Crop breeding programs must accelerate crop improvement, spur widespread adoption of new varieties and increase variety turnover they are to meet the diverse needs of their clients. More comprehensive quantitative approaches are needed to better inform breeding programs about the preferred traits among farmers and other actors. However, the ability of current breeding programs to meet the demands of their clients is limited by the lack of insights about value chain actor preference for individual or packages of traits. Ranking traits based on monetary incentives, rather than subjective values, represents a more comprehensive, consistent, and quantitative approach to inform breeding programs. We conducted a large pilot in Uganda to assess the implementation of a novel approach to trait ranking, using a uniquely large sample of diverse sweetpotato value chain actors. We found meaningful differences in trait ranking and heterogeneity among different actors using this approach. We also show our approach’s effectiveness at uncovering unmet demand for root quality traits and at characterizing the substantial trait demand heterogeneity among value chain players. Implementing this approach more broadly for sweetpotato and other crops would increase the effectiveness of breeding programs to improve food security in developing countries. The CGIAR carries out public international breeding programs that are developing improved varieties for the benefit of smallholder farmers. These programs typically follow iterative cycles of creating relevant genetic variation, directional selection of superior genotypes, and thorough field testing in target populations of environments across which future varieties will be grown.In response to demands of rapid social, demographic, climate and nutrition changes and gender inequality, the Consortium of International Agricultural Research Centers  and participatory variety selection (PVS) were introduced in the 1970s and 1980s in many crop breeding programs and these, or similar approaches are currently being used to assess varietal preferences  to incorAs a possible remedy to these shortcomings, and in contexts where PPB and PVS are still the main methods, we present a novel, comprehensive and quantitative approach for crop breeding programs to target key value chain actors (and nodes) more efficiently. Even in cases where programs have evolved beyond PPB and PVS, there is still inadequate understanding of the combination of preferred traits to target for adoption and maximum impact. This paper presents an alternative taking inspiration from an approach recently developed for animal breeding , and aimIpomoea batatas) and many other crops has drawn on PPB, PVS and farmer surveys. PPB was devised with intention for “bringing farmers back into breeding”  varieties enriched with beta-carotene. Uganda is a leading sweetpotato producer in SSA, with a large per capita sweetpotato consumption of approximately 83 kg/year . Furthereconomic selection index approach that provides a comprehensive and systematic approach to inform crop breeding programs about the most desired traits. The trait weightings derived from this method  a workshop with value chain actors organized as a series of focus group discussions (FGDs) targeting seed producers and multipliers, root producers, traders , processors, and consumers; (ii) community forums with seed multipliers; (iii) household surveys; and (iv) market surveys. The workshop, FGDs and community forums generated understandings about the most desired traits, the desired direction of improvement, gender differences in trait preferences, and price differences across a range of variation at the trait level by monetizing the traits.n = 1,000) who were randomly selected from sweetpotato farmers in the central Uganda region, with 984 successfully completing the survey. The sample was stratified by gender, and the surveys captured sociodemographic information on respondents  and their value chain activities. We also surveyed 52 sweetpotato seed multipliers. The market survey targeted sweetpotato traders (n = 134) and consumers (n = 147) drawn from rural and urban sweetpotato retail markets through systematic random sampling. Data were collected using an information and communication technology-based platform (ICT) and analysed using the 1000minds software to generate breeding indices. The indices formed the basis for ranking specific traits relative to each other.Household surveys targeted a large sample of root producers  in the 1000minds® preference survey. We use CA, on the other hand, to identify groups of respondents who have similar trait preference patterns without making any a priori assumptions as to which groups are likely to have different preferences.The analysis was conducted separately for both sociodemographic and 1000mindsThe calculation of economic values from preference data enables the development of economic selection indices to rank varieties. Trait economic values were calculated according to the preference (%) for each trait relative to the preference (%) for the trait expressed in monetary terms in the survey, “price per 100 kg bag” . Thus, ti and individual respondent q, P is the preference (%) for each trait, α is the number of units represented in the trait level , and β is the monetary value per preference (%) for individual q. Values for β were calculated as:where for trait mv is the number of units represented in the level for the monetary trait  and, for individual respondent q, Pmv is the preference (%) for the monetary trait. An example of the calculation is provided in the Findings section.where αEVBP) was calculated by multiplying the economic value of a trait in survey units (EVS) by the unit transformation (ut), as follows:The economic value of a trait in breeding program units , to rank varieties on the selection index. For the case of root size, the unit of root size in the survey was the percentage (1%) of roots that are of medium size, whereas the unit of root size in the breeding program was a score . Hence, an increase of 1 score, therefore, resembled a decrease of 12.5% in roots of medium size (100% / 8), assuming that medium roots are considered excellent on the 1 to 9 scale. For all respondents, the economic value for root size in breeding program units (EVBP) therefore equals UGX 5,338 per score [= UGX 427 × 12.5(%)]. Note that breeding values presented in this pilot were computed based on several assumptions (see The ions see  about thR) was on each trait (t) in the selection index, and therefore the potential to make genetic progress. The genetic standard deviations were used in the following equation:Lastly, relative emphasis  on each trait (t) in the selection index has also been converted to relative emphasis in UGX per USh100 of index progress by multiplying R for trait t by 100. where, σSweetpotato value chain actors attach importance to a wide range of traits, ranging from agronomic production to quality varying according to their position in the value chain . Vine suMultivariate analysis of preferences using principal component analysis and cluster analysis  identifiAs it might be expected, most consumers fell within the “root quality” cluster, while traders/retailers and processors were in an intermediate position between producers (“productive output”) and consumers .n = 984), which was selected by design because this actor group is proportionally larger compared to other groups in the value chain. Root producers are also the most immediate customers and some of them interact directly with genetic improvement programs.s), unit transformation (ut) coefficients, and in breeding program units (EVBP). The EVBP weights per cluster are presented in the last four columns. The negative values are a result of the way traits are defined in the breeding program. For instance, root size in the breeding program was a score on a 1–9 scale (excellent to terrible) while the units presented in the survey were based on increases in regular/good shape of roots. As such, the direction of the unit transformation factor converts the survey units to the breeding program units for each trait.EVBP. To understand how selection indices align with the preferences of the cluster groups, REs were added together within the cluster . Vine anSweetpotato breeding programs in Uganda and other developing countries continue to be focused on storage root yields and, more recently, nutrition objectives that target biofortification of staple crops. Traits relevant to other value chain actors have not found their way into the breeding programs on a regular basis, despite evidence that neglect of quality traits  impedes This case study presents a novel approach to setting objectives for crop breeding, designed to help prioritize traits for different nodes of the value chain using comprehensive market insights. This approach was used for sweetpotato in our study and with some improvements may be extended to other sweetpotato breeding programs and crops. It informs the definition of breeding objectives as breeders often face the difficulty of balancing the selection of a few preferred traits from a wide array of “nice-to-have” traits, some of which can be negatively correlated (have trade-offs), and have multidimensional  preferenOverall, producers prioritized fresh root yield below vine survival, sweetpotato weevil and SPVD resistance, and storage shelf life. But we also found significant variations in preferences for these traits within this actor group, implying that there is no one-size-fits-all scenario and that new varieties with different combinations of traits can target different producer types.Surprisingly, the sensory/quality traits—namely, flesh color, root flesh sweetness, hardness/firmness of the roots after cooking—were ranked very low contrary to recent suggestions in the literature . HoweverIn summary, the key contribution of this new approach is that traits are prioritized using economic values derived from systematic market intelligence, rather than subjective values, and targets all key value chain actors. Our study also underscores the relevance of gathering market insights from multiple actors in the value chain in defining breeding objectives. To gain sufficient insight about markets served, and to articulate their trait preferences, and traits required for impact and to achieve SDGs, it is essential to collect insights from other experts such as social and gender scientists, nutritionists, food technologists, economists, and marketing specialists, as well as plant breeders. It would be counterproductive, and unfair, for plant breeders to assume such roles since their training, experience and interests are best put to good use by increasing genetic gains and focusing on genetic research to develop varieties able to deliver climate resilience, food security and aligned to rapidly evolving social landscapes.economic selection index does not differentiate the types of questions administered to different value chain actors. Thus, traders and consumers ranked and valued production traits that may not be directly economically relevant to the entire value chain. Second, due to low literacy, it was quite challenging to describe traits in terms that were understood by all respondents the same way, and that could be linked to breeding program definitions. For example, scores of beta-carotene content of roots (a proxy for vitamin A richness) were hard to translate into a language that accurately captures the value of this trait in ways most study respondents could understand and relate to because of low literacy levels. Breeders use scores based on the intensity of orange color for this trait, and most respondents would not understand the relationship between the color and beta-carotene content. Third, incomplete phenotyping information for the national breeding program in our study (Uganda) made it difficult to create economic weights for all the traits as described in the methods section. These methodological/software challenges pinpoint areas where further refinement of the new approach is needed for wider applicability.While this paper represents substantial progress in estimating the economic/monetary worth of crop traits in a manner that can better inform breeding programs, we acknowledge three main remaining challenges. First, without understating the significantly increasing complexity in variety design and deployment, the current The first such refinement will be to build a framework/logic into the approach that allows different traits or trait combinations to be administered to different actors based on the value chain nodes they represent. The second refinement calls for the need of more studies to ground-truth trait selection and breeding lexicons with nutritionists and food scientists to ensure a clear description of traits for the target respondents. This study relied on information from expert and stakeholder consultation only because work by nutritionists in developing such lexicons was ongoing at the time of this study. The third refinement will balance out the survey population, which was almost 70% root producers in our case, thus weighting the findings in favor of root producers. Future application of this approach in a value chain context should assess if and how equal representation of the actors in the sample influences the results. These refinements are critical if this approach is to be scaled up to other crops and contexts.This study has demonstrated the merits of value chain-wide market intelligence to inform crop breeding programs in developing countries. Despite the limitations with our approach (acknowledged above), it presents a new and objective method for prioritizing traits in crop breeding programs with good insights from the market based on economic incentives instead of subjective ranking. We conclude from this pilot that to be successful in understanding actors’ preferences, it is essential to crowdsource market intelligence from a wide range of actors. We also note that trait preferences are not homogenous: we find distinct actor groups, even within a given value chain node, who have distinct preferences for specific combinations, or baskets, of traits. The large heterogeneity in trait preferences that exist within root producers as a cluster, and other actors, clearly indicates that the current approach to plant breeding which focuses mostly on farmers is not optimal. As expected, a consumer who does not grow the crop is likely to rank sensory quality traits highly while a farmer would express preference for yield and yield protecting traits such as drought tolerance and disease and pest resistance. We further conclude that subjective ranking of traits has tended to mask the real trade-offs that clients of the breeding program have to make when comparing traits. Notably, economic valuation and preference ranking in this pilot highlight, in general, the continued importance of production traits—vine survival, yield, and disease and pest resistance. However, there is also strong demand for root quality traits, which further suggests that breeding needs to be pluralistic. Lastly, our pilot demonstrates that real-time gathering and analysis of value chain-wide market intelligence using internet-based platforms designed for developed countries can be performed successfully for plant breeding purposes.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.JJO led the study design and implementation and writing of the manuscript and revisions. BS, TB, AS, and DM-C co-led the study design and implementation, data analysis, and review of the manuscript. JS contributed to data analysis and writing of the manuscript. BY and ROMM co-led the study design and reviewed several drafts of the manuscript. HC conceived the study, cowrote the manuscript, and reviewed several drafts. GT, SH, MQ, and GH reviewed and edited the drafts. All authors contributed to the article and approved the submitted version.TB was employed by AbacusBio Ltd., United Kingdom. BS and AS were employed by the company AbacusBio Ltd., New Zealand. JS was employed by Aardevo Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Water availability is a crucial environmental factor on grain number in wheat, which is one of the important yield-related traits. In this study, a diverse panel of 282 wheat accessions were phenotyped for grain number per spike (GNS), spikelet number (SN), basal sterile spikelet number (BSSN), and apical sterile spikelet number (ASSN) under different water regimes across two growing seasons. Correlation analysis showed that GNS is significantly correlated with both SN and BSSN under two water regimes. A total of 9,793 single nucleotide polymorphism (SNP) markers from the 15 K wheat array were employed for genome-wide association study (GWAS). A total of 77 significant marker-trait associations (MTAs) for investigated traits as well as 8 MTAs for drought tolerance coefficient (DTC) were identified using the mixed linear model. Favored alleles for breeding were inferred according to their estimated effects on GNS, based on the mean difference of varieties. Frequency changes in favored alleles associated with GNS in modern varieties indicate there is still considerable genetic potential for their use as markers for genome selection of GNS in wheat breeding. Triticum aestivum L.) is one of the most important crops globally, mainly grown in semiarid and arid regions of the world , and thousand grain weight (TGW), which are important grain yield components . Recent TaTEF , FZP  controlling GNS and spikelet infertility were identified under high-yield potential conditions . RelativShanxi Province in China is situated in a semiarid region, with an annual rainfall between 400 and 650 mm. Dryland occupies 70% of the wheat planting area. Shanxi has a long history of wheat planting and has always been famous for its drought resistance and stable yield varieties of wheat. Varieties such as Jinmai 33, Chang 6878, and Jinmai 47 were widely cultivated in dryland areas. The descendants of these excellent accessions are the main varieties currently spreading in China, thus making it a representative to study the genetic evolution of wheat GNS in semiarid areas and the effects of water regimes on GNS with Shanxi wheat varieties. In this study, 15 K SNP array markers were used to identify the population structure of the Shanxi wheat panel and genome-wide MTAs of wheat GNS, SN, BSSN, and ASSN under different water regimes. This association analysis provides useful information for marker-assisted selection in breeding wheat for increasing yield.A total of 282 hexaploid wheat collections in Shanxi Province of China were used in this experiment . These gThis study examined the results under two irrigation regimes at the experimental station of Linfen in Shanxi Province, China, located at 36°48′ N and 111°30′ E. The study was conducted over 2 consecutive years . The monthly rainfall rates and average temperature during the two trial years are presented in Ten representative primary tillers from the center of each row were collected to investigate the following traits: total SN per spike, the GNS, the basal sterile spikelet number (BSSN), and the apical sterile spikelet number (ASSN). After harvest, thousand grain weight (TGW) was measured. The drought tolerance coefficient (DTC) of GNS, SN, and TGW values was calculated using the formula I1/I3, while for BSSN and ASSN, it was calculated using I3/I1.R. The H2 value was calculated using the formula H2 = VG/(VG + VE/r), where VG is the genotypic variance, VE is the environment variance, and r is the number of replications  values across two repetitions were conducted using ications . CorrelaFor the genotyping assay, approximately 1.0 g of a young leaf was collected from each wheat genotype before they reached the elongation stage. Genomic DNA was extracted using the cetyl trimethyl ammonium bromide (CTAB) method and stored at − 80°C until use. DNA dissolved in TE buffer was sent to MOL-BREEDING company  for high-throughput genotyping using a set of GenoBaits Wheat 20995 (10111mSNP) panels. After filtering out markers with minimum allele frequency (MAF) <0.05 and markers with >10% missing data, as well as >20% heterozygosity , a total of 9,793 high-quality SNPs were included in the following population structure and GWAS analyses.P-value of < 0.001 or − log10 < 3 was used as the screening criterion  in I1 and I3 environments. Descriptive statistics data and frequency distribution of the genotypes for the investigated traits in I1 and I3 environments based on the average data over years are presented in  = 82.40 . CompareFor the three different types, compared with I1, under the I3 irrigation regime, the highest increase in the mean value of GNS happened in irrigated varieties (29.85%) than in dryland ones (25.33%) and landraces (20.44%). Among the irrigated varieties, Taimai 101, Xiangmai 23, and Yunhei 161 were most sensitive to water supply, with an increase in GNS by 89.53, 84.23, and 73.57%, respectively. Otherwise, the most insensitive ones were Ziyou 5, Yunyin 1, and Tai 615, and their GNS were increased by 3.01, 3.37, and 3.40%. SN did not change much under two conditions, namely, irrigated varieties and dryland varieties, and landraces were increased by 0.38, 0.95, and 1.24% in I3, respectively. The TGW of irrigated varieties increased the most, 14.30%, that of dryland varieties increased by 6.89%, and that of landrace varieties increased by 3.84%. In the I3 condition, the ASSN decreased by 32.42% in irrigated varieties, more than in dryland (22.93%) and landraces (− 6.46%), whereas the BSSN were less decreased in irrigated varieties (1.26%) than in dryland varieties (10.71%) and landraces (12.17%).Pearson’s coefficient of correlation between traits was calculated based on the data averaged across 2 years under the two irrigation conditions . GNS wasTGW and DTCSN of most accessions were close to 1  was plotted against the ΔK calculated using software STRUCTURE version 2.3.4, the highest ΔK was observed at K = 5  ≥ 3.0] were identified for the traits in the two water regimes examined. In total, 77 MTAs distributed on 20 chromosomes (except 2D) were identified. Among them, 18, 39, and 20 were on genomes A, B, and D. The phenotypic variation explanation rate (R2) ranged from 4.62 to 11.19%  but also ASSN . 5A_30786531 was significantly associated with GNS (I3_BLUP) and BSSN (I1_BLUP and BLUP_ALL). 6B_623314284 was significantly associated with ASSN (I1_BLUP and BLUP_ALL) and BSSN . 3D_68039763 and 7B_650666608 were significantly associated with both SN and BSSN traits. 5A_575163867 associated with BSSN (575.2 Mb) was colocated with QTLs qSN5A.3, qSL5A.1, qGN5A.3, and qGS5A.2 (574.6–575.4 Mb)  .GNS (1), DTCSN (4), DTCBSSN (2), and DTCASSN (1) (2A_82034103 (GNS_I3_BLUP and DTCGNS), 2A_567774459 (SN_I1_BLUP and DTCSN), 2B_3313327 (ASSN_I3_BLUP and DTCASSN), and 6B_283377788 .For the DTC values of each trait, eight SNPs/genetic regions were identified in DTCASSN (1) . Four ofTT2B_26062934, GG6B_52209942, and GG6B_283377788 showed more GNS (increased by 1.09–2.03 with I1 and by 3.29–4.21 with I3; 6B_52209942 and 6D_83175038 were 81.91 and 92.20%, respectively, whereas the proportion for locus 2B_132280332 was only 17.73%, which implied that 2B_132280332 had not experienced strong selection. The effect of favored alleles was estimated for GNS investigated in this study . Higher TGW showed a slight increase over the years; however, the DTCGNS showed a decrease in recent years (TGW, indicating that irrigated cultivars were less sensitive in TGW when encountering water deficiency.We also analyzed the changing trends of GNS and TGW, as well as the DTC value, of these two traits in dryland and irrigated wheat varieties over the years. Results showed that the GNS of both dryland and irrigated wheat was increased along with the increasing frequency of favored alleles, and the TGW also improved together with GNS over the years . For thent years , which iA wheat spike normally produces up to 180 floret primordia; however, during development, more than 70% of the florets abort , 2016. ADue to nutritional competition, the increase in spikelet and grain number of a panicle is often accompanied by a decrease in TGW. In this study, there is no significant correlation between SN and TGW or GN and TGW under the I1 and I3 environments . StudiesSNS_7AL QTL, the increase in total grain yield in haplotype 2 relative to haplotype 1 was higher in the irrigated treatments than in drought treatments (Irrigation conditions can also affect the increase in GNS of favored alleles. In eatments . This maeatments .2A_82034103 for DTCGNS, 2A_567774459 for DTCSN, 2B_3313327 for DTCASSN, these markers were significantly associated with traits under the I1 condition but cannot be examined under the I3 condition (As ondition .The original contributions presented in the study are included in the article/XZ and LQ drafted and revised the manuscript and contributed to data analysis. YL, NW, JJZ, and BW performed the phenotypic evaluation and helped with data analysis. JJZ, BY, and JZ helped to draft the manuscript. JZ and JW designed and coordinated the study and revised the manuscript. All authors have read and approved the final manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen  The ability to perform genome-wide analyses of DNA variations has enabled detailed investigations of the genetic architecture of traits in many organisms. In human genetics, the study of association, prediction and heritability across the genome has received considerable attention and the main statistical challenges related to problems such as the robust estimation of SNP (single-nucleotide polymorphism) heritability are being overcome ,2. SimilStreptococcus pneumoniae.To address this shortcoming, we present a suite of analyses that take into account the challenges of bacterial genetics such as genome-wide linkage disequilibrium (LD) and genome plasticity. Our methods are based on popular methods in human and bacterial genetics, but these are coupled with innovations to better adapt them to bacterial datasets. Our suite of methods uses linear mixed models (LMMs) and linkage disequilibrium score regression (LDSC) to investigate genome-wide association, heritability and heritability partitioning, along with elastic-net regression for trait prediction. We use simulation studies to validate our suite of methods and demonstrate its capabilities in comparison with current state-of-art methods. We use the methods to analyse three traits, two of them previously unstudied, in Streptococcus pneumoniae, or the pneumococcus, is a Gram-positive human pathogen that can cause several invasive diseases such as pneumonia, meningitis and sepsis, as well as milder diseases such as acute otitis media and tonsillitis. Typically, pneumococci colonize the nasopharynx of a host asymptomatically and transmit effectively between young children, who frequently carry the bacterium until they develop broad natural immunity. This may be supplemented by vaccination with any of the polysaccharide conjugate vaccines (PCVs), which induce effective protection against some common virulent serotypes.et al.  is also well established from genome-wide association studies (GWAS)  is a polygenic trait with moderate heritability and predictive accuracy.Several population genomic studies have characterized epidemiological traits of the pneumococcus. In a pioneering study, Lees et al. , found hs (GWAS) . Howeverrculosis  but not Given the increasing availability of large-scale bacterial genetic datasets, the developments presented here will provide a valuable guide to future studies.The present study is based on nasopharyngeal swab data collected monthly from infants and their mothers in the Maela refugee camp in Thailand between 2007 and 2010  and/or tPenicillin and ceftriaxone susceptibilities were assessed using 1 μg oxacillin disks in accordance with the 2007 CLSI guidelines . Only isS. pneumoniae strain. To allow for occasional false negatives in strain identification, we followed  in 370 host infants . The median CD was 64 days .We used a published dataset  of high By definition, the sequences from different isolates within the same carriage episode are of the same strain, but there can be sequence variation. For the 337 episodes represented by >1 genome sequence, we used the sequence from the last isolate sampled, which we expect to be the most representative sequence as it may incorporate some effects of host–pathogen interaction that increased CD. However, as within-strain sequence variation is low this choice has little impact, which we checked by repeating analyses using the sequence from a randomly chosen isolate from each of the 337 episodes, finding only negligible variation from the results reported here.accessory. Pangenome data were extracted by assembling and annotating the read sequences using Prokka version 1.14.6  of 0.05, we performed 500 permutations of the ceftriaxone MIC phenotype, each time re-running the association analysis pipeline and recording the largest test statistic. From the resulting 500 values, we set the significance threshold for the real-data analyses to be the 25th largest (= 24.8). In comparison, the corresponding Bonferroni threshold based on 133K tests and a For comparison, we also employed a 1 df association test based on presence/absence of the major allele at each variant, whether gap or a nucleotide, using the Bonferroni threshold. While this test allows some gap effects to be detected, if gap is not the major allele it assumes that the gap and minor nucleotide effects are the same. If gap is the major allele then all nucleotide effects are assumed to be the same.Levels of recombination vary over bacterial species, but in general asexual reproduction leads to strong population structure, which is challenging for association analyses ,21. Popua priori based on measures of similarity between pairs of sequences.There is no complete solution to the problems caused by population structure, and attempts to address them risk discarding true as well as spurious signal. Most approaches introduce either covariates or a genetic random effect into association models to absorb signals that can be explained by population structure, which then do not contribute to association statistics. The variance-covariance matrix S. pneumoniae, and consequently the usefulness of the resulting phylogeny has been questioned \docubj = 0. It requires binary coding of genetic variants, and so can be used for the gap and major-allele tests, but it cannot accommodate the frequency-coding or omission of the gap sequences at each SNP test. To overcome this problem, we used a two-stage LMM/GLS pipeline for the SNP test, similar to EMMAX . In the CD analyses for the SNP test, we were able to incorporate an extra random effect to model shared host in the LMM/GLS pipeline, but for the gap and major-allele tests performed using Pyseer-LMM, this was replaced by a binary covariate indicating previous carriage.Pyseer  has receto EMMAX , in whicto EMMAX  with bj  lme4qtl . The bj Accessory genome genes were tested using the LMM/GLS pipeline, with a single test based on standardized gene counts.S. pneumoniae  and leave-one-strain-out (LOSO)  CV were h2, with Genetic effects at different genome sites can interact (epistasis), but we restrict attention to the narrow-sense heritability ocument} . For theression) .h2 using a modification of LDSC . Following \docuollowing , prior tlj involves a sum over the whole genome. In human genetics applications only a neighbourhood of j is included, but the presence of genome-wide LD in S. pneumoniae makes it difficult to define a suitable neighbourhood. The definition of lj also incorporates a bias adjustment .The score justment  that canment} in  used weiSj for LDSC, we found that the very strong population structure effects distort the LDSC regression relationship in the absence of any adjustment, yet a fully effective adjustment for population structure was also unsatisfactory because it removed informative signal. The best compromise that we could identify between inadequate control for population structure effects and loss of association signal with effective control, was to compute the major-allele test statistic Sj in the fixed effect model (FEM):a is the corresponding effect size. For the CD analyses, we also included the previous carriage covariate in , we do not report association results based on this FEM and only use the Sj obtained under this model within LDSC.When choosing the testing method to generate the el (FEM):\documentpbp1a and pbp2x. We first identified a set of large effect-size variants with basepair positions between 285 000 and 340 000 by clumping the frequency-coded variants using correlation threshold 0.85. These variants were used as fixed covariates when re-calculating the Sj for this analysis, which prevents tagging of effects from the omitted region.As well as estimating genome-wide ment} in  omittingh2 ∈ {0.1, 0.2, ..., 0.5}. In each simulation, 5, 10, 15, 20 or 25 causal variants were randomly selected such that MAF > 0.05 and r2 < 0.2 for all pairs of causal variants. Four replicates were performed for each of the 25 combinations of causal loci and h2, and the resulting 100 simulated datasets included a total of 1500 causal loci , continuous traits were simulated under an additive model with h2 ∈ {0.1, 0.2, ..., 0.9}, we simulated 100 continuous traits using 10 randomly selected causal variants with MAF > 0.05 and r2 < 0.2. We then computed We used BacGWASim  to simulThe gap/SNP test with max statistic  performed better than the alternatives we considered . The QQ-plot  indicates some inflation of test statistics suggestive of population structure effects . The major-allele test also identified no associations  and treeWAS identified 3 hits in 2 genes: purF and polA .None of the 2 310 tested accessory genes were associated with CD. Similarly there were no genome-wide significant results among the 44 097 gap and 91 822 SNP tests at core genome variants Figure . The shah2 from the accessory genome. LDSC analyses also confirmed only a small contribution to h2 from the known drug-resistance region . Furthermore, phenotype prediction with allele frequency-based coding of variants slightly outperformed major-allele coding .Despite the lack of associations for CD, prediction accuracy Table  and herik-mer analysis (purF and polA (reported above) nor the region identified in our LMM analyses. We conclude that we are unable to reliably identify individual associations for CD, but there is good evidence for it being a moderately-heritable polygenic trait.We also performed association testing on all 1612 isolates linked to a carriage episode. This analysis identified four sites at basepair positions 1 522 542–1 522 896, near the previously-reported phage hit based on analysis . HoweverFor both MIC phenotypes, from the 2242 accessory genes tested, one (with Panaroo label group_102) showed genome-wide significant association. Gap and SNP tests were performed at 36 020 and 97 224 core genome sites, respectively. For ceftriaxone MIC and penicillin MIC, respectively, 998 and 833 variants showed genome-wide significance Figure  , and had lower GIF than the major-allele test . Further results for the major-allele test are in Supplementary Figures S10 and S11, and for treeWAS in Supplementary Figures S12 and S13. The lists of genes identified are in Supplementary Appendix S3.As expected from the large number of associations, prediction accuracy for both MIC phenotypes is very high under 10F CV  for the beta-lactam antibiotics ceftriaxone and penicillin, finding many associations and high heritability. Prediction of MIC traits was correspondingly accurate under 10F CV.Using these innovations, we present the first genomic analyses of pbp1a, pbp2b, pbp2x) and transferases required for cell wall biogenesis (mraY and mraW for ceftriaxone MIC). A single heat shock protein (clpL) and a gene from the recombination pathway (recU) were also identified as associated , but strong evidence that it is a polygenic trait of moderate heritability that is predictable from the genome sequence .We found no reliable associations for h2 estimate of 0.45 using warped-lmm  was onlng GEMMA , which iIn summary, our results support the use of separate testing of gap and SNP effects, and wg-enet for prediction of quantitative traits. We find that LDSC performs best for heritability analyses. Further work is required to assess optimal strategies in a wider range of settings for population structure in bacterial genomes.https://github.com/Sudaraka88/bacterial-heritability.All codes, figures and accession details for the genetic data used in this analysis are available at lqac011_Supplemental_FileClick here for additional data file.


Zea mays, L.), soybean , and rice , which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize . Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.The online version contains supplementary material available at 10.1186/s12870-022-03479-y. Plant breeders observe the phenotypes of crops to choose desirable offspring in an aim to genetically improve target traits. Selection during the breeding process is a crucial step in crop breeding, with historical and current conventional plant breeding depending on phenotypic selection. The Selection-index (SI) method  selects Genomic selection provides a potential advantage in crop breeding by accelerating the genetic improvement of crops per unit time through the reduction in cost per breeding cycle and shortening of the generation interval . AdditioNumerous genomic prediction models have been developed for predicting phenotypes using large sets of genetic markers   because Several factors affect the accuracy of genomic prediction including the size and genetic diversity of the training population, trait heritability, marker density, gene or marker effects, and the extent and distribution of LD between markers and QTL. Therefore, there is an ongoing need to understand how accuracy of different models reacts among crop species that vary in the LD decay as well as how prediction accuracy is affected by marker number, training population proportion, and trait heritability. The main objective of this study was to compare the accuracy of numerous genomic prediction models for several traits that differ in heritability for three crop species with different LD decays rates and contrasting genetic architecture, as well as testing the effect of several methods of sub-setting total marker number and the interaction with training population size.13C ranged by 1.46%. In maize, the DT ranged by 30.5 days and the EH ranged by 128 cm. In rice, the PPP ranged by 1.89 panicles and the SPP ranged by 2.19 seeds  of the association between a marker and given trait by FarmCPU [Tables S2, S3, and S4  or by marker significance (four threshold subsets) and training-to-testing population proportions. We found a difference in prediction accuracy for all traits in soybean, rice, and maize using different subsets of markers at three training-to-testing proportions Fig. . Compariδ13C at all cross-validation levels. For SPP in rice, BayesB yielded the highest accuracy for all cross-validation levels. For PPP in rice, the accuracies of all models were similar for all of proportions between training and testing. For DT in maize, the highest prediction accuracy models were BayesB and RKHS at the 90:10 proportion. At the 70:30 and 50:50 proportions, the accuracies of all these models were mostly similar. For EH in maize, BayesB had the highest prediction accuracy at all training-to-testing proportions.Because the highest prediction accuracy was when using the SNP_05 subset, this subset was used to compare the different genomic prediction models Fig. . For soyP < 0.05 increased prediction accuracy in all selected traits of all crops, this subset was used to report the effect of the different training-to-testing population proportions . These trends were similar for all the different sets of marker subsets  in soybean, seeds per plant (SPP) in rice, and ear height (EH) in maize at the 90:10 training-to-testing proportion Fig. . We usedh2 for all traits using all the different combinations of markers sets and three training-to-testing proportions . , maize (Zea mays L.), and rice (Oryza sativa L.). For each crop, two traits were used that varied in broad-sense heritability (H2). For soybean, these traits included canopy wilting  and ear height  [2 = 80%) and seeds per plant  [Data sets for three plant species that vary in LD decay rates were selected for our experiments: soybean  and car2 = 60%, ). For ma2 = 65%) . Lastly 2 = 55%) .δ13C [https://www.panzea.org/data) and rice data (http://ricediversity.org/data/) were publicly available and soybean data are included herein . In soybean, SNP data were obtained using the application of Illumina Infinium SoySNP50K iSelect SNP BeadChip that provided 42,509 SNPs for all 346 accessions  than soybean (150 kb in euchromatic and 5 kb in heterochromatic regions) or rice (123 kb).Additional file  project . Qualitygenotype . The finEleven different statistical models were compared for genomic predictions. These models differ with respect to assumptions about the markers as described in Additional File r ≥ 0.90, 0.80 ≤ r < 0.90, 0.70 ≤ r < 0.80, 0.60 ≤ r < 0.70, and 0.50 ≤ r < 0.60 between alleles. For simplicity, these subsets are subsequently referred to as 0.90, 0.80, 0.70, 0.60, and 0.50, respectively. For example, if a haplotype block consisted of five SNPs that were linked with each other at r ≥ 0.90, then only one SNP out of five was kept in the subset of markers.Ten different marker subsets were compared to evaluate the effects of marker distribution across the genome on the prediction accuracy. Subsetting markers was done based on two approaches: 1) linkage disequilibrium between markers and 2) markers that met a significance threshold. Linkage disequilibrium between markers, which defines a haplotype block, was evaluated using the correlation coefficient between alleles at a pair of genetic loci. Five LD based subsets of markers were selected from the haplotype block that was made using a correlation coefficient (r) of P-value of the significant association of markers with a trait at probability levels of P < 0.5 (SNP_5), P < 0.1 (SNP_1), and P < 0.05 (SNP_05). The significant association between markers and traits was conducted using the Fixed and random model Circulating Probability Unification (FarmCPU) model [P > 0.05 (SNP_NS). The F-test for two-samples of variance was conducted to compare the significant effect between the different sets of markers.In addition to selecting marker subsets based upon haplotype blocks, we also selected subsets of markers based on the U) model . One subThree different sizes of training populations among these species were evaluated to determine the effect of the training population proportion on the genomic prediction accuracy. These three training population proportions consisted of 90 training to 10 testing set, 70 training to 30 testing set, and 50 training to 50 testing set. Random assignments of individuals to training and testing sets were repeated 10 times and the average value of the prediction accuracy are reported in this work. The correlation coefficient (r) between the GEBVs and observed phenotypic values was used to determines predictive ability (r), which was used as the indicator of prediction accuracy in this paper. This approach has been used previously by several groups , 46.h2) was estimated to understand the variation and trend of predictive ability across traits [Y = Xβ + Zu + e, where Y is the vector of observed phenotypes; β is an unknown vector containing fixed effects, including the genetic marker, population structure (Q), and the intercept; u is an unknown vector of random additive genetic effects from multiple background QTLs for individuals/lines; X and Z are the known design matrices; and e is the unobserved vector of residuals. The u and e vectors are assumed to be normally distributed with a null mean and a variance of: 2aK with σ2 a as the additive genetic variance and K as the kinship matrix. Homogeneous variance was assumed for the residual effect . The proportion of the total variance explained by the genetic variance is defined as marker-based narrow sense heritability, which was calculated for all traits using different subsets of markers and different training-to-testing population proportions.Marker-based narrow sense heritability  between markers and significant markers, in soybean for two traits including canopy wilting (CW) and carbon isotope ratio (δ13C). Table S3. Markers distribution in the different subsets of markers, which were selected based on the two methods, linkage disequilibrium (LD) between markers and significant markers, in maize for two traits including days to tasseling (DT) and ear height (EH). Table S4. Markers distribution in the different subsets of markers, which were selected based on the two methods, linkage disequilibrium (LD) between markers and significant markers, in rice for two traits including panicles per plant (PPP) and seeds per plant (SPP). Table S5. Correlations of narrow sense heritabilities and prediction accuracies of 11 different genomic prediction models for canopy wilting (CW) carbon isotope ratio (δ13C), seeds per plant (SPP), panicles per plant (PPP), and days to tasseling (DT) and ear height (EH) at different training-to-testing proportions . Table S6. Prediction accuracy of genomic models for canopy wilting (CW) and carbon isotope ratio (δ13C) in soybean, and seeds per plant (SPP) and panicles per plant (PPP) in rice, and days to tasseling (DT) and ear height (EH) in maize using different subsets of markers at the 90/10% training-to-testing proportion. Table S7. Prediction accuracy of genomic models for canopy wilting (CW) and carbon isotope ratio (δ13C) in soybean, and seeds per plant (SPP) and panicles per plant (PPP) in rice, and days to tasseling (DT) and ear height (EH) in maize using different subsets of markers at the 70/30% training-to-testing proportion. Table S8. Prediction accuracy of genomic models for canopy wilting (CW) and carbon isotope ratio (δ13C) in soybean, and seeds per plant (SPP) and panicles per plant (PPP) in rice, and days to tasseling (DT) and ear height (EH) in maize using different subsets of markers at the 50/50% training-to-testing proportion.Additional file 2: Fig. S1. Prediction accuracies for carbon isotope ratio (δ13) in soybean using BayesB model and a training-to-testing cross-validation proportion of 90:10%. Thirteen sets of marker subsets were selected based on the different significant levels including P < 0.001, P < 0.0025, P < 0.005, P < 0.0075, P < 0.01, P < 0.025, P < 0.05, P < 0.075, P < 0.1, P < 0.25, P < 0.5, P < 0.75, and complete set.Additional file 3. Zip file containing genotypic and phenotypic data files for three datasets: soybean, two traits (δ13C and CW) and 42,509 SNPs for 346 accessions; maize, two traits (DT and EH) and 50,896 SNPs for 273 accessions; rice, two traits (PPP and SPP) and 44,100 markers from two projects: OryzaSNP project and OMAP project.Additional file 4. Zip file containing R scripts used in this study.


Genome-wide association studies (GWAS) identify loci underlying the variation of complex traits. One of the main limitations of GWAS is the availability of reliable phenotypic data, particularly for long-lived tree species. Although an extensive amount of phenotypic data already exists in breeding programs, accounting for its high heterogeneity is a great challenge. We combine spatial and factor-analytics analyses to standardize the heterogeneous data from 120 field experiments of 483,424 progenies of Norway spruce to implement the largest reported GWAS for trees using 134 605 SNPs from exome sequencing of 5056 parental trees.We identify 55 novel quantitative trait loci (QTLs) that are associated with phenotypic variation. The largest number of QTLs is associated with the budburst stage, followed by diameter at breast height, wood quality, and frost damage. Two QTLs with the largest effect have a pleiotropic effect for budburst stage, frost damage, and diameter and are associated with MAP3K genes. Genotype data called from exome capture, recently developed SNP array and gene expression data indirectly support this discovery.Several important QTLs associated with growth and frost damage have been verified in several southern and northern progeny plantations, indicating that these loci can be used in QTL-assisted genomic selection. Our study also demonstrates that existing heterogeneous phenotypic data from breeding programs, collected over several decades, is an important source for GWAS and that such integration into GWAS should be a major area of inquiry in the future.The online version contains supplementary material available at 10.1186/s13059-021-02392-1. Picea abies (L.) Karst.] breeding program where large sets of phenotype data were accumulated over the past 50 years from nation-wide field plantations, and genome-wide exomes capture polymorphism to identify genes involved in the variation of growth, phenology, and wood quality traits. Today, one of the main limitations of GWAS is the availability of phenotypic data or, in the cases when those data have been collected from existing breeding programs, their large heterogeneity. Coping with the heterogeneity of these data and using them efficiently is therefore a major challenge. However, success in doing so could unleash a large amount of information gathered over the years and partly help to overcome the dearth of phenotypic data.Understanding how phenotypic traits are genetically controlled has been, and will likely remain for years to come, one of the central challenges in biology. Decades of quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) have detected a bounty of loci associated with the variation of quantitative traits in organisms ranging from humans to crops –3. TheseNorway spruce is the most economically important conifer species in Europe , especiaIn this study, we conducted a GWAS for budburst stage (BB), frost damage (FD), tree height (Height), diameter at breast height (DBH), and wood quality traits in Norway spruce using data from the Swedish long-term breeding program Table . These hP<1.4 × 10-7). These SNPs were distributed across 24,104 contigs (P. abies v1.0 annotation) [Using exome sequencing with customized probes , 10.6 M otation) . The meaotation) , PCA detp<10-6, Additional file Fst among seven genetic clusters are shown in Table S4. The NFE and ALP domains were the most divergent (Fst =0.026), and the ROM domain was closest to NPL. The CSE cluster was much closer to the Rus-Bal cluster than to NFE. The pedigree-based narrow-sense heritabilities of these traits estimated from two full-sib progeny tests were 0.41, 0.45, and 0.44, respectively  or adjusted phenotypic values of the seven traits showed an approximately normal distribution  [r2) rapidly to 0.2 within 37 base pairs (bps) for contigs harboring significant SNPs  with each other, the number of independent association signals (SNPs) for seven traits was 55  , DBH (n=4), FD (n=3), WD (n=4), MFA (n=1), and WS (n=4).There were strong population genetic structure and phenotypic differences between genetic clusters, especially between NFE  and other genetic clusters. The phenotypic values of growth and phenology traits vary clinally and at least three of the seven genetic clusters have enough data for performing GWAS with more than 800 individuals each Table . Thus, wl (CMLM)  implemenl (CMLM) . One of n=4138), while one SNP (MA_798143_7416) was only detected by CMLM in the CEU cluster, and seven associations identified by BLINK were specific to some of the genetic clusters: CEU (n=1), CSE (n=1), and NFE (n=5). More associations were identified by BLINK (n=24) than by CMLM (n=10) in the whole population as shown in Manhattan plots and pair-wise LD r2 in Fig. n=1428). Only one association (MA_12842_2490) overlapped between BLINK and CMLM.For BB, we identified 40 independent associations, 31 from BLINK, and 11 from CMLM, with two of them  overlapping . Only SNP MA_93306_19178 is unique to BLINK. Four SNPs detected from CMLM completely overlapped with those detected from BLINK, resulting in four associations in total.For WD, we detected four association signals in the whole population and two in the ALP cluster  were found to be associated with three traits  simultaneously  in the whole population   were evaluated using two full-sib progeny plantations established in northern Sweden , five (33%) of the total 12 SNPs associated were located outside of the gene regions (Table S11), comparable with 49% SNPs selected outside of the gene regions in the 50K SNPs array.Using our recently developed 50K SNP array developed from the whole genome sequencing data , we founIn total, there were 43 annotated genes located on 37 contigs underlying SNPs significantly associated with 40 independent SNPs for BB  detected in cluster ALP for WD encodes serine/threonine kinase and is involved in the regulation of cell population proliferation and cell expansion to maintain organ growth [We identified 12 SNPs, located in 10 contigs harboring ten genes, associated with the wood quality traits including WD, MFA, and WS  and explained 0.56% of the total genetic variance , located on the same contig MA_12842, were simultaneously associated with BB, FD, and DBH forming a single independent signal  -7 . In annual plants, however, a clear pattern has emerged indicating that the association signal of common variants in large sample sizes, although spread across the entire genome, is heavily concentrated in regulatory DNA in open chromatin marked by deoxyribonuclease hypersensitive sites . This coPinus taeda L.) and radiata pine (Pinus radiata D. Don.) [Genotyping in the two most commercially important angiosperm tree species  is relatively well developed due to their small and high-quality reference genomes (400–600 Mb) –41. HoweD. Don.) , 46. ThiD. Don.) . AnotherTwo aspects that affect the power of a GWAS are the overall genetic structure of the studied population and the genetic relationships among the tests from which the phenotypic data originate. Ideally, GWAS are conducted on a set of unrelated individuals that have been accurately phenotyped. In this respect, progeny field plantations in tree species, particularly in conifers, are suitable because progenies are at the same age and environmental variation is minimized through a proper field experimental design . Addition=30) and the trait BB for the whole population (IF=1.09). This means that BLINK which uses putative QTNs as covariates in the model already control well for population structure. In the CMLM method, 0–3 PCs were used to correct population structure. Based on the QQ plot and IF  found in a previous study using a subset of data (n=808) [Replication will increase the confidence of GWAS association signals and is commonly used in human genetics studies . Replica (n=808)  and the  (n=808) . It may A very important step in GWAS is to validate the candidate genes by the genetic transformation if the studied species can be transformed. Unfortunately, in conifer, a genetic transformation system is so far not developed and transformation in spruce itself has seldom been used  and remA. thaliana, MAP3Ks are induced by various abiotic stresses, including cold and drought. In our study, the late budburst and less FD trees all had a high expression level of the MA_12842g0030 in RNA-Seq data. We observed that trees with less expression of MAP3K showed a decrease of more than 6% for height and DBH in the long-term field plantation. Unfortunately, there is no matured conifer transformation system so far to further verify the role of MAP3K in controlling budburst.To tolerate cold temperature, plants must be able to perceive a cold signal and transduce it into downstream components that could induce appropriate defence mechanisms . In thisBased on predictions from climate modeling, increased growing season of plants in the Northern Hemisphere from global warming may induce earlier budburst with more frequent FD , 62. ConIn the present study, we used a large data set from 120 progeny tests and newly collected data from clonal archives to carry out the largest association study in Norway spruce. Through factor-analytic and spatial analyses, we made the combination of a diverse and heterogeneous dataset of different ages, sites, and pedigrees with a minimum genetic connection possible to improve the accuracy of phenotypic value for GWAS. While the total number of significant SNPs identified for growth and wood quality traits was limited, we were able to detect more SNPs for phenology traits. It also shows the value of ascertaining the identified SNPs through both replication and verification steps. Indeed, our GWAS study succeeded in identifying, replicating, and verifying through a gene expression study the association between the cold tolerance genes MAP3K and bud burst. Since the SNPs associated with cold tolerance are also associated with tree growth and DBH, by improving cold tolerance, growth, and productivities of forests could be simultaneously increased.We used 483,424 progeny trees and their 5056 unrelated parents in the Swedish Norway spruce breeding program to dissect the genetic basis of phenology, growth, and wood quality traits. These plantations belong to the breeding program managed by the Forestry Research Institute of Sweden (Skogforsk) over the past 50 years which started with elite tree selection and continued with the establishment of progeny tests. The 5056 parents were genotyped using exome capture and either phenotyped directly for some traits or, for other traits, phenotypically characterized through their predicted breeding values obtained from 482,054, predominantly open-pollinated progeny trees, at 120 progeny tests across Sweden  and 914 trees were genotyped by exome capture and a recently developed SNP array, respectively , and (7) removing individuals with call rate < 50%. After these filtering steps, a total of ~300K SNPs with MAF > 0.005 were left for population structure analysis. In our previous study [To improve the quality of called SNPs, several filtering steps were performed: (1) removing indels, (2) keeping only bi-allelic sites, (3) marking sites with a genotype quality (GQ) < 10 as missing, (4) removing sites with call rate (“missingness”) < 70%, (5) removing sites with minor allele frequency (MAF) < 0.005, (6) removing SNPs with an excess of heterozygotes and deviation from Hardy-Weinberg equilibrium test (HWE-test) for the whole elite tree population , following best practice with default parameters . After filtering the SNPs with MAF <0.05, 37819 SNPs were kept for downstream analysis.We used Beagle v4.1 to impute the missing genotypes . To estir2) exceed 0.2 in blocks of 20 SNPs using PLINK 2.0 [Fst index estimates among subpopulations/genetic clusters defined by PCA and missForest function were estimated using VCFtools [To infer population structure among the sampled individuals, we used linkage disequilibrium (LD)-trimmed SNPs by removing randomly one SNP from each pair of SNPs when the squared pairwise correlation coefficients , two phenology traits (BB and FD), and three wood quality traits, wood density (WD), microfibril angle (MFA), and wood stiffness (WS). The three wood quality traits and BB were measured directly on the elite trees in the three common gardens, and GWAS were performed on adjusted phenotypic measurements after removing environmental effects (see below). For the three remaining traits, height, DBH, and FD, estimated breeding values (EBVs) of the elite trees were predicted from 120 progeny tests across Sweden. Importantly, these progeny tests were unevenly distributed, depending on the trait (see below). Furthermore, not all seven traits’ measurements were available for the 5056 elite trees and the validation progeny population of 1370 trees. Table For height, 330,966 progeny trees generated from 3838 parental elite trees and grown in 115 progeny plantations were phenotyped at ages varying from 6 to 16 years. DBH was measured on 213,931 progeny trees at age of 10 to 28 years. These progenies descended from 2674 parents and were planted in 75 progeny tests. The 115 plantations for height measurement were distributed across Sweden while 75 plantations (70 overlapped with height measurement) for DBH measurement were distributed in southern and central Sweden  in 4127 elite trees with ca. three ramets/clone  each and Hitman ST300 instrument  in 4122 elite trees with ca. three ramets each, respectively, in 2016. The WD was measured using depth of needle penetration of the Pilodyn with a 2.0-mm diameter pin, without removing the bark; therefore, WD values represented by penetration depth would be lower when wood density is higher. The MFA was determined by acoustic velocity from the Hitman ST300 measurement. The third trait of WS, also called the modulus of elasticity, was estimated using the equation by combining the Pilodyn penetration and acoustic velocity data [Three wood quality traits and BB were measured in the three common gardens  at each of three clonal plantations (Table S17).To improve the accuracy of response variables used for GWAS, we did three steps of preparation for height, DBH, and FD. The three steps are as follows:We used spatial analysis to correct for environmental effects for height, DBH, and FD traits . The detTo predict the estimated breeding values (EBVs)  and adjuy is a vector of the spatially adjusted observations, τ is a vector of fixed effect, including the grand mean, plantation, and age, e is a vector of random residual terms. X and Fp are known incidence matrices for fixed effects and parents, respectively. The random effects in the model are assumed to follow a multivariate normal distribution with mean and variance defined by:where App is the numerator relationship matrix of parents; I is the identity matrix, with order equal to the number of trees; and i at plantation j can be expressed as follows:where 0 is a null vector; k multiplicative terms. Each term is the product of an additive genetic effect (k of the FA models is the number of factors (multiplicative terms), and we denote an FA model with k factors as a FAk model. To obtain main additive genetic effects for all parents and also additive genotype by environment effects, an additive effect ua = m + me, where m is the additive main additive genetic effect, and me is the additive genetic-by-environment effect. var(ua) =  var (m) + ɅɅT + Ψ, where Ʌ is a matrix of loadings and Ψ is a diagonal matrix with diagonal elements referred to as specific variances. The model is referred to as the FAMK model [k+1) factors, where the first set of loadings are constrained to be equal.which includes a sum of MK model , which iTo predict EBVs of FD, a logit function was used to transform the binary variable, FD and then a generalized linear mixed model was employed in ASReml V4.1 . The deti was obtained asc is the proportion of variance not accounted for by the markers (here assumed to be 50%) [Using EBVs as phenotypes in genomic prediction may introduce bias and heterogeneity ; thus, d of EBVs . The res be 50%) .Two steps of preparation were used for BB and the three wood quality traits before performing a GWAS. First, we performed a linear mixed model including blocks as fixed effects to adjust for environment effects in each common garden. The model was as follows:y is the vector of observations, X and Z are the incidence matrices related to fixed effects (the grand mean and block effects) in vector β and random effects in vector u (genotype effects) assuming e is the vector of residual effects assuming where cor function in R 3.5.3 [Genetic parameters were estimated using ASReml V4.1 . Details R 3.5.3  and ASReTo identify significant SNPs associated with each trait, GWAS was carried out for each of the seven phenotypes using 134,605 SNPs (the MAF > 0.03 to account for the fairly large size of the population) with two methods (BLINK and CMLM) for the whole population and for the large genetic clusters defined by PCA.p values compared with the distribution of the expected p values following a uniform distribution. We found that BLINK model with fitting one PC as a covariate or even without fitting PCs is effective in accounting population structure  [LDheatmap function implemented in the R package LDheatmap [r2). Independent SNPs were defined by pairwise SNPs in different contigs or with LD r2 < 0.2 in the same contig. Proportion of variance explained by a given SNP (PVE) and cumulative phenotypic variance (R2) explained by all associated SNPs was calculated according to the method described in [One of the main aims of both BLINK and CMLM is to consider cryptic relationships among individuals and control for false-positive detection risk. Thus, here we only needed to consider the effect of population structure for GWAS. We tested both methods without additional corrections than the ones inherently included in the method, or by including 1 to 3 PCs to control for population structure confounding effect. To assess the efficiency of the methods used here in controlling for population structure, we used the genomic inflation factor (IF). The latter expresses the deviation of the observed a <1.05)  and CMLMa <1.05) . The LDhDheatmap  was usedribed in  using a The 1370 full-sib progenies from 55 parents/elite trees and established in two field plantations (Vindeln and Hädanberg) in northern Sweden that were genotyped in a previous study  were useThe 914 full-sib progenies from 32 control-pollinated families with 49 parents/elite trees and established in three field plantations in southern Sweden that were genotyped by the recently developed SNP array  were useP. abies SnpEff database. To assess the variant effect of the associated SNPs, annotation of the putative genes and their associated orthologs was performed using the P. abies v1.0 genome on ConGenIE database (http://congenie.org/). Candidate gene with an associated SNP inside was extracted from ConGenIE. Due to the fast LD decay with averaged r2 values dropping to 0.2 at 37 base pairs (bps) within all contigs and 67 bps within all contigs harboring associated SNPs  searches, using the option for only highly similar sequences (MEGABLAST) in the National Center for Biotechnology Information (NCBI) nucleotide collection database (https://blast.ncbi.nlm.nih.gov/Blast.cgi), were performed to manually evaluate possible candidate genes.Significantly associated SNPs at 5% false discovery rate (FDR) were annotated using SnpEff 4.3 with default parameters . The genThe pooled libraries were sequenced on the Illumina NovaSeq 6000 System (2*150 bp) using the paired-end module. The raw reads were processed using fastp . The aveAdditional file 1: Supplementary Table S1-S17.Additional file 2: Supplementary Figure S1-S13.Additional file 3: Supplementary Methods.Additional file 4. Review history.


We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGG There is a variety of phenotypic definitions of aggressive behavior (AGG), from broadly defined externalizing problems to narrow definitions like chronic physical aggression . Generalrgs between AGG at different ages, by different raters and instruments, and test how AGG assessed by multiple raters differ in the rg with other phenotypes.Despite a heritability of roughly 50% , genome-rgs with a preselected set of external phenotypes from multiple domains—with a focus on psychiatric and psychological traits, cognition, anthropometric and reproductive traits, substance use, and classic biomarkers of AGG, including testosterone levels. Second, meta-analyses were done by rater, instrument, and age. We estimated rgs across these assessments of AGG. To identify context-specific genetic overlap with the external phenotypes, rgs were also estimated between rater-specific assessments of AGG and the external phenotypes.Data on AGG from parent-, teacher-, and self-report in boys and girls were collected in 29 cohorts from Europe, USA, Australia, and New Zealand with 328 935 observations from 87 485 participants, aged 1.5–18 years. First, we combined all data to produce the largest GWAMA on childhood AGG to date. SNP-based association tests were followed up by gene-based analyses. We computed polygenic scores (PGSs) to test the out-of-sample prediction of AGG to explore the usefulness of our GWAMA in future research . To asseExtended description of the cohorts and phenotypes is supplied in the Supplementary text and Supplementary Tables In total, 29 cohorts contributed 163 GWASs, based on 328 935 observations from 87 485 unique individuals (Supplementary Table Neff) was approximated by the third formula in Table Neff is equal to the sum of sample sizes.Within cohort measures of AGG may be dependent due to including repeated measures of AGG over age and measures from multiple raters. To account for the effect of sample overlap, we applied a modified version of the multivariate meta-analysis approach developed by Baselmans et al.  see Tab. Insteadoverall). Second, we meta-analyzed all available data within rater (rater-specific GWAMAs). Third, rater-specific age-bins were created for mother- and self-reported AGG based on the mean ages of the subjects in each GWAS (age-specific GWAMA). To ensure that the age-specific GWAMAs would have sufficient power for subsequent analyses, age-bins were created such that the total univariate number of observations (Nobs) exceeded 15 000  we obtained (1) a list of 18 087 genes and their start- and end-positions, and (2) pre-formatted European genotypes from 1000 Genomes phase 3 for the reference LD. We applied a Bonferroni correction for multiple testing at α = 0.05/18 087. A lookup for significant results was performed in GWAS Catalog and PhenoScanner (see URLs).For AGGin MAGMA . The genN = 4491), which represents the largest NTR univariate stratum. In the QIRM participants, we tested whether our childhood AGG PGS predicted adult retrospective assessment of their own CD behavior during adolescence (N = 10 706). We allowed for cohort-specific best practice in the PGS analysis. In the NTR, we created 16 sets of PGSs in PLINK1.9 [P value thresholds between 1 and 1.0E–05  and MAF below 1% and selected the most significant independent SNPs using PLINK1.9 [r2 = 0.1 within windows of 10 MBp). We calculated different PGS for seven P value thresholds  of the GWAS summary statistics. PGS were calculated from the imputed genotype dosages to the 1000 Genomes (Phase 3 Release 5) reference panel. We fitted linear mixed models, which controlled for relatedness using a Genetic Relatedness Matrix (GRM) and covariates sex, age, two dummy variables for the GWAS array used, and the first five genetic principal components. The parameters of the model were estimated using GCTA 1.9 [All data were meta-analyzed twice more, once omitting all data from the Netherlands Twin Register (NTR) and once omitting the Australian data from the Queensland Institute for Medical Research (QIMR) and the Mater-University of Queensland Study of Pregnancy (MUSP). As the NTR target sample we considered mother-reported AGG at age 7 . Genomic Structural Equation Modelling (Genomic SEM) [rgs were significantly different across raters. Specifically, for every phenotype, we tested whether (1) all three rgs between the external phenotype and rater-specific assessment of AGG, i.e., mother, teacher, or self-ratings, could be constrained at zero, and (2) whether rgs could be constrained to be equal across raters. A rgs between the phenotype and three rater-specific assessment of AGG were allowed to differ. We applied an FDR correction at α = 0.05 over two models for 46 external phenotypes, for a total of 92 tests. An FDR correction for 4 × 46 = 184 tests was applied to correct for multiple testing of whether the genetic correlations were significantly different from zero.We computed SNP2 ≥ 4 . Next, wmic SEM)  was applNeff of 151 741, the overall was estimated at 3.31% (SE = 0.0038). The mean overall  is provided in Supplementary Table P = 2.0E–07). The SNP is located inside a gene desert, without any gene in 400 Kbp in any direction. The second strongest independent association was found with rs113599846 (P = 4.3E–07), which is located inside an intronic region of TNRC18 on chromosome 7. None of the suggestive associations have previously been reported for AGG or AGG-related traits [We first meta-analyzed the effect of each SNP across all available univariate GWASs. Assuming an overall. We restricted lookup to associations with autosomal SNPs that were found in samples of European ancestry, resulting in three loci. One genome-wide significant hit was reported for adult antisocial personality disorder  [overall . Tielbeek et al. [overall (both P > 0.5).We tested previously reported genome-wide significant associations for AGG  and perf.64E–09) . The samk et al.  reportedST3GAL3 , PCDH7 , and IPO13 . ST3GAL3 codes for a type II membrane protein that is involved in catalyzing the transfer of sialic acid from CMP-sialic acid to galactose-containing substrates. ST3GAL3 has been implicated in 107 GWASs, most notably on intelligence and educational attainment. The top SNP in ST3GAL3  is in strong LD (r2 > 0.8) with several other SNPs inside the gene body of ST3GAL3 and in moderate LD (r2 > 0.6) with SNPs in several neighboring genes  is in strong LD (r2 > 0.8) with a small number of other closely located SNPs and the signal for the gene-based test appears to be driven by two independent loci  is in moderate-to-strong LD with multiple SNPs . In general, the highest positive correlations were seen with psychiatric traits, notably ADHD, ASD, and major depressive disorder (MDD). The largest negative genetic correlations were found for age at smoking initiation, childhood IQ, and age at first birth. Based on the biomarker-aggression literature, we tested for the presence of genetic correlations between AGGoverall, and lipids, heart rate, heart rate variability, and testosterone levels. Very low genetic correlations were observed for AGGoverall, and these biomarkers, with in many cases the sign of the genetic correlation opposite to what was expected based on the literature on biomarkers of AGG.Genetic correlations between AGGrg between rater-specific assessment of AGG, except for father-reported AGG, which returned a non-significant Mother and AGGTeacher . Moderate genetic correlations were observed between AGGSelf and AGGMother , and between AGGSelf and AGGTeacher . Both genetic correlations involving self-reported AGG were significantly lower than 1.Separate meta-analyses were carried out for raters, instruments and age. None of these GWAMAs returned genome-wide significant hits. Manhattan plots for the four rater-specific GWAMAs are shown in Supplementary Fig. ASEBA) and another GWAMA across all GWASs for the SDQ (AGGSDQ). SNP-heritabilities for AGGASEBA and AGGSDQ were 0.031 (SE = 0.0099) and 0.026 (SE = 0.0086), respectively. The GWAMAs were insufficiently powered to estimate rg across instrument-specific assessment of AGG.We performed a GWAMA across all GWASs where an ASEBA scale was used  compared to teacher-  and mother-reported AGG .Next, we tested for each external phenotype whether the three rater-specific PCDH7, ST3GAL3, and IPO13, based on the overall meta-analysis , which did not return genome-wide significant SNPs. Lead SNPs in the implicated genes were related to educational outcomes, but did not reach genome-wide significance and these loci require further evidence before being considered as AGG risk variants. PGS predicted childhood AGG and retrospectively assessed adolescent CD. Stratified analyses within AGG generally returned moderate-to-strong genetic correlations across raters. We found substantial genetic correlations between AGGoverall and a list of preselected external phenotypes from various domains, including, psychiatry and psychology, cognition, anthropometric and reproductive traits. Most notably was the perfect rg between AGGoverall and ADHD . This is in line with the moderate-to-strong phenotypic correlations that have consistently been found across sex-, rater-, age-, and instrument-specific assessment of AGG with attention problems and hyperactivity [rg = ~−0.5) were found with all three traits from the cognitive domain. Genetic correlations were positive with smoking initiation  and smoking quantity , and negative with age at smoking initiation .We present the largest GWAMA of childhood AGG to date. The gene-based analysis implicated three genes, activity . Signifirg between AGGoverall and LDL , which has an opposite sign than what was expected based on the literature [rg = −0.13; SE = 0.07), all measures of lipid levels returned significant positive rgs with AGGoverall, albeit weakly (rg < 0.2). No heart rate measure showed a significant genetic correlation with AGGoverall. The relationship between testosterone levels and (childhood) AGG in the literature is, at best, unclear. A positive association between AGG and testosterone is often assumed, but the relation may be more complex [rgs between AGGoverall and testosterone levels in males and females  and having offspring earlier in life , tending to confirm that not all associated outcomes are harmful.We examined genetic correlations with classical biomarkers of AGG. Higher levels of aggression have been associated with lower levels of LDL  and loweterature . More br complex . Both po complex . We foun Biobank , and gen Biobank . GeneticMother and AGGTeacher . However, the 95% confidence interval covers 1, which makes these results hard to reconcile with previous findings of rater-specific additive genetic effects in childhood AGG [rgs with mother-, self-, and teacher-reported AGG. For ADHD, ASD, MDD, schizophrenia, well-being, and self-reported health, rgs differed significantly across raters. Weaker rgs were consistently found in teacher-reported AGG compared to mother- and self-reported AGG. These findings indicate the presence of rater-specific effects when considering the genetic correlation of AGG with other outcomes. rgs are generally stronger in the psychopathology and psychological domains. A lack of power, however, seems insufficient to explain why we found weaker rgs between AGGTeacher and phenotypes from these two domains. Other phenotypes, like smoking behavior, educational attainment or age at first birth, are, like psychopathological phenotypes, highly genetically correlated with AGGoverall, but, unlike psychopathologies, have near identical rgs across raters. The rater-specific effects on rgs between childhood AGG and external phenotypes might be limited to psychopathologies, and future research into the genetics of childhood psychopathology might consider these nuances in effects of assessment of childhood AGG from various sources, be that multiple raters, instruments, and ages.The stratified design of our study also allowed for examination of the genetic etiology of AGG in subsets of the data and examination of genetic correlations among raters. We found a high genetic correlation between AGGhood AGG . Most exDespite the considerable sample sizes, we were still underpowered to compute genetic correlations with external phenotypes while stratifying AGG over age or instrument. Age-stratified GWASs in larger samples across development are a desirable target for future research. Because genetic correlations can be computed between phenotypes for which a well-powered GWAS is available, age-stratified GWAS of many developmental phenotypes, behavioral, cognitive, and neuroscientific can be leveraged to better understand development of childhood traits.We note that multivariate results should be interpreted with some caution. While combining data from correlated traits can indeed improve power to identify genome-wide associations, interpreting the phenotype may not be straightforward. In the current GWAMA, we have referred to our phenotype as “aggressive behavior” and interpreted the results accordingly. AGG, however, is an umbrella term that has been used to identify a wide range of distinct—though correlated—traits and behaviors .overall and other traits. A limiting factor in this regard is the relatively low SNP-heritability, which puts an upper bound on the predictive accuracy of PGSs. Since measurement error suppresses SNP-heritability, better measurement may offer an avenue to higher powered GWAS, and subsequently to better PGS. Furthermore, sample sizes for developmental phenotypes, including AGG, may need to increase by one to two orders of magnitude before PGS become useful for individual patients.GWASs are increasingly successful in identifying genomic loci for complex human traits  and alsorgs with other complex traits. Non-significant associations are expected to capture part of the polygenicity of a trait [rg) provide insight into the genetic overlap between complex traits [Despite our extensive effort, the first genome-wide significant SNP for childhood AGG has yet to be found. Even in the absence of genome-wide significant loci, however, GWASs aid in clarifying the biology behind complex traits. Our results show that, even without genome-wide significant hits, a GWAS can be powerful enough to illuminate the genetic etiology of a trait in the form of  a trait  and vari a trait . PGSs ag a trait . Similarx traits , 50.http://www.action-euproject.eu/content/data-protocolsGWAS SOP: https://ctg.cncr.nl/software/magmaMAGMA: https://www.snp-nexus.org/index.html (Accessed 28 Aug 2019)SNPnexus: https://www.ebi.ac.uk/gwas/ (Accessed 29 Aug 2019)GWAS Catalog: http://www.phenoscanner.medschl.cam.ac.uk/ (Accessed 29 Aug 2019)PhenoScanner: Supplementary textData Set 1