessions in the training set, and we expect prediction to be worse than in the former scenario. For instance, the ADM group is a small, highly heterogeneous collection of accessions.In the second scenario, we predicted performance of all Admixed . Following Pérez and de Los Campos (E(π) = π0; on the other hand, p0 can be interpreted as the number of prior counts (prior ‘successes’ plus prior ‘failures’).” Here we chose π0 = 0.01.In Bayes C, a probability π of presence/absence of a SNP in the model is sampled from s Campos  and S1, In a subset of cases, we evaluated whether using only genic SNPs improved prediction compared to using all available markers. Similarly, we conjectured that not all transposable elements are equally likely to cause phenotypic changes. We analyzed predictive performance of models containing TIPs from each of the largest 18 MITE families present in the rice genome (Supplementary Table 3). To avoid repetitive, lengthy results we make the additional analysis using two agronomic traits of high importance on rice breeding, time to flowering, and grain length. An earlier or later growing can determine seed production. Grain size-related traits such as grain length/width are important breeding targets since they affect the quality of the crop yield. These two traits may also represent alternative genetic architecture .Figure N = 451). Note model  analyses. This indicates that adding additional markers may contribute to overfitting and reduce model performance in prediction. Overfitting is a well-known phenomenon in the machine learning literature when the model is not properly regularized. This has been clearly observed with simulated data in a genomic prediction scenario  of entering the model for each marker type . This occurred despite setting equal priors for all types of markers (p = 0.01). This was not due only to the priors or different number of TIPs compared to SNPs, because the pattern differed between traits.Next, we wished to study how the different genetic architectures influence the statistical behavior of the three sets of markers. Bayes C is a variable selection method and so we reasoned that the number of markers entering the model and their effects would differ between traits. Broadly, estimates of marker effects were quite similar across traits (for the same type of marker) as can be seen in Supplementary Fig. 3. The only exception was grain width and grain length, where we observed much larger estimated effects for MITE/DTX and SNPs, respectively, in agreement with results in Fig. Using a subset of all markers available can improve prediction. For instance, the accuracy of a model which contains only the causative SNPs can approach one  and LINEs (RIX), whereas DNA transposons (DTX) and MITEs are the main components of rice class II TEs  were treated as continuous. It has been known for decades that a threshold (logistic model) is theoretically a better choice for binary traits than standard linear models Below is the link to the electronic supplementary material.


The investigation of carcass traits to produce meat with high efficiency has been in focus on Japanese Black cattle since 1972. To implement a successful breeding program in carcass production, a comprehensive understanding of genetic characteristics and relationships between the traits is of paramount importance. In this study, genomic heritability and genomic correlation between carcass traits, including carcass weight (CW), rib eye area (REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate (YI), and beef marbling score (BMS) were estimated using the genomic data of 9,850 Japanese Black cattle . In addition, we investigated the effect of genetic relatedness degree on the estimation of genetic parameters of carcass traits in sub-populations created based on different GRM-cutoff values. Genome-based restricted maximum likelihood (GREML) analysis was applied to estimate genetic parameters. Using all animal data, the heritability values for carcass traits were estimated as moderate to relatively high magnitude, ranging from 0.338 to 0.509 with standard errors, ranging from 0.014 to 0.015. The genetic correlations were obtained low and negative between SFT and REA [−0.198 (0.034)] and between SFT and BMS [−0.096 (0.033)] traits, and high and negative between SFT and YI [−0.634 (0.022)]. REA trait was genetically highly correlated with YI and BMS . In sub-populations created based on the genetic-relatedness ceiling, the heritability estimates ranged from 0.212 (0.131) to 0.647 (0.066). At the genetic-relatedness ceiling of 0.15, the correlation values between most traits with low genomic correlation were overestimated while the correlations between the traits with relatively moderate to high correlations, ranging from 0.380 to 0.811, were underestimated. The values were steady at the ceilings of 0.30–0.95  for most of the highly correlated traits. The results demonstrated that there is considerable genetic variation and also favorable genomic correlations between carcass traits. Therefore, the genetic improvement for the traits can be simultaneously attained through genomic selection. In addition, we observed that depending on the degree of relationship between individuals and sample size, the genomic heritability and correlation estimates for carcass traits may be different. Japanese Black cattle , due to its unique characteristic, is one of the most important and well-known native cattle in Japan. The excellent quality of its meat has been noticed worldwide. This breed has been having significant genetic improvements in carcass traits since 1972 by applying a two-stage selection . VariousBefore undertaking a successful breeding program in carcass production, it is necessary to understand the genetic characteristics of carcass traits including the genetic correlation between these traits. Recent genetic studies have been estimating genetic parameters  for carcass traits , and thiHeritability indicates a proportion of phenotypic diversity in the population that has a genetic origin and can be inherited to the next-generation. High heritability values are an indicator of a population’s ability to respond quickly to selection . Among dThis study aimed to estimate genomic heritability, correlation, and variance components for carcass-related traits using 9,850 Japanese beef cattle genotyped for genome-wide SNPs. In addition, to highlight the significance and effect of the relatedness degree in the calculation of genetic parameters in animal traits, and also following 2), rib thickness , subcutaneous fat thickness , yield rate  and beef marbling score (BMS). All of the animals were genotyped using BovineLD Genotyping BeadChip  and then the genotypes were imputed to the higher density of the Illumina BovineSNP50 BeadChips using 1,223 sires and 4 dams as a reference and Beagle v4.0 software . The set of data analyzed here is fully explained by software . Data prsoftware  by filteWe used bivariate linear mixed models  to evaluX and Z are incidence matrices related to effects b and u, respectively.The animal model of two traits or bivariate is written as:I is the identity matrix, G is a genomic relationship matrix (GRM) provided from a set of SNPs. The genetic relationship matrix calculates and scores a value for each pair of individuals in a data set using the estimates of genomic sharing and comparing two genomes. GRM was provided by https://doi.org/10.5061/dryad.tdz08kpz4). We prepared GRM binary file using R v4.1.3 software for the subsequent analysis (The (co)variance matrix of random effects is of the form:analysis . In addiThe genomic-based restricted maximum-likelihood (GREML) approach implemented in the Genetic Complex Trait Analysis (GCTA) software was used to estimate the genetic parameters. In the current study, we created sub-populations by different genetic-relatedness ceilings. The ceilings are specified using GCTA v1.94.0 software (GRM-cutoff option) . It maing is a vector of the individual genetic effects distributed as A) matrix in the model. Then, phenotypic variance can be written in the following equation:I is an identity matrix, and GREML approach used for estimating heritability detects the proportion of the differences in phenotypes that is due to genetics, by modeling random effects. For example, a phenotype can be defined as follows:There are positive or negative correlations between many economically important traits, which can cause undesired changes in the target traits. Therefore, the estimation of the correlation between traits is of considerable importance.The genetic correlation parameter was calculated as Phenotypic correlation was computed using The phenotypic values associated with carcass traits are presented entirely by According to In this study, the heritability values for carcass traits were observed moderate to relatively high. Heritability for CW, REA, SFT, RT, YI, and BMS was estimated at 0.509, 0.434, 0.473, 0.338, 0.446 and, 0.486, respectively. Due to the presence of considerable genetic variation for these traits, an effective genetic improvement can be achieved through the genomic selection of superior animals. The estimated heritability for CW agreed closely with around 0.50 reported by In breeding programs and predicting response to selection, the accurate estimation of heritability and other genetic parameters is of particular importance . The parThe heritability estimate for carcass traits has also been compared with that of other published estimates of beef cattle. Several studies have estimated the genetic parameters of carcass traits using pedigree-based information in different beef cattle which are comparable with our results. We obtained the heritability values for carcass weight higher than those reported in Hanwoo , Angus , SimmentIn the present study, the standard errors of the heritability estimates for carcass traits, ranging from 0.014 to 0.015,   were lowThe calculated values of genetic and phenotypic correlations between carcass traits are given in When comparing the results with the pedigree-based studies, we found that the estimated genomic correlation between CW and REA (0.46) was similar to the value reported in the reviewed study by In a study based on genomic data, the genetic correlations between CW with BMS (0.2), REA (0.46), and SFT (0.15), and also between SFT with BMS (0.01) and REA (−0.23) in Hanwoo population  were obtTo estimate accurate genetic correlations, large sample sizes are required . The aveIt should be noted that the comparison of the genetic parameters estimated based on GRM with based on pedigree information (A matrix) must be done with caution. The genomic relationship matrix contains both IBD (identity by decent) and IBS (identity by state) information where as numerator relationship matrix is only considered identity by decent.We created sub-populations depending on the degree of relationship among individuals. The diagonal and off-diagonal elements of the genomic relationship matrix are indicated in The size of the population varied under the defined ceilings. The sample size in the subpopulations is a consequence of applying the different degrees of relatedness to the main population and, it was not directly manipulated. According to At genetic-relatedness ceilings of 0.30–0.95, when the population size was between 8,182 and 9,850, the heritability values were steady at about 0.508 for CW, 0.433 for REA, 0.337 for RT, 0.474 for SFT, 0.444 for YI, 0.486 for BMS. Traits with low to moderate heritability  were more affected by decreasing population size and indicated more fluctuations of estimates at low degrees of relationship. It has been shown that as the genetic-relatedness ceiling increases, the precision of heritability estimates increases . In our ncreases . In our ncreases .Both sample size and the degree of relationship between individuals affect the estimation of genomic correlations between traits . At the In a study, Our results indicated that there is moderate to relatively high heritability for carcass traits, therefore genomic selection can be effective to improve these traits in Japanese Black cattle. The highest and lowest heritability estimates were related to carcass weight and rib thickness, respectively. A favorable genetic correlation was observed between beef marble score with subcutaneous fat thickness and rib thickness. This is a unique characteristic that distinguishes Japanese Black cattle from other beef cattle. In addition, we revealed that the values of genomic heritability and correlation between carcass traits may be differently estimated, depending on sample size and correlation between individuals. Compared to the estimates obtained from the analysis of the full data without GRM-cutoff, the lowest heritability values were obtained at the subpopulations with low relationship degree and sample size, for most carcass traits; moreover, traits with relatively moderate to high genetic correlation were underestimated at the low genetic-relatedness ceilings.


Progne subis subis) by integrating genomic data with an extensive dataset of direct migratory tracks. A moderate to large amount of variance in spring migration arrival timing was explained by genomics . On chromosome 1, a region that was differentiated between migration timing phenotypes contained genes that could facilitate nocturnal flights and act as epigenetic modifiers. Overall, these results advance our understanding of the genomic underpinnings of migration timing.The impact of climate change on spring phenology poses risks to migratory birds, as migration timing is controlled predominantly by endogenous mechanisms. Despite recent advances in our understanding of the underlying genetic basis of migration timing, the ways that migration timing phenotypes in wild individuals may map to specific genomic regions requires further investigation. We examined the genetic architecture of migration timing in a long-distance migratory songbird (purple martin,  For example, migrants must synchronize arrival at breeding grounds to coincide with seasonal resources availability2. These resources are becoming available earlier and it is unclear if migrants will be able to match these advances in timing, potentially leading to substantial population declines3. Migration phenology may be influenced by many factors, including extrinsic factors such as rainfall4 and wintering habitat conditions5. However, migration timing is thought to be largely endogenously controlled6, and thus knowledge of its genetic architecture  is essential for predicting how migrants will respond to phenological changes that accompany climate change.Climate change affects spring phenology in temperate zones and could have significant, negative impacts on migratory animals12; however, results vary across species13 and are limited to small portions of the genome. Another limitation associated with earlier studies was an inability to quantify migratory behavior in the wild—prior to 2007, it was not possible to track animals < 100 g on migration14 and most migratory avian species fall into this size class. We overcame these limitations here, combining high resolution genomic data with an extensive migration tracking dataset for purple martins (Progne subis subis), while specifically focusing on spring arrival timing given the importance of matching timing with resources critical for breeding. The purple martin is a Nearctic-neotropical migrant that travels over 7000 km between North America and South America15 and exhibits extensive latitudinal variation in migration timing. It is thus a powerful system to study migration genomics. For example, individuals breeding in the southern edge of the range in Florida may arrive as early as mid-January, while their northern counterparts in Alberta may arrive as late as June16.Previous genetic studies provide important insights regarding migration timing, such as in genes associated with circadian and circannual rhythmsOur objective was to examine the genomic architecture of migration timing by assembling a reference genome for the purple martin and integrating sequencing data with light-level geolocator tracks. We examined results from genome-wide association studies (GWAS), to calculate the proportion of phenotype variation explained by our genomic data (PVE) along with presence of any associated single nucleotide polymorphisms (SNPs). We also calculated polygenic scores (PGS) to assess the genetic predisposition of migratory timing. Lastly, we conducted a genomic differentiation analyses between different migratory phenotypes to identify regions in the genome that may influence this trait. This study expands our understanding of the whole-genome contribution to migration and yields insight into migration timing behavior.P. subis reference genome assembly based on long reads and linked reads was 1.17 Gb in length, consisted of 2896 scaffolds, had an N50 scaffold length of 6.13 Mb and an N50 contig length of 3.08 Mb. The annotation included 12,686 genes , which was in the range of other non-model avian genomes18. We aligned resequencing data for 87 individuals to this reference resulting in 4.6 million SNPs after filtering. All these individuals were tracked on migration with light-level geolocators yielding precise estimates for migratory timing.The final Birds in this study exhibited considerable latitudinal variation in migratory timing  of 0.10–1.00. PGS estimated from linear mixed models20 were strongly correlated with spring migration timing . However, a survey of net genomic differentiation (ΔFST) between the earliest and latest spring migrants in our dataset did reveal a region of elevated differentiation on chromosome 1  may play a role in facilitating rapid microevolutionary changes27. While our sample size limits our estimates of PVE (the ETIs were quite wide), our PGS analysis supported the potential to predict individual migration timing with genomic data. Given the present sample size, it is remarkable that a substantial proportion of variation was explained with genomics. Whether this proportion could be even greater with larger sample sizes29 could be determined in future studies. However, collecting enough samples to capture strong statistical power in tests of genomic associations with phenotypes in field-based wildlife research will be challenging.Phenotypes that vary across individuals are a result of both environmental and genetic factors, and PVE represents the proportion of variance attributed to genetic factors. The large PVE estimate (0.74) is evidence that variation in migration timing is largely influenced by genetics. The predictive utility of the polygenic model across multiple deciles showed it is possible to distinguish early and late migrants using genetic variants, which could potentially aid in estimating a birds’ phenotype in the wild. With overlapping wintering grounds among purple martin colonies17. However, elevated genomic differentiation on chromosome 1 provides evidence for a potential connection between migration timing in purple martins with some genes related to rest and others that could act as epigenetic modifiers. Ppfia2 and nts are 2 of the 13 genes located in this region. Ppfia2 has been linked to sleep and wakefulness in white-crowned sparrows21, and nts has been linked to sleep regulation in European mice22. While purple martins are primarily diurnal migrants, they can incorporate both day and night flights on spring migration30, and the former genes could play a role in these nocturnal flights. Epigenetic modifiers may play a role in migration timing, such as methylation on the polyglutamine repeat domain of the clock gene affecting phenology and breeding performance31. Therefore, it is interesting that mettl25 and acss3 are in a region of high differentiation between early and late migrants. Mettl25 may be similar to mettl25b and encode a methyltransferase that represses gene expression23 and acss3 produces acetyl-CoA which promotes gene expression by acetylating histones32. Acetyl-CoA is also important for generating, using, and storing energy24. While this study suggests associations with these genes, our current data cannot pinpoint how these genes are functioning with purple martin migration timing. Further work could elucidate these mechanisms and their roles in migration timing. While estimates of FST are often considered bottom-up comparisons, we compared extreme phenotypes while controlling for population differentiation to identify genomic regions associated with migration timing. Results from this approach were further supported by a comparison of early and late migrants within populations that recovered a similar elevated pattern of genomic differentiation  to generate resequencing data40 for an additional 87 birds (average coverage 2.7 × per sample). Missing genotypes were then imputed with Beagle41, using information from the reference, surrounding genotypes, linkage disequilibrium structure, and haplotype blocks42. These included 45 male and 42 female blood samples collected from 13 different breeding colonies across North America between 2008 and 2015  from a PCA summarizing genetic variation in our dataset. We summarized results from these runs for PVE and used posterior inclusion probabilities (PIP) to identify specific SNPs with strong associations to the timing phenotypes. PIP is the probability that the SNP is associated with the phenotypic variation46 and following47 we considered SNPs with PIPs > 0.1 important. Polygenic models were created using the PLINK v1.948 and following Choi et al. (2020)'s PGS pipeline49. Accuracy of the PGS model was assessed with jackknife cross-validation partitions using a random 85% of the dataset for the GWAS (n = 74) and the other 15% to test prediction accuracy (n = 13) over each of the 100 runs. We used VCFtools50 to estimate FST between the 10 earliest (originating from two Florida colonies) and 10 latest  spring migrants. Since this FST could be elevated by processes unrelated to migration, including linked background selection and population structure51, we controlled for these potential effects by subtracting FST between Alberta and Florida (representing the northernmost and southernmost breeding regions) from values estimated between extreme timing phenotypes. This approached has been used in crows52 and blackcaps53 to isolate differentiation associated with specific phenotypes. Additionally, we estimated FST between early and late migrants within Alberta and Florida populations separately to examine if we could recover the same signature of elevated FST in the same genomic region.BSLMMs and LMMs were run using GEMMASupplementary Information.


In cereals with hollow internodes, lodging resistance is influenced by morphological characteristics such as internode diameter and culm wall thickness. Despite their relevance, knowledge of the genetic control of these traits and their relationship with lodging is lacking in temperate cereals such as barley. To fill this gap, we developed an image analysis–based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions. Analysis of culm trait data collected from field trials in seven different environments revealed high heritability values (>50%) for most traits except thickness and stiffness, as well as genotype-by-environment interactions. The collection was structured mainly according to row-type, which had a confounding effect on culm traits as evidenced by phenotypic correlations. Within both row-type subsets, outer diameter and section modulus showed significant negative correlations with lodging , but no correlation with plant height, indicating the possibility of improving lodging resistance independent of plant height. Using 50k iSelect SNP genotyping data, we conducted multi-environment genome-wide association studies using mixed model approach across the whole panel and row-type subsets: we identified a total of 192 quantitative trait loci (QTLs) for the studied traits, including subpopulation-specific QTLs and 21 main effect loci for culm diameter and/or section modulus showing effects on lodging without impacting plant height. Providing insights into the genetic architecture of culm morphology in barley and the possible role of candidate genes involved in hormone and cell wall–related pathways, this work supports the potential of loci underpinning culm features to improve lodging resistance and increase barley yield stability under changing environments. Oryza sativa) semidwarf (SD1) locus, encoding a OsGA20ox2 involved in GA biosynthesis (Triticum aestivum), mutants of Reduced Height-1 (Rht) genes are responsible for the expression of mutated forms of DELLA GA signaling repressor proteins (Hordeum vulgare), semi-dwarf 1 (sdw1) and semi-brachytic 1 (uzu1) mutant alleles were widely used in breeding programs (Sdw1 encodes a GA 20-oxidase (like rice SD1), while a missense mutation in the BR receptor gene HvBRI1 causes the uzu phenotype  exhibits altered cell wall composition and is less prone to lodging (SMOS1 gene encodes an APETALA2 (AP2)-type transcription factor  and SCM2/APO1 (ABERRANT PANICLE ORGANIZATION1) were, respectively, identified on chromosome 1 and chromosome 6 /FINE CULM1 (FC1) gene  gene regulation  gene . Recentlgulation .The lack of efficient and accurate phenotyping protocols has been a limiting factor in genetic dissection of culm architecture, for example, through exploration of wider genetic diversity in germplasm collections. In this context, different solutions emerged in recent years relying on high-throughput phenotyping methods based on the use of new image analysis tools with advanced software and special platforms .So far little is known about the genetic architecture underlying barley culm development and morphology. The aims of this work were to explore natural genetic diversity for culm architecture traits in barley; analyze their correlations with plant height, lodging, and phenology; and identify associated genomic regions and candidate genes through multi-environment genome-wide association studies (GWAS) on a collection of 261 European accessions. To these ends, we developed an image analyses–based protocol to accurately phenotype culm diameter and culm wall thickness and integrated the resulting data with genome-wide marker data from 50k SNP iSelect genotyping .2, and all the trials were rainfed – additional details about field trials and sowing densities are presented in The germplasm collection considered in this study was composed of 165 two-row and 96 six-row barley lines, including both European cultivars and a set of Spanish landraces grown at two Northern and two Southern European sites, respectively . SoutherZadoks scale was used throughout all trials in order to define the specific developmental stage for sampling and phenotypic measurements . Details1. Allele calls were made using GenomeStudio Genotyping Module v2.0.2 . After manual checking, SNP markers with more than two alleles, missing values greater than 10% and minor allele frequency (MAF) < 5% were excluded from analyses, along with unmapped single nucleotide polymorphisms (SNPs). As a result, 36020 SNP markers and 261 genotypes (165 two-row and 96 six-row barleys) remained for the analysis. Missing genotypes were imputed using Beagle v5.0 (The barley germplasm panel was genotyped with the 50k Illumina Infinium iSelect genotyping array . Physicagle v5.0 .r2) using an unbiased  estimation implemented in the R package called LDcorSV  after scaling the PC scores across a range of zero to one , r2 with relatedness , and r2 with both population structure and relatedness (rsv2). The r2 values were plotted against the physical distance (Mb), and a non-linear LOESS curve was fitted to investigate the relationship between LD and physical distance. A square root transformation of unlinked r2 values was calculated, and the parametric 95th percentile of the distribution of transformed values was taken as a critical r2 value  is influenced by the presence of population structure and relatedness due to demographic and breeding history of the accessions. To take into consideration these factors, the intrachromosomal LD between two SNPs was estimated as squared allele-frequency correlations , and allowing a diagonal residual matrix  from the similarity matrix described above.For each panel, we first organized associations from all traits into a matrix with SNPs (SNPs within the same LD region were treated as a single QTL) in rows and traits in columns, which was filled with cells for corresponding marker effects and association with the corresponding trait , after correction for population structure and kinship. The resulting matrix was then used to provide a pairwise Pearson correlations matrix between loci. The correlation matrix was subsequently used as an input matrix for network analysis. We used undirected graph networks to visualize submodules of loci using igraph package in R to visualize proximities between loci in a network plot . Nodes  compared to LD decay observed within the six-row panel (LD = 0.6 Mb), with slightly higher LD in the whole panel (LD = 0.8 Mb).As a prerequisite for GWAS, LD was calculated for each chromosome using the squared correlation coefficient between marker pairs, election . The levThe barley collection was grown under field conditions in seven environments including four locations and 2 years, 2016 and 2017 . Field sThe single and across environment means, standard deviations (SDs), ranges, minimum, and maximum values are indicated in The diversity of phenotypic values according to row-type and germplasm source within the panel was visualized based on the box plots . AccordiWe further compared phenotypic means according to row-type and germplasm sources as these were important factors shaping population structure within the panel . ResultsTogether, these analyses show that our germplasm panel harbors significant genetic variation for culm-related traits and suggests the existence of complex genotype x environment interactions. The obtained datasets provide an ideal starting point for investigating the genetic architecture of barley culm morphology under contrasting environmental conditions.In order to gain insight into the relationships among different traits, pairwise correlations were calculated based on phenotype values estimated both within single and combined analyses of environments –7. GermpIn order to explore the relationships between culm traits and lodging, excluding the effect of row-type, further analyses were conducted within row-type subsets.In the two-row panel, correlations between culm traits were generally maintained and stronger negative correlations were observed between culm morphological traits and lodging. Some discrepancies were also observed compared to the whole panel, e.g., the negative relationship between TH and lodging in contrast to the positive correlation between these traits in the whole panel, which was possibly due to confounding effects from six-row landraces (thick culms and more prone to lodging). Furthermore, while positively correlated with lodging, PH was environment-dependent and did not show strong correlations with culm morphology, e.g., in Southern environments, the relationship was mainly weakly negative and in Northern weakly positive . HD was In the six-row panel, culm morphological traits had the strongest interrelationships. HD was also in agreement with the whole panel with stronger negative correlations with culm morphology, and in contrast to the two-row panel, it was positively correlated with lodging. PH had a negatively weak relationship with culm traits with stronger positive correlations in Northern trials and negative correlations in Southern trials .Together, these results highlight the potential of culm morphological traits as interesting targets for the improvement of lodging resistance in barley. In particular, the general lack of correlation within row-type subsets suggests that culm diameter is largely controlled by distinct genetic factors with respect to PH.r2 threshold estimated from LD decay analysis. These threshold values were found to be –log10 (P) ≥ 4.94, –log10 (P) ≥ 4.75, and –log10 (P) ≥ 5.02 for the whole panel, two-row, and six-row panels, respectively. However, the p-values with –log10 (P) ≥ 4 were also retained as suggestive QTLs.We performed GWAS using multi-trait mixed model (MTMM) proposed for multi-trait or multi-environment association mapping to detect quantitative trait loci (QTLs) underlying culm morphological traits, incorporating kinship estimated from marker data and population structure using principal components . This meP) ≥ 4 in close vicinity were grouped into a single QTL based on the average LD decay, due to variable LD blocks for individual chromosomes and thus a variable decay across the chromosomes  was associated with both OD and SM in the whole panel and located in close proximity with candidate gene n barley .OsSDG725 encoding a histone H3K36 methyltransferase and playing an important role in rice plant growth and development  explained a high proportion of phenotypic variance. We found that QTL1-2H (pos: 1.51–1.74 Mb) harbors the ortholog of rice elopment .Sdw1 , which is associated with both PH and SI across all panels and is closely linked to the well-known plant height gene Sdw1  found inCCD8-d (carotenoid cleavage dioxygenase). QTL18-4H was detected in both the whole panel  and two-row panel , explaining between 2.74 and 7.1% of the variance (pos: 589.66–590.52 Mb). SNP10-4H was associated with SM and located within a pseudo-response regulator gene (470.68 Mb). Also, about 0.8 Mb from this marker we noted a homolog of TRANSTHYRETIN-LIKE PROTEIN (TTL), a gene that was previously associated with stem circumference in sorghum  associated with ID, OD, and SM in the two-row panel and explaining at least 6% of the phenotypic variance. This QTL was found to harbor a homolog of rice  sorghum . OD and G GRAIN1 .OsCCD1  as well as contained the rice homolog of OsCCD1 . QTL2-5HLarge Grain 1 (LG1/OsUBP15), a gene involved in seed size and plant height  that can directly interact with SMALL ORGAN SIZE1 (SMOS1/RLA1), and RLA1 plays as an integrator with both OsBZR1 and DLT to modulate their activity . The region contains several candidates, including a gene encoding a GRAS transcription factor orthologous to rice activity .HvD27, the barley ortholog of rice strigolactone biosynthesis gene DWARF27 encoding beta-carotene isomerase -sterol reductase previously shown to act in the brassinosteroid pathway in barley . SNP16-7H (pos: 265.29 Mb), a hotspot SNP, associated with ID, OD, and SM in the six-row and whole panels. Another noteworthy QTL was QTL27-7H, associated with PH, SI in the whole panel; OD, PH, and SM in the six-row panel; and SI in the two-row panel (pos: 570.827–572.61 Mb). The region contains somerase . QTL30-7itecture . Finallyn barley .PPD-H1 gene  was associated with SI in the two-row panel. QTL6-2H (pos: 22.37–23.99 Mb), associated with SI and PH , spans the well-known barley -H1 gene , involve-H1 gene . The genn barley . QTL7-7Hnditions . CurrentIn order to appraise the effects of the QTLs on lodging susceptibility, we focused on QTLs with QM and QF effects 10. AllelicIn conclusion, the results from these analyses support the usefulness of SM and culm diameter as parameters for selecting alleles to improve lodging resistance and provide chromosomal positions and markers associated to promising loci.In this study, we investigated natural genetic variation for morphological characteristics of the barley culm and their relationships with lodging and agronomic traits. To date, no genetic studies have used image processing-based phenotyping to investigate the genetic architecture of culm morphology in barley. For this reason, we developed a robust method to extract quantitative measurements of culm diameter and thickness from images of culm sections, showing that significant phenotypic variation exists within our barley germplasm panel with a major contribution of genetic variation to these traits as supported by medium–high heritability values.VRS1 into the barley gene pool during domestication  minimize the confounding effects of row-type on association analyses; (ii) understand whether distinct loci are segregating in row-type subpopulations and thus different regulatory networks are involved in genetic control of the studied traits. The use of mixed model in GWAS is a well-established approach to efficiently reduce false positive associations for most traits, but it may also mask true signals that are correlated with population structure. As a result, loci that distinguish barley subpopulations are often difficult to detect using mixed model. To circumvent this problem, many association mapping studies have analyzed each subpopulation separately and successfully identified loci specific to each subpopulation. In our study, 120 marker-trait associations were detected in the whole panel, including 21 and 27 that were shared with the two-row and six-row panels, respectively. Six associations were detected across all three panels. In addition, we uncovered 24 and 45 QTLs specific for two- and six-row panels, supporting the relevance of running GWAS on row-type subsets. We also noticed that for some QTLs detected across both row-types, allele frequencies and peak markers differed between the row-type subsets, resulting in opposite effects of minor alleles on the same trait. Taking as an example the PH locus QTL19-3H closely linked to the well-characterized HvDIM gene encoding the barley Δ5-sterol-Δ24-reductase, an enzyme involved in the brassinosteroid biosynthetic pathway , suggesting HvDIM as a possible candidate for QTL34-7H. However, this genomic region harbours other potential candidate genes, that have been reported as members of glycosyl transferase (GT) gene family, such as cellulose synthase genes of the GT2 family that influences culm cellulose content  is located near SNP7-1H (262.1 Mb): a retroelement insertion within this gene was previously associated with the fragile stem2 (fs2) mutant phenotype in barley, suggesting a link between stem strength and genes involved in cellulose content (HvMND1 (552.9 Mb), which encodes an N-acetyl-transferase-like protein recently shown to regulate barley plastochron and plant architecture  gene located in close proximity to the peak marker. A recent phylogenetic study showed that rice has four CCD8 genes , while Arabidopsis has only one: both Arabidopsis CCD8 and rice CCD8-b are involved in the biosynthesis of strigolactones, phytohormones that control lateral shoot growth, and affect stem thickness at least in some species (reviewed in OsCCD8-b is located on chromosome 3H, while the CCD8 gene associated with QTL17-4H is more closely related to OsCCD8d, whose function has not been characterized yet (MDP1, encoding a MADS box transcription factor implicated in brassinosteroid signaling (Beside these SNPs, additional QTLs were identified as associated with culm features and having an impact on lodging, independent of PH. Among them, QTL17-4H had the main effects on ID, OD, and SM and contained a carotenoid cleavage dioxygenase 8 (ized yet . An alteignaling .ad hoc phenotyping protocol to obtain accurate measurements for internode diameters and thickness and derive section modulus and other parameters known to impact lodging resistance in cereals. Application of this method to a diverse set of field-grown barley accessions under seven different environments in Southern and Northern Europe provided a unique platform to dissect genotype x environment interactions and identify stable QTLs across environments. These results support the robustness of our phenotyping protocol that may now be applied to other major crops with similar stem morphology, such as wheat and rice.In conclusion, we established a new While validation of potential candidate genes will require more detailed analyses, this work represents the first comprehensive analysis of the genetic architecture of culm morphology in a barley germplasm collection and its relevance for lodging. Identification of 21 main effect loci for culm diameter and/or section modulus with significant effects on lodging, independent of plant height, may open new avenues to improve lodging resistance and increase barley yield stability.The genotyping dataset used for GWAS is provided in GB developed the stem phenotyping protocols, collected the data for culm morphology traits, and contributed to writing of the manuscript. SS conducted the statistical analyses and drafted the manuscript. AT, HB, and WT multiplied and dispatched the seed for trials. AS, AT, WT, AC, and EI generated the standard scoring protocol. WT designed the field trials and matched the genotypic data with the phenotypic. AT, LC, WT, HB, JR, PG, and AS hosted field trials. SD, HB, PG, and EI collected the samples and agronomic data. RW, JR, AC, and EI provided genotypic data. RR helped in candidate gene searches. LR conceived and coordinated the study, contributed to writing of the manuscript and agrees to serve as the author for contact and responsible for communication. All authors revised the manuscript and approved its final version.