Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured  and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less . Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice ( The particularity of RS breeding in rice as performed in various countries in LAC is that it uses a recessive nuclear male sterility (ms) gene to facilitate outcrossing . Subsequently, parental lines are selected to be used for the next recombination cycle. Among others, two compromises have to be made that have a direct impact on the genetic gain achieved by the RS breeding scheme: (1) the number of candidate units evaluated through progeny testing with direct impact on the selection intensity and; (2) the required degree of fixation of those progenies prior to phenotyping, which would affect the breeding cycle length and influence the precision of genetic variance estimates.Population improvement strategies are recognized as methods to exploit the genetic diversity of a crop and enrich the genetic basis of breeding programs. In rice, population breeding through recurrent selection (RS) was suggested as a valuable option in countering the decline in genetic diversity among the improved rice germplasm from Latin America and the Caribbean LAC;  and late ms gene . The mos0 individuals could allow a more precise estimate of the genetic variation in the case of early generation segregating candidate units. GP was simulated on multiparent populations (0 progenies to save time in building the models and to accelerate the recycling of the selected germplasm.Since its introduction by ulations  and maizGP integrating genotype by environment interaction (GxE) has proven successful, showing greater PA than the single environment prediction, provided environments are positively correlated. An approach to multi-environment GP was proposed and applied by This study was conducted in the context of a collaborative rice breeding program between CIAT  and Cirad . The CIAT-Cirad rice breeding program has historically conducted population development and improvement through RS. Its current RS program based on progeny testing is conducted in two locations; at CIAT-HQ in Palmira, where rice is cultivated all year round under irrigated conditions, and in Santa Rosa (SRO), an experimental site where rice is grown under rainfed conditions during the main cropping season. Although aiming to implement early GP in our RS scheme, we were also interested in making optimal use of all the data gathered in both locations (target and not target) for the breeding program. The main objective was to evaluate the PA of the GP model including the GxE interaction to obtain reliable estimates of the breeding value of selection candidates in the target site.Oryza sativa L.). Several synthetic populations developed in the CIAT-Cirad rice breeding program were improved for adaptation to upland ecosystems and acid soils. In 2015, 2:4 lines derived from four populations to study the potential of GP in an RS scheme. Of the 348 families at the S2 generation, marker-assisted-selection for the ms gene . The degree of allelic fixation varied greatly between the genotypes but remained relatively low for individuals at the S0 generation (Supplementary Table S1). Considering the rather large average LD (Supplementary Table S2) and the slow LD decay observed , the average marker density (1 SNP every 40 kb) was considered good enough to allow the capture of all linked QTLs with the SNP matrix in hand. The whole population was characterized, with a total absence of structure, which provides a good base for setting up a GP scheme through CV .Leaf tissues were sampled on the 334 SField phenotyping was performed at two locations in Colombia. One site was an experimental field at CIAT-HQ in Palmira (PAL) located in the Valle del Cauca, Colombia . At this location, rice evaluation trials are conducted under irrigated conditions and can be performed all year round due to favorable environmental conditions and good water availability that enable the irrigation scheme throughout the crop cycle. As it is not a rice prone area, no severe disease pressure is naturally present, and the rice crop usually expresses its full potential. On the other hand, SRO is an experimental site, owned by the Colombian National Federation of rice growers (Fedearroz) located in the Oriental plains of Colombia, in the department of Meta, Colombia . At this site, the rice crop is established through direct seeding and the trials are conducted under rainfed conditions during the main cropping season, between May and September. The predominance of rice cultivation in this area, the climatic conditions of hot and humid summers during the main growing season and the natural occurrence of various strains of pathogens  make this site a hot spot for disease screening.0:2 in the 2017 trials and S0:2 and S0:3 lines in the 2018 trials) were randomly distributed across the design within each repetition of the two sites and 2-year trials. In PAL, the trials were established after transplanting 3-week-old seedlings in a bundled field. The plot size was two rows of 17 plants with 25 cm between plants and between rows. Fertilizer application followed a split application with N, P and K nutrients added at 25 and 35 days after transplanting. Irrigation was maintained continuously in order to ensure a 25 cm layer of water in the field until a week prior to the crop maturation period. In SRO, the trials were established by direct sowing of two 4-m long rows, spaced by 26 cm at a density of one gram of seed per linear meter. Split fertilizer application was performed according to the recommended application for growing tropical japonica rice in upland soil conditions. Phytosanitary treatment was applied in SRO to prevent blast outbreaks. For all four trials, a similar design was applied, but with a different randomization.Four trials were conducted during two consecutive years, 2017 and 2018, using different semesters for each location. Field trials were established in PAL on December 4, 2017 and December 10, 2018 and in SRO on December 5, 2017 and May 30, 2018. At each site, the experimental design followed a lattice with 16 blocks and three repetitions. The 50 temporal check lines  for the genotype effect and genotype by site interaction  cycle based on the BLUP-adjusted phenotypes of the sampled training set, as in To compute the variance structure  was used as a measure for repeatability and computed using the formulaModels 1 and 2 with identity matrix as variance–covariance matrices were used to compute broad sense heritability. NR represents the harmonic mean number of plots per genotype. The global H2 per generation (considering the two sites for each generation) was computed with Model 2 following the formula:, where NE is the harmonic mean of the number of sites per genotype, NR is the number of plots per genotype across both sites and H2 was based on the genetic variance and the mean interaction variance and mean residual variance across the two sites . Various calibration set sizes (s) were tested, s ϵ {25, 50, 100, 200}.In the first instance, only phenotypic data from the target site of selection SRO was considered . In that0 families. Sets of “s” S0 were selected and their phenotypes in both sites were used for the training , the remaining families being observed in only one of them. An overlap of 50% of the total number families included in the calibration was targeted. For “s” S0 families observed in both sites, the total number of S0 families was then For the two-site CV procedures, Model 2 was used. Calibrations were constructed with either a balanced (BAL) or imbalanced (IMB) representation of both sites. BAL1 represents a calibration method where both sites were represented by an equal number of phenotyped Straining . This cod in SRO .0 included in the validation set, defined as the set for which no phenotype at SRO was recorded. In each scenario, 100 alternative samplings were performed for which the PA was measured as SRO or The same CV procedures were applied to each generation and with both GP models (GBLUP and RKHS). The GEBVs in SRO were obtained for the STo ensure that the variation in accuracy between the CV procedures was only due to the size differences in the training set, the correlations were always computed on the predictions for 100 genotypes randomly selected from the validation sets. However, for BAL2 with a training set size of 200, the validation set was reduced to 34 genotypes as those were the only genotypes with no phenotypic records that could be used for the validation with this strategy (Supplementary Table S3). The PA was still computed, but as the correlation was computed on only 34 points, the results must be considered with caution.2, as Eta2 = SSqeffect/SSqtotal, where SSqeffect is the sum of squares for the effect under consideration and SSqtotal is the total sum of squares of all effects, errors, and interactions in the ANOVA study. Throughout the text, this ratio is expressed as a percentage.To investigate the response of the PA to the calibration parameters, linear models were fitted to the PA obtained from the 100 iterations with each scenario. Depending on the scenario, the independent variables were year, GP method, CV scenario, training set size, and all their combinations. Proportion of variance associated with one or more main effect, errors or interactions were estimated through the Eta0 progeny at two generations. In each site, the phenotyping was done in 2017 for 334 families at the S0:2 generation and in 2018 for the same 334 families at the S0:3 generation.The phenotypic data were collected in two sites and on the same S0:2 families evaluated in 2017 had later flowering, shorter PH, lower yield and higher zinc concentration in SRO than in PAL. However, this tendency did not hold for the 2018 trials. The differences between sites in PH were greater at the 2018 trials, with taller S0:3 plants in SRO. For each trait, the spread of the data was consistent across site and year with 0.4–4 points of difference in the coefficient of variation. The highest coefficient of variation was observed for YLD in 2018 (34%), and was higher than in the 2017 trial (27%). The trait broad sense heritability (H2) at trial level showed large differences between traits and across sites and years. This measure of trial repeatability ranged from 0.52 for YLD in the PAL_2017 trial to 0.96 for FL in SRO_2017. Heritability was systematically higher in SRO than in PAL, and similar or slightly increased in the 2018 trials for all traits in all locations, but for FL and YLD measured in SRO.For most traits recorded in the two locations, the mean phenotypic values differed between sites . AlthougAs the year and the generation effect were confounded, 50 temporal checks were used to untangle the potential effects of generation and year. The significance of the fixed effect and variance decomposition among the 50 temporal checks showed that differences were exclusively due to year effect and neither a significant generation effect nor a significant genotype by generation interaction could be observed (Supplementary Table S4).PAL and GxSSRO) only for FL in 2018, and PH recoded in both years. As a result, greater heritability was observed for these traits/year combination with H2 = 0.57, 0.50, and 0.62 for FL_2018, PH_2017, and PH_2018, respectively. The lowest genotype contribution to the explanation of variance was encountered for YLD, with large interaction effects and error effects associated with a particular site for each year, resulting in low H2 in both years . For ZN, the genotype effect represented a third of the combined GxS interaction variances in both year trials, leading to similar and moderate H2 for both years . The variance decomposition for each trait was coherent with the site correlation observed within years (r = 0.62) and FL (r = 0.62) in 2018. For the same traits in 2017, the correlations were lower . The site correlation was the lowest for YLD in both years (r between 0.13 and 0.20) and intermediate for ZN with comparable values in both years .For each trait scored in each year, an analysis of the variance components was performed on the combined data from both sites using Model 2 . The proin years . The higSRO . The year factor described a lower proportion of the total explained variance, with a maximum of 11% of the explained variance in PA for FL. For all traits GP model explained only a very limited proportion (<1%) of the variance. For most traits , the average PA was greater when predictions were performed with the GBLUP model. For this reason, the rest of the article will focus on the results achieved with the GBLUP model. However, the results for RKHS can be found in Supplementary Material (Supplementary Table S8).The training set size accounted for most of the PA variance explained by the model for all the traits. The largest training set size greatly improved the PA for all the traits (EtaSRO including only SRO data to investigate the combined effect of the training set composition and its size (Two CV scenarios including SRO and PAL (BAL1 and BAL2) were compared with SINits size . The calSRO allowed us to assess the effect due to the addition of phenotypes from PAL to the training set. Globally, across all set sizes, PA in the BAL2 scenario was greater for all the traits considered (Supplementary data Table S6), with average PA ranging from 0.23 for ZN to 0.38 for PH.When the two sites were included in the training set, the main source of variation was the number of phenotypes from SRO and PAL included in the training set. Comparing the PA associated with the set size “s” in the case of BAL1 and BAL2 with the PA obtained with the same “s” in SINAlthough training set size was the factor explaining most of PA variation (>22%) for all traits, year effect had some importance (11%) but only for FL. CV methods on the other hand accounted only for a small fraction of the PA variation. The highest gains in PA provided by any two-site CV scenarios compared with the single-site model were obtained for the training set size of 50 to predict PH_2017 (PA increase of +0.07) using the BAL2 model.So far, we have compared single-site prediction with two-site prediction methods to predict the phenotype of families that were never observed, based solely on between-family information exchange. Another possible approach is to take advantage of the population information by phenotyping all the families in one environment other than the one targeted for the prediction. As PAL is easier to manage, being free of main rice pathogens and closer to the research institute, we tested a scenario with unbalanced representation of the sites in the training sets (IMB), where all 334 families phenotyped in PAL and only a subset of a varying set size “s” phenotyped in SRO were considered.SRO = 0.29, IMB = 0.56) and PH . However, for both traits the increase of “s” did not yield a much higher PA with the IMB method. Average ZN predictions also benefited from PAL information, but less so . For those three traits, the average PA with the IMB method was rather close to the phenotypic correlation between the two sites (dotted line in SRO (0.27) and IMB (0.34), with values above the indirect phenotypic prediction as represented by the site correlation. The partition of factor effects in the linear model revealed that the proportion of variance explained by the CV method depended on the traits .The training population with which we tested the various CV scenarios had the expected characteristics for applying GP, both in terms of marker density relative to the specific population LD and total absence of structure among the 334 SH2 between the S0:2 and S0:3 progeny testing was limited and fell within the confidence interval of each other. However, the H2 of the S0:2 progenies was significantly higher for FL and YLD in SRO and was significantly lower for PH in PAL. This lack of consistency suggested that changes were driven more by environmental causes than by the degree of allelic fixation within the genetic material. This was supported by the temporal checks for which a significant year effect could be observed for all traits and sites, while no effect of the generation was observed. We concluded that the changes in mean between S0:2 and S0:3 within sites were essentially driven by the environment effect. As the phenotypic variance due to generation was minor compared with the variance associated with the year, generation advance did not seem to influence the PA. For time and economic reasons, calibration on S0:2 phenotypes could thus be preferred as it allows a reduction of the breeding cycle length and cost.Our progeny phenotyping method could not capture the within-line variation, as we recorded traits as the mean of the evaluated plot (FL and PH) or from the bulked harvested plot (YLD and ZN). For most combinations of traits and sites, the difference in 2:4 lines. Conversely, maximum PA for PH (0.46) was comparable with the PA obtained for the 343 S2:4 (0.50) (1:3 (0.52) (1:3 lines (0.44) (H2 for YLD that was higher in our study (H2 = 0.58) than in the two others aforementioned (0.44 in S1:3 lines and 0.32 in the diversity panel). Overall, for these commonly reported traits, the PA obtained in our study did not greatly differ from what was reported for GP in rice diversity panels or synthetic populations , which is comparable to the average estimated PA obtained with the fivefold CV1 model applied to the HarvestPlus association mapping panel of 330 wheat lines  generated substantial improvement of PA for all traits. The interest of this sparse testing method lies in borrowing information within lines across environments (SRO_s25 = 0.14 to IMB_s25 = 0.44 with the 2018 data). Overall, the sparse testing provided an improvement in the prediction of ZN in the rice synthetic population, with average PA (IMB_s200 = 0.51 with the 2018 data) in the range of those reported for spring wheat (Although neither BAL1 nor BAL2 could greatly improve PA compared with SINIn our study, we tested the calibration of a GP model using phenotypic records gathered from early progeny testing in two sites. The potential of using two-site data and sparse testing for the model calibration, was considered as a satisfactory measure to predict most traits, even for YLD, despite a slightly reduced advantage compared with what was reported for the other traits.0 plants, based on the breeding value of the S0. Yet, there is still a need to confirm that the models do predict well the performance of more advanced generations for inbred line development. Indeed, the units to derive in the pedigree breeding scheme should be selected on the basis of “varietal ability” (0 progenies extracted from the PCT27 and brought to near fixation.We have demonstrated that the calibration using phenotypic data collected on progeny testing at two successive early generations could deliver relatively good and comparable PA. This opens up possibilities for rapid cycling RS, with recycling of parental lines from the genotyping of Sability” , which iWe are aware that the optimized scheme we suggest, based on random sampling of the training set, genome-wide markers considered as random effects, and random allocation of genotypes to sparse testing could be improved further still by considering other criteria known to increase the performance of GP. It remains to be seen whether PA can be improved by optimized assembly of the training set as performed in various studies (0 fertile plants of the nC cycle. Prediction of S0 in nC+1 would be done with calibration based on data from the previous cycle nC. This has been tested through simulation (Notwithstanding optimization of the calibration to develop efficient prediction models to fit our scheme, we ought also to consider the gain of applying GP-aided RS in our rice breeding program. So far, only the PA within generations has been tested, starting with the extraction of Smulation  and showhttps://doi.org/10.25387/g3.14139806.All supplementary tables, figures, and the data used in this study are available at figshare:


Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually ‘highly polygenic’. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise ‘wet biologists’ with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits. Complex traits, by definition, depend on a large number of genetic and environmental factors and the interaction between these factors. Complex traits are both the most common and the most interesting phenotypes to study. The key problem in the study of complex traits is the difficulty of researching them using reverse genetics methods, the purpose of which is to search for unknown functions of a molecular sequence by introducing mutations and tracking emerging phenotypes. Naturally, when the expected effect of each of the numerous genes on the phenotype is very weak, such studies are extremely difficult to conduct, as they involve multiple directional changes in the genetic apparatus of the cell that are not possible with current directed genome editing technologies. In addition, for complex traits, it is often unclear what specific changes should be made in cells to obtain the desired phenotypes.This question can be answered by classical forward genetics, which aims to establish the genetic variability associated with the variability in the studied trait by, for example, searching for mutations responsible for a specific phenotype in screens conducted in model organisms using artificial mutagenesis. The field of forward genetics has been enriched with new tools in the last ten years owing to the development of new technologies: genome sequencing and hybridisation arrays. Single-nucleotide polymorphism (SNP) hybridisation arrays make it possible to perform experiments such as genome-wide association studies (GWAS) with simultaneous interrogation of the allelic status of hundreds and later hundreds of thousands of polymorphisms. Recently, thousands of GWAS have been carried out, the results of which can serve as a foundation for subsequent biological experiments. However, it is difficult not to notice the weak interest of experimental biologists in the results of GWAS. We believe that this is largely due to a cultural barrier between geneticists and experimental biologists. From the point of view of devoted experimentalists, GWAS are often perceived as overpriced experiments aimed at exploring the genetics of overly specific, reductionist or bizarre traits such as exhaustion in shift workers , restlesAPOE gene with Alzheimer’s disease (AD) . . 207]. PMendelian randomisation [ALDH2 strongly affects the ability of organisms to metabolise alcohol. This SNP is common in East Asian populations, and for men, it greatly affects their average alcohol consumption. Thus, the genotypes of rs671 could be used as an IV to study the effect of alcohol consumption on, for example, blood pressure and cardiovascular diseases risk, which turned out to be quite high [In many situations, researchers are interested not only in similarity of traits, but also in causal relationships between them. There is an approach, instrumental variable (IV) analysis, that exploits the genetics of complex traits to address this type of question. In social sciences and epidemiology, when studying the causal interaction of exposure and outcome, it is often difficult to isolate them from the influence of other external factors that have not been taken into account in the model. To solve this problem, IV analysis utilises so-called instrumental variables that should be (1) associated with the exposure, (2) associated with the outcome only by means of the exposure and (3) not be associated with any confounders, including unknown ones f. If thimisation . For exaite high ,214. Befite high ; brain site high ; telomerite high ; AD and ite high ; and bloite high . While tite high  and assuite high , the majite high , CAUSE [ite high , MR-APSSite high , cML-MA ite high  and otheTypically, GWAS results contain a small number of easily interpretable results, with a majority of loci lacking ‘natural’ functional interpretations. Linkage regions of GWAS can be very expansive, contain many genes or contain genes with unknown functions. Often, a GWAS linkage region does not contain genes at all if, for example, the causal variability of a trait is associated with an enhancer for a gene located far from this region. Such situations motivate further research aimed at interpreting specific GWAS signals. For example, the biological implications of a well-known genetic signal in a very large (approximately 6 million base pairs) linkage group in the MHC cluster for schizophrenia, one of the strongest signals associated with GWAS, remained a mystery for a long time. The mystery was solved, at least partially, in the 2016 work by Sekar et al., in which this association signal was linked with the variability in the gene of complement component 4 (C4) responsible for the degree of synaptic pruning during maturation of the brain . In addiDPEP1 and CHMP1A, involved in the regulation of ferroptosis and that they surprisingly had the opposite effects on a trait [ADCY5 gene. ADCY5 expression in CRISPR/Cas9-edited adipocytes and osteoblasts is highly dependent on the rs56371916 alleles, which appear to be responsible for bone fragility in type II diabetes [Defining the biological interpretation of a specific GWAS signal often involves fine-mapping of a supposed causal SNP. Experimental data could be utilised for this purpose. In a 2021 study by Guan et al., eQTL, mQTL and ATAC-seq data were used to fine-map the region of rs164748, which was identified in GWAS for estimated glomerular filtration rate (eGFR). In this study, genome editing in mice was used to demonstrate that there are at least two genes,  a trait . The stu a trait . An examdiabetes .Experimental data are often processed in bioinformatics analysis using various ontology enrichment techniques. This could of course be done with GWAS data if a researcher wants to check whether, for example, ChIP-seq active chromatin peaks are colocalised with significant SNPs for a specific trait. The modern way to do this, as discussed earlier, is to use methods specialised for functional interpretation of GWAS data, like MAGMA or S-LDSC. While all of these methods were created for the interpretation of GWAS results, they could in some circumstances be used in reverse scenarios aimed to answer questions such as which complex trait genetics corresponds to a given functional annotation or which functional annotation better describes a given complex genetic trait.This type of analysis is natural to apply to single-cell experiments, since it assists in the interpretation of revealed cell classes. For example, in a 2021 study by Sheng et al., on single-cell characterisation over different cell types in kidneys, MAGMA was applied to gene expression data and S-LDSC was applied to the scATAC-seq data . Both ofAn interesting example of GWAS-informed interpretation of expression data is described in a 2017 paper by Calderon et al., which describes a method (named ‘RolyPoly’) for cell-specific enrichment in GWAS results using scRNA-seq data on a gene level. Specifically, RolyPoly gene scores for AD GWAS in the most relevant cell type  are correlated with test statistics of genes found in an independent differential expression experiment on AD. The result is quite impressive, as it theoretically allows us to obtain the same information from gene expression data from healthy people with AD GWAS results and from much more elaborate experiments involving collection of specific laser-microdissected brains of deceased AD patients to study differential expression .SORT1 gene expression and low-density lipoprotein using eQTL data in liver tissues [In experimental biology, the causality between a gene and a phenotype could be established in direct experiments with altered and rescued gene expression via, for example, genomic editing. However, the genetic factors of complex traits are too numerous, and their effects are too small for any existing genome editing technology to be practical to use for phenotypes manifested at the level of the whole organism. There have been attempts to utilise the Mendelian randomisation approach to find causal genes linked to a trait of interest using GWAS summary statistics and eQTL data in relevant cell types. In 2019, Porcu et al., described an approach, called ‘Transcriptome-wide summary statistics-based Mendelian Randomisation approach’, TWMR, that allows the identification of potential causal links between gene expression and a trait. The utility of the method is backed by its ability to predict significant GWAS hits retrospectively and to correctly point to known causal links, for example, between  tissues . It shou tissues .Biological perspective gives us an intuition that information that is more relevant to the biology of a trait should be a better predictor; hence, use of functional data could improve or even outperform PRS-based predictions. For example, TWAS results provide a direct link between gene expression and a trait, which is tempting to use for diagnostic purposes. In a 2021 study by Pain et al., gene expression risk scores (GeRS), which are based on imputed gene expression from TWAS data, were constructed for a panel of human diseases: rheumatoid arthritis, inflammatory bowel disease, coronary artery disease, type II diabetes, height, BMI, intelligence and depression. In all cases, while GeRS explained a significant portion of heritability, they all performed worse as a trait predictor than the corresponding PRS . This coPleiotropy seems to drastically affect the performance of methods based on individual SNPs and individual functional effects. On the other hand, polygenic scores, which integrate all genetic effects into a single number, could surprisingly be effective in informing which genes are associated with a specific trait. In 2021, Võsa et al., studied the role of distant expression–genotype interactions (trans-eQTL) in the genetics of complex traits. Notably, trans-eQTL effects are neglected in conventional eQTL-based analyses like TWAS because potential trans-eQTLs are much more abundant than traditional cis-eQTLs and attempts to identify them all individually would inevitably fail due to the dimensionality curse. The authors found that if testing for eQTL effects was limited by SNPs known to be related to a different trait, trans-eQTL effects became apparent and widespread. They further used PRS to construct expression quantitative trait scores (eQTS), in which PRS was used as a mediator for gene expression in a blood dataset for which gene expression and genotype data were simultaneously available. This allows detection of situations in which multiple weak and trans-eQTL effects converge on a single gene. The eQTS method seems to be more appropriate for the highly polygenic nature of complex traits; for example, it proved to be able to correctly detect genes for lipid metabolism using PRS for high-density lipoprotein concentration .The other way to use PRS in experimental biology is to search for rare mutations relevant to a trait. The reasons why GWAS on a common disease generally does not provide suitable candidate targets for medical research include low effects, unclear genomic location due to LD and, potentially, a too-broad mechanism of action due to pleiotropy. In contrast, rare mutations are more straightforward to interpret, more prone to have large and focused effects and can be easily manipulated with conventional genetic methods. The problem is they are, by definition, rare and in general much more difficult to find and even more difficult to identify as a modificator to a trait. Usually, these mutations are found in a family genetic analysis, massive screening of trios for de novo mutations or even more massive exome-wide screenings. At the same time, candidate regions from GWAS proved to be useful as starting points for the search for meaningful rare mutations in targeted sequencing experiments focused on, for example, inflammatory bowel disease ,242, ageIn general, it seems possible to use PRS in experimental biology for sample stratification. Sometimes it is difficult to generate a good cell model for a trait. For example, in psychiatric genetics, it seems that the most relevant cell model for research is a foetal brain of a future psychiatric patient, which is, of course, close to impossible to obtain. Instead, stem cells from any donor could be procured with the goal of creating a relevant cell model, leveraging PRS to study a trait of interest. For example, this logic motivated a 2020 report by Dobrindt et al., on iPSC for several extreme PRS for schizophrenia . In prinStudies of the genetics of complex traits have now reached maturity, and powerful new instruments have been developed to measure and interpret their results. These results are usually available for any interested researcher in the form of GWAS summary statistics, which allows researchers to relate complex trait genetics to a broad range of biological experiments and unlock new experimental designs in complex traits research. For instance, LDSC and other methods discussed could be used for functional data interpretation, Mendelian randomisation-based methods could help to infer causality and polygenic scores allow the direct consideration of polygenic inheritance. However, we urge avoiding overestimation of non-additive genetic effects or underestimation of pleiotropy. We hope that after reading this review, it will be clearer for experimental biologists how the results of modern genetic research can help them in their work.


The majority of the genome is shared between the sexes, and it is expected that the genetic architecture of most traits is shared as well. This common architecture has been viewed as a major source of constraint on the evolution of sexual dimorphism (SD). SD is nonetheless common in nature, leading to assumptions that it results from differential regulation of shared genetic architecture. Here, we study the effect of thousands of gene knockout mutations on 202 mouse phenotypes to explore how regulatory variation affects SD. We show that many traits are dimorphic to some extent, and that a surprising proportion of knockouts have sex‐specific phenotypic effects. Many traits, regardless whether they are monomorphic or dimorphic, harbor cryptic differences in genetic architecture between the sexes, resulting in sexually discordant phenotypic effects from sexually concordant regulatory changes. This provides an alternative route to dimorphism through sex‐specific genetic architecture, rather than differential regulation of shared architecture. In organisms with separate sexes, different evolutionary interests of males and females can lead to divergent trait optima, which can be realized through the evolution of sexual dimorphism (SD). The change from monomorphic to dimorphic requires that the underlying genetic mechanisms be decoupled between males and females. However, even in species with sex chromosomes, males and females share the vast majority of their genome  for a trait of interest. This correlation describes the extent to which a particular genotype affects both male and female phenotypes in the same way. If rfm≈1, genotypes affect males and females similarly , whereas if rfm≈0, male and female phenotypes vary independently  , body size , traits with fewer than 100 genotypes, and traits that were clearly not actually continuous (such as a count of the number of ribs). After triage, we had 260 traits for which we downloaded all available phenotype data, including both knockout phenotypes and control data. On average, we obtained data for 8069 control mice and 21,513 mice from 1713 knockout lines, per trait. Per knockout line, seven females and seven males were typically phenotyped.rfmA). However, to delineate the knockout lines from the traditional approach, we denote these estimates as rfmK, where K denotes the genetic variance‐covariance matrix between knockout genotypes  and SD. The Bayesian approach allowed us to evaluate and propagate the uncertainty in the estimates of rfmK and SD in downstream analyses. This is especially important for rfmK, because this correlation can be biased toward 0 if it is difficult to estimate  of genotype for each sex, and their correlation. Finally, we added group‐level intercepts for known sources of variation when they were present, which were (1) the phenotyping center in which testing was performed, a parameter encoding several methodological differences (“meta group”), and (2) the date of testing. This leads to the final model definition in lme4/brms syntax: phenotype ∼ weight + sex + (0 + sex | genotype) + (1 | center) + (1 | meta_group) + (1 | date). In mathematical notation, following Gelman and Hill  for the intercept and N for the effect of body mass. For the group‐level standard deviations and residual standard deviation, we used the positive range of unit student‐t distributions with 5 degrees of freedom. Finally, we used a Lewandowski‐Kurowicka‐Joe (LKJ) prior with η = 1 for rfmK, which is uniform over the range −1 to 1. Posterior distributions were obtained using Stan's no‐U‐turn HMC sampler, with two chains of 8000 iterations, with the first 4000 used as warm‐up and discarded. We additionally set the max tree depth to 20 and the adapt delta parameter to 0.9. To evaluate the ability of our models to accurately estimate the between‐sex genetic correlation, even though the sample size for each genotype was limited, we performed a simulation study . We estimated the transform using the bcnPower method in the car package VG, and the Riemannian distance of the scaled variance‐covariance matrix from [1111]. For more information about the Riemannian distance, see the Supporting Information. We then derived posterior distributions of SD by predicting average male and female phenotypes for wild‐type  mice. When there were multiple genetic background variations in which a trait was tested, we used the marginal means across backgrounds. To make SD estimates comparable across traits, we used a mean standardized effect size for SD for downstream analyses, the SD index:x¯largersexx¯smallersex−1. Note that the SD index requires that comparisons to 0 are biologically meaningful , which was not true for all the traits in our dataset, such as body temperature, indices, and fractional measures. We therefore performed back transformations of the marginal means to the original scale, and we only calculated SD for 156 out of 202 traits.For each of these models, we derived posterior distributions for three metrics of the genetic variance structure: measure∼ SD index model using the brm_multiple function, and performed inference on the combined set of 500 chains. Note that we performed a Z‐transformation on rfmK, also called the Fisher transformation, to stabilize the variance. Additionally, we log transformed the ratio of the genetic variances and the Riemannian distance.After obtaining the posteriors for each trait, we used a linear model to test for a relationship between each of the three genetic (co‐)variance measures and SD. To account for uncertainty in those estimates, we performed random draws from the posterior distributions of those estimates to create 500 datasets. For each of those samples, we ran one MCMC chain of a rfmK, we split the body mass data into different ages. Mice were weighed once a week, with most mice being measured between 4 and 16 weeks of age. For each week, we ran the same analysis as for the separate traits outlined above.Because data on body mass were available at different ages, we analyzed this trait separately. To quantify sexual size dimorphism (SSD) during development, and associated changes in rfmK, described above, to obtain estimates of the male and female trait values for the measured genotypes. We extracted the posteriors for the male and female parameter for the genotype group term (Best Linear Unbiased Predictor). Note that these estimates are adjusted for body weight and environmental effects, have already undergone parameter shrinkage, and are centered around 0. We then translated the male and female phenotypes into concordant and discordant effects, by rotating the axes so that the concordant axis is the positive diagonal  and the discordant axis is the negative diagonal . The absolute value along the two diagonal axes was taken, so that the effect of a genotype is larger when it is further from the population average. Because the size of the discordant effects of a genotype is strongly affected by the trait architecture , we assigned genotypes percentile ranks to aid comparison across traits.In addition to the trait‐level analyses above, we made use of the repeated phenotyping of knockouts for different traits to ascribe sexually discordant effects to particular genotypes. The concordant and discordant nature of knockout genotypes was determined by evaluating whether the genotypes were consistently ranked low or high along the concordant and discordant axes across traits. For each trait, we used the multilevel model that was used to estimate SD and th percentile in Fig. For all genotypes that were tested on at least 100 phenotypes, we calculated the average concordant and discordant rank across traits. Credible intervals (CIs) for this average were calculated by computing that average for 100 random draws of the posteriors. We categorized genotypes as less or more discordant than average by checking whether the CI overlapped a median rank  terms for the underlying knockout genes. Using limma , and empirical Bayes moderated t‐statistics with adjusted P‐values. We then classified genes as sex biased if the fold change was at least 2, and the adjusted P‐values were significant (α = 0.05). Genes that did not satisfy both those criteria were categorized as unbiased.We obtained published gene expression profiles of male and female gonadal tissue from the ArrayExpress database under accession number E‐GEOD‐1148 , which are binary traits (fertile vs. infertile) where each sex has been allowed to breed with a wild‐type mate. Combining these, we defined four fertility categories: fertile, female‐limited infertile, male‐limited infertile, and infertile. To test for an association between gene expression category and fertility outcome after knockout, we performed a 3 × 4 chi‐squared test for independence.tidyverse  is only slightly higher than the overall average: 0.630 .To test the constraint that high rfmK and SD is different for different trait categories VG(f) = 1.14 ). Thus, knockout mutations have, on average, substantially larger phenotypic effects in males. It has been noted previously that mutations have larger fitness effects in male Drosophila . The gene expression profiles of male and female gonadal tissue are available from the ArrayExpress database under accession number E‐GEOD‐1148. All estimates used in downstream analyses are available in the Supporting Information.No new data were collected for this study. All raw phenotype data are available from the International Mouse Phenotyping Consortium  between trait categories.Figure S4: The Riemannian distance to the null model (see Supplementary Methods) does not depend on sexual dimorphism in the trait.Figure S5: The fraction of knock‐out experiments with significant sex‐by‐genotype interaction does not depend on sexual dimorphism in the trait.Figure S6: Fertility of gene knock‐out lines, comparing between genes that have sex‐biased expression in the gonads, and genes that have unbiased expression.Figure S7: As Figure 1, but without accounting for body mass.Figure S8: As Figure 2, but without accounting for body mass.Figure S9: A simulation study on the potential for biased estimates for Kmfr from our modelling approach.Figure S10: Comparison between our estimates of Kmfr and the fraction of significant sex‐by‐genotype interactions reported by Karp et al.Table S1: Estimated parameters for each trait.Table S2: List of genotypes with consistently low or consistently high discordant ranks.Click here for additional data file.


Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease; however, little is known about its wider health impacts. This study explores health outcomes associated with JIA genetic liability.We used publicly available genetic data sets to interrogate the genetic correlation between JIA and 832 other health‐related traits using linkage disequilibrium score regression. Two‐sample Mendelian randomization (2SMR) was used to examine four genetic correlates for evidence of causality.P [Padj] < 0.05) of genetic correlation between JIA and rheumatoid arthritis , hypothyroidism/myxedema , celiac disease (CD) , systemic lupus erythematosus , coronary artery disease (CAD) , number of noncancer illnesses , paternal health , and strenuous sports . 2SMR analyses found robust evidence that genetic liability to JIA was causally associated with the number of noncancer illnesses reported by UK Biobank (UKBB) participants .We found robust evidence (adjusted This study illustrates genetic sharing between JIA and a diversity of health outcomes. The causal association between genetic liability to JIA and noncancer illnesses suggests a need for broader health assessments of patients with JIA to reduce their potential comorbid burden. The strength of genetic correlation with hypothyroidism and CD implies that patients with JIA may benefit from CD and thyroid function screening. Strong positive genetic correlation between JIA and CAD supports the need for cardiovascular risk assessment and risk factor modification. This study demonstrates the causal role of genetic liability to juvenile idiopathic arthritis (JIA) on comorbidity and the potential breadth and burden of JIA‐associated comorbidity later in life.JIA was genetically correlated with multiple other autoimmune traits, including hypothyroidism and celiac disease; thus, patients may benefit from routine screening of these conditions.JIA is genetically correlated with both coronary artery disease and a number of adverse cardiometabolic traits, highlighting the need to consider cardiovascular risk factor assessment and modification in JIA.Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood, with an estimated prevalence of 32.6 per 100,000 in Europeans  and Mendelian randomization (MR), enable estimation of the genetic and causal relationships between JIA and other health outcomes. LDSC uses genome‐wide association study (GWAS) summary statistics to estimate the single‐nucleotide polymorphism (SNP) heritability of a trait  and geneThe aims of this study were, firstly, to use a hypothesis‐free approach to examine the shared genetic correlations of JIA with other health‐related traits using cross‐trait LDSC and, secondly, to use 2SMR to interrogate whether JIA is causally associated with genetically correlated health‐related traits.rg) between traits by regressing the product of the z scores from two studies of traits against the LD score for each SNP, with possible rg estimates ranging from −1 to 1  online platform  patients with polyarticular JIA  and 13,056 healthy controls  of European ancestry (H2z score <3.5) were excluded. Benjamini–Hochberg  <0.05 was considered statistically significant. For clarity, some traits have been renamed; GWAS summary data held within LD Hub are not sex stratified and are predominantly from populations of European ancestry. All studies in LD Hub have undergone rigorous quality control . The JIAancestry . Data seancestry . As per ancestry . Traits Hochberg  adjustmer2 < 0.001) variants that were associated with JIA (P < 5 × 10−8) in an additive model as instruments for MR. The variance explained (R2) and F statistic were calculated for the JIA instrument (see The JIA instruments were selected from the same JIA data set used previously . We selement see .Padj < 0.05) in relation to their genetic correlation with JIA (see Results). We further removed four autoimmune traits because the shared immunogenetics of many autoimmune diseases was likely to violate the third assumption of MR, ie, that of no genetic pleiotropy  were used where the instrument was not available in the outcome GWAS. The alleles were harmonized to ensure that the SNP exposure effect and the SNP outcome effect corresponded to the same allele. Palindromic SNPs were aligned using default settings, and noninferable palindromic SNPs were excluded to limit effect allele ambiguity between JIA and outcome data sets. We applied the inverse variance weighted (IVW) method to estimate the effect of JIA on selected outcomes. Briefly, the IVW method calculates a weighted average of effect estimates obtained using each individual SNP  through a fixed‐effect meta‐analysis. SNPs are typically weighted by their corresponding inverse variance  for binary traits. To aid interpretation, MR estimates were transformed to represent the change per doubling in odds of JIA. However, given that JIA represents a binary exposure, it may be more straightforward to consider the MR estimates as evidence for the existence versus absence of a causal effect of exposure (JIA) on the selected outcomes . The JIA data set met the recommended thresholds (Single‐trait LDSC estimated the JIA SNP resholds .z scores were low (<3.5), we identified eight health‐related traits that were genetically correlated with JIA after correction for multiple testing , plus 32 suggestively correlated traits , hypothyroidism/myxedema , celiac disease (CD) , and systemic lupus erythematosus (SLE) . A further five autoimmune conditions showed positive correlation with JIA: primary biliary cirrhosis (PBC) , inflammatory bowel disease , ulcerative colitis (UC)  and multiple sclerosis , although these did not survive adjustment for multiple testing  and negative correlation between JIA and reporting to not take medication for cholesterol, blood pressure, diabetes, or exogenous hormones . JIA also showed genetic correlation with cardiometabolic risk factors traits, including physical activity, body habitus, and smoking. The only negative correlation with JIA after adjustment for multiple testing was for strenuous sports . However, suggestive correlation was also seen between JIA and other markers of more sedentary behavior, eg, reporting to have a job involving mainly walking or standing , bone mineral density , and reporting to use cycling as a mode of transport . Suggestive positive genetic correlations were also seen between JIA and anthropometric traits, including obesity  and hip circumference .Genetic correlation was also seen between JIA and multiple cardiometabolic traits. A positive correlation was observed between JIA and coronary artery disease (CAD)  and paternal health , following adjustment for multiple testing. A number of correlations were also seen with other adverse individual and familial health‐related outcomes, including overall assessments of health, although these did not survive correction for multiple testing.Furthermore, JIA was genetically correlated with a number of other health traits Figure , includiF statistic = 52.64, R2 = 0.0352). Genetically predicted JIA was causally implicated in the number of reported noncancer illnesses . There was limited evidence of a causal relationship between genetically predicted JIA and CAD , with the estimates from all MR methods directionally consistent  in all MR analyses, except for the analysis evaluating the effect of JIA on strenuous sports (P > 0.05) for any of the outcomes based on the MR‐Egger intercept (P < 0.05) for CAD  identify health‐related traits that are genetically correlated with JIA and b) investigate whether these may indicate a causal effect of JIA to assist in patient counseling, risk stratification, lifestyle modification, and treatment targeting for patients with JIA. We show that genetic liability to JIA is correlated with genetic liability to many systemic and organ‐specific autoimmune and nonautoimmune disorders. We found evidence for a causal effect of JIA on the number of noncancer illnesses reported in adulthood, illustrating the potential comorbidity associated with JIA. There was limited evidence that genetic liability to JIA is causally implicated in CAD, strenuous sports, or paternal health, suggesting that the genetic correlation seen between these traits is more likely due to shared genetic architecture, exposure to environmental risk factors, or other shared factors that influence these traits but via different causal paths. However, the JIA data set has a relatively small sample size and because estimates from multiple MR methods are consistent, we cannot fully exclude the possibility of a small causal effect of liability to JIA on these outcomes.H2 as 58% using LDSC. Previous studies into the genetic architecture of pediatric autoimmune disorders report genetic sharing between JIA and a number of immune‐mediated disorders using genome‐wide and locus‐specific analyses, including CD, UC, PBC, psoriasis, type 1 diabetes (T1D), and ankylosing spondylitis  risk. CAD  was the Observationally, JIA is associated with poor health outcomes in adulthood , 25, 26.Our findings highlight a number of potential clinical implications; however, we caution that further investigation is warranted. The causal association between liability to JIA and number of noncancer illnesses suggests a need for risk assessment and risk factor modification in patients with JIA to reduce the burden of its comorbidity. In the United Kingdom, children with T1D are recommended to have regular assessment of thyroid function . Similarrg = −0.063, P = 0.44)  , and imp = 0.44) . Given tThe main strength of this study is its hypothesis‐free approach to capture a broad range of JIA‐associated health outcomes. The outcome data sets used in this study have considerably larger sample sizes than those used in previous JIA studies , 21, oft− polyarticular subtypes  coronary artery disease, (B) strenuous sports, (C) paternal health and (D) number of non‐cancer illnesses, a leave on out analysis was conducted. Each row represents 2SMR analysis of JIA on the selected outcome using all SNPs in the JIA instrument, except the SNP listed on the y‐axis. The point represents the MR effect size (beta) and the bars represent 95% confidence intervals. The row in red represents the estimate when all SNPs are included in the analysis.Click here for additional data file.Supplementary S1 MethodsClick here for additional data file.Supplementary Tables S1Click here for additional data file.


Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features.We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model.A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.The online version contains supplementary material available at 10.1186/s12711-021-00672-9. Growth traits of chickens are well known for their genetic architectural complexity. Since the release of the chicken genome assembly , many reIn 2001, Meuwissen et al.  proposedhttps://www.animalgenome.org/cgi-bin/QTLdb/GG/index) contains information on 3828 records related to chicken growth traits [The Chicken QTL Database  strategy to conduct a large-scale genomic analysis of the nucleus breeding population of commercial yellow-plumage chickens. Based on 151,519 autosomal single-nucleotide polymorphisms (SNPs) located along the genome of 6359 chickens, we identified multiple QTL that affect growth traits with major or minor effects. We performed genomic evaluation and estimated the contribution of each locus to the accuracy of genomic predictions. Thus, the objectives of this study were to: (1) conduct a genome-wide association study (GWAS) in yellow-plumage chickens to identify key genetic factors and explore the polygenicity of growth traits in chickens; (2) investigate the accuracy of GP in this population; and (3) evaluate genomic predictions that harness genomic features.The animal and phenotypic data used for this study were obtained from a commercial nucleus breeding population of yellow-plumage chickens. Two generations (8 and 9) of chickens hatched in six batches from 2018 to 2019  were generated, managed, and penned in a single nucleus breeding farm. Approximately 1400 candidate roosters and ~ 2800 candidate hens  were available in each generation, and ~ 50 roosters and ~ 750 hens were selected for breeding the next generation; the main selective trait was feed conversion ratio (FCR), with a selection pressure of 1:20 for males and 1:3–4 for females.All birds were fed ad libitum, following the feeding management procedure of the company during the brooding stage 1–35 days) and the growing stage (36–84 days). During the brooding stage, the birds were raised in a closed brooding area. They were then transferred to pens with vertical ventilation systems for the growing stage, where the males and females were separated and penned at different sides of the building. Each pen had 17 feeding stations and five hanging water fountains. Birds with physical maldevelopment or deformities were eliminated. During the brooding stage birds were fed a diet containing 2837 kcal metabolic energy/kg and 200 g crude protein/kg, before switching to a high-energy diet containing 2900 kcal/kg and 190 g/kg. Daily feed intake of each bird was recorded throughout the feeding trial from 42 to 84 days of age by subtracting the food remaining in the afternoon from the initial mass recorded in the morning. Body weight was recorded at 42 and 84 days of age (denoted as 42-day-old weight (DW) and 84 DW, respectively); FCR was calculated by dividing the total feed intake during the 42- to 84-day period by the total weight gained during that same period, and the average daily gain (ADG) was calculated by dividing the total weight gained during the 42- to 84-day period by 42 days. In total, 8122 body weight phenotypes  and 8115 FCR phenotypes were recorded. Basic statistics for these traits are in Table –35 days http://www.deenabio.com/services/gbs-adapters) and a reverse adapter with an MseI overhang was designed according to Wang et al. [Gallus gallus 6.0 as a reference. The average genome sequencing depth reached 0.78 × /bird. After SNP detection, data on 6684 individuals with genotypes on 1,994,350 SNPs were obtained. Beagle 5.0 was used to impute missing SNP genotypes [p ≥ 1e − 4. We also removed individuals with a SNP missing rate higher than 30%. Finally, 1,842,831 low-quality SNPs and 325 birds with high SNP missing rates were removed, and 6359 individuals with 151,519 autosomal SNPs were retained with a marker density of one SNP per 6.34 kb. SNPs were uniformly distributed along the entire genome . Genomic DNA was extracted from blood samples and quantified using a Qubit 2.0 Fluorometer. DNA concentrations were normalised to 200 ng in each well of 96-well plates. Two-enzyme GBS was used, with the enzymes EcoRI and MseI. A set of 96 forward barcoded adapters with an EcoRI overhang was designed using the GBS Barcode Generator ; A mixed linear model approach was used for the single-SNP GWAS, as implemented in the GCTA software . The mixsoftware  with opt is the additive genetic variance, and  is the residual variance. The GRM, Two generations of birds were used to construct the relationship matrix. The mixed model for genomic best linear unbiased prediction (GBLUP) was the same as that used for GWAS, except that SNP genotype was removed as a fixed effect. In this model: package . GenomicThe GBLUP of individual genetic values in the reference and validation samples were calculated during the last GREML iteration. The heritability and effect of each SNP were also computed using the GREML_CE programme. and  of the associated additive genetic variances; and  is the residual variance.To further improve the GP model by harnessing genomic features, we used a two-GRM GBLUP model (GFBLUP) to perform genomic evaluations. First, we defined a population for the GWAS N = 1500) by random sampling from 6359 birds; this population was used to identify significant SNPs and QTL (i.e. the discovery population). Then, we separated the significant SNPs obtained from the GWAS from the 151,519-SNP dataset and generated two GRM for the birds not used in GWAS analysis (N = 4859) , the GRM of the GBLUP model was replaced by the pedigree-based additive relationship matrix  package . The lasA tenfold cross-validation was conducted to evaluate the predictive ability , 29 of tTo estimate the contribution of QTL regions to heritability and prediction accuracy, we used GBLUP and GFBLUP models to estimate heritability and evaluate prediction accuracy using all the birds (N = 6359). Similarly to the above steps, we randomly divided these birds into ten validation datasets, and for each run, nine partitions were treated as the reference group, whereas the remaining one was used for validation. Phenotypic observations in the validation dataset were omitted from the GBLUP and GFBLUP models.To evaluate the influence of the reference population size on the prediction accuracy of GBLUP, we randomly selected different subsets of the population from the full set of 6359 birds . For each subset, data were randomly partitioned into 11 equal portions: for each analysis ten portions were used as the reference group for the GBLUP model , while the remaining portion was used for validation . For example, when the size of the population was 550, 500 birds were randomly subsampled as the reference set, and the remaining 50 birds were used as the validation set. Therefore, 11 reference sets (N = 500 for each set) and evaluation sets (N = 50 for each set) were obtained. is the additive genetic variance. The theoretic accuracy is defined as the correlation between GEBV and G  comparison of full and reduced models, (2) estimation of the heritability of two GRM (the significant SNP set from GWAS and the remaining set), and (3) evaluation of the prediction accuracy of the GFBLUP model.th QTL were selected from the GWAS analysis. Then, the reduced model fitted significant SNPs as fixed effects to remove their effects and those of other SNPs that were in high LD with the targeted SNPs from the random genetic effects on phenotype [The contributions of QTL to genomic heritability and prediction accuracy were defined as “partial heritability” and “partial accuracy”, respectively, based on results from a full model and a reduced model. The GBLUP model that fits all SNPs as random effects was used for the full model. For each run, significant SNPs  of the henotype . The GVCi on GGA1, GGA2, GGA4, GGA17, or GGA27) to heritability. Observed prediction accuracy was computed as:In this method, which was based on the GWAS results, we split the 151,519 SNPs into a set with the significant GWAS results and a set with the others. Then, we used GCTA to compute a GRM for each set. Using the GRM, the GFBLUP model was used to estimate the additive variance explained by each GRM, and the contribution of the The change in prediction inflation addition for the above two methods was calculated as follows:We used the full model described above to estimate the contribution of the GWAS top SNPs  by removing each in turn from the statistical model. To avoid data reuse, we repeated a random, tenfold cross-validation GWAS. For each fold validation, 5723 birds (nine partitions of the full set of data) were used for GWAS and reference, and the remaining were used as the validation set. To avoid obtaining SNPs from the same QTL with variants in high LD, we used LD pruning to remove one SNP in each SNP pair with an LD > 0.9 using the PLINK ‘-indep-pairwise’ command [The GWAS of four growth traits , GGA17, and GGA27 (QTL detected only for 84 DW). We also compared the GWAS results for ADG and FCR, which were measured during the same period (birds 42 to 84 days old), and the difference between the detected QTL was identified on GGA17. Collectively, these results indicated that the detected QTL affected chicken growth traits that had significant differences.We performed genomic evaluations with the GBLUP model, using 151,519 SNPs for the four traits. The observed average accuracy of the GEBV from the tenfold validation data set ranged from 0.31 to 0.45 for the GBLUP model Table , which wBased on the reduced GBLUP model that fitted significant SNPs within a QTL region as fixed effects, the contribution of a QTL was estimated as the reduction in heritability and prediction accuracy compared to the full model. The QTL on GGA4 contributed the most to all four phenotypes, with the contribution to heritability ranging from 14.5 to 34.5%, and the contribution to the observed and theoretical prediction accuracy ranging from 23.3 to 46.7% and 4.5 to 11.1%, respectively. The contributions to GP and heritability differed between traits. In particular, for 84 DW, the contribution was more than twice as high as that for 42 DW  to improve the prediction accuracy by allowing for increased weight on detected QTL through a separate GRM. As a result, the average accuracy of GP was improved by 13.8 to 15.2% compared to the conventional GBLUP model , and with the further advancements in genomic selection for broilers, we expect that large-scale application of whole-genome sequencing will offer improved opportunities for genomic selection in poultry breeding in the near future.Genomic prediction based on genetic markers across the genome has become the standard evaluation method in dairy cattle breeding programmes , 58. In Genomic prediction ability is primarily influenced by the prediction model that is applied , the sizIn our study, we evaluated the contribution of significant SNPs to heritability and prediction accuracy. Figure With the increasing availability of whole-genome sequencing data, selected sites that are in high LD with causative variants have become particularly important for estimating the GEBV. In this study, the two-GRM GBLUP model that harnessed preselected significant SNPs improved the prediction accuracy compared to the conventional GBLUP model. However, the preselected SNPs or QTL were confined to a yellow-plumage chicken population, which has not been selected for growth and efficiency to the same extent as commercial broilers. Therefore, the preselected SNPs may not be suitable for assisting GP in commercial broilers. To make the SNP pre-selection method universal, it is important to optimise the method with a more systematic pre-analysis to determine the genetic contribution of each marker required to increase prediction accuracy. For example, for some commercial broilers that have been under long-term selection, there may be no detectable major genes and the genetic architecture of their traits may be more in line with the “infinitesimal model”. In this case, further improvements in GP using only GWAS information may not be feasible. We used GWAS analysis to preselect SNPs and improve prediction accuracy by using the GFBLUP model, however, a more systematic optimisation for preselecting SNPs is needed. Multi-omics meta-analysis provides a promising and accurate strategy for optimising the GP model, since an evaluation that integrates external information may more efficiently and precisely identify loci that have important genetic contributions to phenotypes –73. A reIn this study, we examined the polygenicity and compared the predictive ability of different prediction models for growth traits in a yellow-plumage chicken population: (1) by using GWAS, we identified five candidate QTL intervals for growth traits and confirmed a major effect of the QTL on GGA4 and four other QTL with minor effects on GGA1, GGA2, GGA17, and GGA27; (2) by using the GBLUP model, the prediction accuracy was greatly improved compared to the conventional pedigree-based method (ABLUP model); (3) by accounting for QTL markers using the GFBLUP model, the prediction accuracy was further improved compared to the GBLUP model. Although our genomic evaluation and the genomic feature-harnessing model results demonstrated an improvement of prediction performance, these improvements must be considered as a “proof of principle” for evaluating the feasibility of applying genomic selection in poultry breeding.Additional file 1: Figure S1. Phenotypic distribution of four traits in yellow-plumage chickens . The data present the phenotypic distribution of four growth traits according to males (left panel) and females (right panel). Figure S2. SNP density of 151,519 variants. The data present the SNP density of detected SNPs within 1-Mb windows. Figure S3. Principal component analysis (PCA) of birds. PCA of sequenced individuals, where the top two principal components were plotted. Figure S4. Study design for cross-validation. The discovery sets were randomly selected from 6359 birds; 1500 individuals were used for GWAS; and the remaining 4859 birds were used for predictive evaluation , where the accuracy and inflation of prediction were averaged across all validation sets. Figure S5. QQ plots of the genome-wide association study of the four traits. The red dots represent SNPs that passed the significance level threshold . Figure S6. Trend line of genomic prediction accuracy and inflation based on different sizes of the reference population (500 to 5500). Three indicators were used for evaluation, namely (a) observed prediction accuracy, (b) theoretical prediction accuracy, and (c) prediction inflation. Figure S7. Trend line of genomic prediction accuracy and inflation. The line is based on the proportion of top SNPs removed based on the GWAS results. (a) Theoretical prediction accuracy, and (b) prediction inflation. Figure S8. Genome-wide distribution of the top 30% of SNPs. The distribution of SNPs is based on GWAS results overlapping in the tenfold validation analysis, where the number of overlapping SNPs was equal to 15,077, 14,460, 14,558, and 14,259 in the analysis of 42 DW, 84 DW, ADG, and FCR, respectively.Additional file 2: Table S1. Information on the significant SNPs associated with 42 DW in the QTL region on chromosome 4. The data present the genomic location, effect, heritability, P value, and functional annotation of each GWAS significant SNP associated with 42 DW on chromosome 4. Table S2. Information on the significant SNPs associated with 84 DW in the QTL region on chromosome 4. The data present the genomic location, effect, heritability, P value, and functional annotation of each GWAS significant SNP associated with 84 DW on chromosome 4. Table S3. Information on the significant SNPs associated with ADG in the QTL region on chromosome 4. The data present the genomic location, effect, heritability, P value, and functional annotation of each GWAS significant SNP associated with ADG on chromosome 4. Table S4. Information on the significant SNPs associated with FCR in the QTL region on chromosome 4. The data present the genomic location, effect, heritability, P value, and functional annotation of each GWAS significant SNP associated with FCR on chromosome 4. Table S5. Average prediction inflation based on tenfold cross-validation. The data provide the average prediction inflation using ABLUP, GBLUP or GFBLUP models. Table S6. QTL contributions to the theoretical prediction accuracy (%). Description: The contributions were estimated by setting GWAS significant SNPs in each QTL as fixed factors. Table S7. Average prediction inflation addition after setting GWAS significant SNPs as fixed factors. The average prediction inflation changes after setting GWAS significant SNPs as fixed factors, where values approaching 0 represent lower inflations. Table S8. QTL contributions to heritability and observed prediction accuracy (%) using the GFBLUP model. The contributions were evaluated by estimating the contribution of two GRM using the GFBLUP model. Table S9. Average theoretical prediction accuracy using two GRM in the GFBLUP model. The data provide the average theoretical accuracy of prediction based on the GRM of GWAS significant sites and the remaining sites, respectively. Table S10. Average prediction inflation addition in the GFBLUP model. The changes in average prediction inflation evaluated by using the GRM without GWAS significant SNPs compared to that using both GRM, where values approaching 0 represent lower inflations.


Unmanned aerial vehicle imagery of a rice MAGIC population at the vegetative stage in a haplotype-based genetic study reveals novel insights into field biomass and plant architecture. Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four quantitative trait loci (QTLs) for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTLs. Further genetic characterization revealed the relationships between these QTLs and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stages of rice growing in the field provides insights into plant growth, architecture, final biomass, and yield. Crop breeding needs both molecular and phenotypic markers for plant biomass and yield. Molecular DNA markers are used to distinguish DNA polymorphisms tightly linked to agronomic traits such as disease resistance, abiotic stress tolerance, days to heading and plant architecture  lines are useful. The first MAGIC population was developed in Arabidopsis thaliana , ‘Suwon 258’ (SU), ‘Takanari’ (TK), ‘Hokuriku 193’ (HO) and ‘Ruriaoba’ . By developing 10 lines from 100 eight-way recombinants, we produced 981 JAM lines. We characterized the JAM lines in 2016 and 2017, and omitted lines with very low fertility, late heading, seed shattering, and highly heterogenous distribution of fertility and days to heading, which could affect yield. We selected 165 JAM lines (F7 in 2018 and F8 in 2019) with consideration of kinship, so as to not use more than three lines from the same eight-way recombinants. The 165 JAM lines were grown without replication according to standard procedures at National Agriculture and Food Research Organization in Tsukuba, Japan, in 2018 and 2019. The eight parental cultivars were grown with three replicates and assessed only in 2019. Seeds soaked in water at 28 °C for 2 d were sown in trays filled with soil and incubated at 30 °C in the dark for 2 d. Seedlings were grown in a paddy field in Kannondai, Tsukuba, for around a month, and then 33 seedlings per line were transplanted  into a nearby paddy field, and grown for five months.We previously developed the JAM population from eight founders ; image format, JPG; white balance, cloudy; aperture, auto; shutter, auto; exposure compensation value, −1. In terms of the altitude of the UAV flight, we tested which altitude was suitable to obtain images. The lowest altitude to fly the P4P automatically and stably in the field was 10 m. By comparing the images at an altitude of 10, 15, 20, and 25 m, the lowest altitude condition was the best for subsequent analysis , we generated an orthomosaic from each set of aerial images using the following steps, as described previously (Regions covering 3 × 3 plants of each JAM line and founders (450 × 270 pixels) in orthomosaic images were extracted for image analysis . RGB datThe nine square binary images were extracted from the original VF image and merged on the basis of average pixel values . The merPa_avg = average pixel value in region a (outermost)Pb_avg = average pixel value in region b (1 in from outermost)Pc_avg = average pixel value in region c (2 in from outermost)Pd_avg = average pixel value in region d (3 in from outermost)Pe_avg = average pixel value in region e (Innermost)Pall_avg = average pixel value in all regions (a + b + c + d + e)We dug up six plants of each line on each of four dates in each year, which were in the same lane as those for VF analysis . The plaP-values obtained from the statistical analysis were used for the Manhattan plot. To identify QTLs for phenotypes, we detected peaks of the −log10P values in the GWAS results by using the ‘findpeaks’ function (minpeakdistance = 100) in the ‘pracma’ package of R software.Haplotypes in the 165 JAM lines were estimated as in an Arabidopsis MAGIC population  of plants is defined as the angle between leaf sheath and ground, and was measured with a laser range finder  at the vegetative stage  between VF and SDW ranged from +0.4 to +0.7  9. This is near Tiller Angle Control 1 , which is a major regulator of tiller angle  and TW in both years, but not PW . At the and qVF9 . Intereswith SLW . These rqVF9 was detected as a QTL for TW and SLW. In terms of the plot pattern of the GWAS, many peaks with low -log10 P values were found, majority of which were not common in the two years. This is similar to the tendency of plot patterns in GWAS using SDW at the vegetative stage . This may reflect environmental conditions. In fact, we also found year-specific QTLs for VF, which may be responsible for this variation. Further investigation of VF after the fourth sampling date would not be meaningful because the canopy would become more closed, so differences in VF between lines would become small. For the accurate prediction of final biomass and yield, modelling using more phenotypic data such as normalized difference vegetation index, biotic/abiotic stress tolerance, plant architecture, and environmental data such as weather and soil, should be attempted.By contrast, it is desirable that lines with high biomass and yield can be selected from the population using digital data from UAV without genomic information. But the prediction of biomass and yield using only VF is likely to be hard, because the correlation between VF and SDW varied widely  and yield traits.Fig. S9. Effects of the four QTLs for vegetation fractions on panicle weight.Fig. S10. Haplotype-based GWAS regarding total weight, panicle weight, and stem and leaf weight.Table S1. Haplotype effect on total weight, panicle weight, and stem and leaf weight at the QTLs for vegetation fractions.eraa605_suppl_Supplementary_FigureClick here for additional data file.eraa605_suppl_Supplementary_TableClick here for additional data file.