Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity.●Sorghum bicolor, which vary extensively in numbers, lengths, and angles of primary branches, as well as the three‐dimensional shape, size, and distribution of the seed.Here, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of ●We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative.●However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history. Measurements of plant structures such as inflorescences or leaves for botany, crop breeding, or ecology studies most often use manual observation or the analysis of two‐dimensional (2D) images, the latter ranging from electron microscopy  was performed on individual panicles , with 1000 radiographs generated per sample rotation, for a total of 6000–8000 radiographs depending on the length of the panicle, and an average helical pitch of 78.94 mm. The X‐ray source was set to a voltage of 70 kV, current of 1000 µA, and focal spot length of 70 μm. The radiographs were then combined using the North Star Imaging efx‐ct software to generate a 3D reconstruction of each sample, with a final voxel size of 106–109 µm. Three‐dimensional reconstructions were then converted to slices for import into Matlab and downstream processing  node were cropped out. The grayscale intensity was then adjusted and standardized automatically with a marker  or semi‐automatically without the marker Fig. a. After th percentile (to avoid measuring underdeveloped seeds). SeedElongation and SeedFlatness were measured in two different ways: (1) computing the ratio of the voxel variation, and (2) fitting an ellipsoid. We also treated the distance between the ellipsoid and seed (SeedEllipsoidError) as a seed morphology feature  traits, each distribution was treated as a 10‐dimensional vector. To determine statistical significance between the distributions of any two races, 1000 random permutations were run. At each permutation, the mean vector was calculated for each race, and the distance  between mean vectors of two races was calculated by using the L2 metric. The P‐value was calculated as the proportion of sampled permutations with i X> X0, where X0 is the original difference in means between the two races.For statistical analysis, phenotypic values of replicates were first averaged for each accession (unless otherwise indicated). For differences in means in univariate traits between botanical races, a Kruskal–Wallis test was first performed; traits with significant differences between any of the botanical races were then tested between every pairwise combination of races using a Mann–Whitney atlab using functions pca and clustergram. The R function cor.mtest and package corrplot  was first used to segment the seeds relative to branch segmentation. When seeds touched each other, seeds were computationally shrunk and re‐expanded to identify a morphological gap separating individual seeds. Seed morphological features were calculated by various approaches, including fitting an ellipsoid to the 3D image . Since seeds are roughly spherical, we computed the histogram of the radius along uniformly and densely distributed directions to extract finer details in seed shape than 2D images can capture Figs , S3a. BeNext, we developed a skeleton‐based method based upon the medial axis Blum,  to measuIn total, our methods retrieved 77 inflorescence and seed features , more than any other quantitative study on sorghum inflorescence phenotype to date; a full list of features, detailed descriptions, and sample measurements are shown in Methods c. 1.5 h per sample, Methods R2 with their digital counterparts  diameter had a weaker correspondence between manual and digital methods (R2 = 0.81), likely due to the rachis cross‐section often being more ellipsoidal or irregular rather than a perfect circle, creating uncertainty in the manual measurements. The remaining manually measured features related to primary branch or internode traits and had R2 values between 0.64 and 0.92. Here again, traits that could more easily be measured by hand (e.g. primary branch tip angle) showed stronger correspondence between manual and digital methods (R2 = 0.92), whereas more tedious or difficult measurements such as longest internode length or second longest internode length  showed lower correspondence, with R2 values of 0.85, and 0.84 respectively. Indeed, most of the digital measurements are likely more accurate than manual measurements, except for primary branch number and first primary branch length, where the digital measurement has relatively higher uncertainty due to challenges in identifying and segmenting all primary branches. Similarly, average primary branch length showed a lower correspondence (R2 = 0.64) possibly due to difficulties in both digital and manual measurements, and manual measurements took the average of nine random branches as opposed to the larger proportion of branches measured by the digital method. Overall, however, the consistency between digital traits and manually measured traits is high enough to give confidence in the measurements, with most instances of lower correspondence being explainable by the aforementioned challenges, primarily among manual methods.To compare and validate the traits that could be estimated manually, we measured 10 traits by hand for 20 individual panicle samples , and between panicle flatness and panicle width , which implies flat, wide panicles are more common than round, broad ones , but slightly negatively correlated to average seed volume , indicating a generally inverse relationship between seed number and seed size in sorghum. On average, seeds also tended to be farther from the rachis when the panicle is longer . We also found relationships between traits that, though not surprising, are difficult to measure manually, such as between the longest internode length and average seed‐to‐rachis distance , and the inverse correlation between 3D panicle solidity and 3D panicle convex hull volume . Finally, our measurements also indicate that seed shape is generally independent of other panicle features.Next, we computed the relationship between each pair of the 77 digital traits using Spearman’s rank correlation coefficient nes Fig. . Total sρ = 0.95, P = 5.45 × 10−29), or 3D panicle solidity and 2D panicle solidity  is verified by the common observation of panicles that were either short and dense or long and sparse. Denser primary branches correlated with shorter internode lengths , whereas wider panicles tend to have greater tip angles and lower primary branch densities along the stem. This suggests that open branches would result in wider and sparser panicles, and that panicles with greater tip angles and branch density also have lower internode lengths. Note that our primary branch detection algorithm performs better for wider panicles that have fewer intersections between branches.Giving confidence to our digital measurements, correlations between 3D features and the closest analogous 2D features were high, such as in the case of 3D convex hull volume and 2D convex area , and within these traits all but one had significant differences in variance between two or more pairs of botanical races  as a vector and tested for significant differences between botanical races using 1000 random permutations Figs , S9. ForTo compare the overall differences in inflorescence traits, we calculated the mean value of every trait according to botanical race and performed two‐way hierarchical clustering Fig. . ClusterTo assess variation both within and between botanical races across all phenotypes, we performed PCA using all 77 traits Fig. . The firk‐nearest neighbor. PCA–LDA, using the values of the first eight principal components from all 77 traits  as inputs into an LDA, performed best , whereas comparing the kinship matrix with just the seed morphology matrix had a somewhat stronger, but still relatively low, correlation . To determine whether this can be affected by feature selection, we performed similar comparisons using increasingly stringent subsets of traits for constructing the morphological matrix, based on the coefficient of determination R2 between accession replicates , our analysis using XRT 3D imaging additionally demonstrates a phenotypic gradient even for specific inflorescence features (such as branch lengths) that cannot be reliably measured using 2D imaging due to the occlusion of branches, which prevents accurate representation of the panicle branching hierarchy.When compared with manual branch phenotyping methods, our skeleton‐based pipeline calculates morphological traits from branches that more completely and accurately represent each panicle. Our computational method nondestructively captures branches evenly distributed throughout the panicle, as opposed to hand‐picked branches that may be biased. By using the thinned, 1D curve structure of the skeleton, the topology of the shape and the semi‐automatic annotation of the stem and primary branches are more amenable for characterization, while retaining information of value. An additional advantage of digital characterization is improved objectivity, as manual measurement of traits such as branch angle, selection of the nine primary branches for average lengths, and node distances can be subjective. Although digital methods also need individual users’ choice of parameters, the space of such choices is significantly smaller than those in manual measurements.In panicles with large tip angles, the longest‐path approach of our skeleton‐based method performs exceptionally well because of the lack of intersections between different branches within the shape. However, for compact panicles, the path of a single branch may appear to intersect with several others in the image. This may cause the detected branch to wind between several actual branches. Although branch length, curvature, tortuosity, and tip angle constrain the predicted branch path to be approximately correct, small deviations will inevitably cause parts of multiple branches to be included in the same path. In future work, we plan to explore methods from combinatorial optimization to better address such topological issues, potentially by clustering ‘branch‐like’ parts of the skeletons together.Another advantage of XRT imaging for inflorescence phenotyping is that all the seeds within a panicle can be segmented and isolated with their entire 3D shape, a task that would otherwise be laborious, if not impossible, by optical imaging without the aid of robotics. Our analysis found that seed shape and size are among the more distinctive traits of the sorghum botanical races, thus illustrating the potential for high‐resolution mapping of seed traits. Additionally, within the genotypes analyzed, we found some support for a trade‐off between seed number and seed size, a long‐standing concept in biology that is often difficult to confirm empirically due to confounding factors  across our samples, indicating that, despite their convenience, they do not accurately reflect the quantitative nature of inflorescence variation present in sorghum. Future studies can be scaled to diversity panels or other mapping populations with reasonable expectations for finding potentially novel quantitative trait loci controlling the myriad of sorghum inflorescence features we have described here.CNT, EAK and TJ designed the experiment. M‐RS performed sample collection and imaging. ML, DZ and M‐RS performed feature extraction and analysis. M‐RS, ML and DZ wrote the manuscript with contributions from CNT, EAK and TJ. ML and M‐RS contributed equally to this work.Fig. S1 Examples of sorghum panicle morphology.Fig. S2 Additional details on X‐ray imaging workflow.Fig. S3 Diagrammatic description of seed distribution features.Fig. S4 Thresholding considerations with primary branches.Fig. S5 Stem identification process.Fig. S6 Branch identification process.Fig. S7 Primary branches and rachises of representative sorghum panicles.Fig. S8 Trait correlation plot.Fig. S9 Distribution and variation for all traits among the five races.Fig. S10 Principal component analysis and linear discriminant analysis loadings.Fig. S11 Isometric feature mapping with all traits.Fig. S12 K‐mean clustering.Methods S1 Additional methods on primary branch and rachis trait extraction, skeleton generation, stem identification, primary branch identification and measurements, and manual panicle measurements.Click here for additional data file.Table S1 Germplasm and genotypes. Table S2 Trait descriptions.Table S3 Complete trait value data.Table S4P‐values for univariate traits.Table S5P‐values for distribution traits.Table S6 Classification results from supervised methods.Table S7 Coefficient of determination between replicates.Table S8 Kinship‐to‐morphology Mantel test.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Click here for additional data file.


Rainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits.A pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits.Our genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection. Oncorhynchus mykiss) is a worldwide cultured salmonid species with numerous breeding programs implemented over the last 50 years in closed populations. Breeding goals, i.e., the number, the nature and the importance given to the selected traits, are an essential feature of a breeding program because this determines the direction and extent of genetic trends in the population under selective breeding. The traits of interest can be recorded either directly on candidates for selection, as in the case of growth and morphology traits or on their sibs when the trait measurement requires a fish to be killed, such as assessment of disease resistance or processing yield. Until the 2000s, commercial lines have been mainly selected for growth traits by mass selection and external morphology assisted by ultrasound to improve gutted yield [Rainbow trout  was not considered as a limiting factor . HoweverReproduction is the basis of all animal production systems. Maintaining or improving reproductive efficiency is essential to the productivity of animal farming. A high reproductive capacity is also one of the main levers for the genetic improvement of the species. It is therefore important to estimate the performance and the genetic trends for reproduction traits, especially since genetic antagonisms are feared with production traits in all species .High selection for production traits in closed and small broodstock populations of rainbow trout have also induced significant levels of inbreeding . PreviouEarly literature reported estimates of genetic parameters for egg production trait in rainbow trout –12 or AtSince early 1990’s, several breeding programs were developed in France based on combined mass selection on growth and sib selection to improve processing traits and, more recently, disease resistance . The aimEstimates of genetic parameters are summarized in Table Our estimates of correlations for the two measures of female body weight with (FW) or without (PW) consideration of the spawning and coelomic liquid weights showed that the two measures were essentially describing the female own weight since we estimated a phenotypic correlation between FW and PW of 0.96 with a genetic correlation close to unity. Regarding correlations between reproduction traits, it should be first mentioned that genetically speaking, EN and SW on one hand, and EW and ED on the other hand corresponded to almost the same traits with genetic correlations (rg) estimated to 0.86 (±0.05) and 0.99 (±0.02), respectively. All those high correlations are partly due to the auto-correlations originating from methods of deriving these traits from common measures of biological components, as shown by the high environmental correlations observed between those pairs of traits (0.82 between EN and SW and 0.55 between EW and ED in our study).Although EN and SW were highly correlated in our study, those two traits did not behave similarly with ED and EW. On the contrary, ED and EW could be considered as two different measures of a unique biological egg size trait, those two traits being associated in a very similar manner to all the other traits in the analysis. SW was significantly and positively associated with egg size traits (rg in the range 0.5–0.6), while EN was genetically uncorrelated to egg size traits (rg in the range 0.01–0.10) in our study. Both EN and SW traits were genetically weakly correlated to SD (0.22 and 0.34 respectively). Intermediate positive genetic correlations were also estimated between egg size traits and SD.Regarding the correlations between SD and PW (or FW), we did not observed any significant phenotypic or genetic associations in our study. The female body weights were also not genetically correlated to any of the egg size traits (EW and ED) or egg quantity traits (EN and SW). Considering FW as a covariate in the modelling of EN and SW led to negative genetic correlations between FW and the adjusted EN* and SW* while regressing towards 0 the corresponding phenotypic correlations.With the only exception of SD, no QTL explained over 3% of the genetic variance Table . TherefoEfficiency of genomic selection in comparison to pedigree BLUP selection is presented in Fig. Whatever the trait and the scenario, we noticed a slight tendency to overestimate (b < 1) the variation of GEBVs compared to the genetic variance although all inflation coefficients were statistically not different from 1  of values reported in recent studies in the same species for female body weight between 13 and 25 months –19. In aHeritability estimated for SD in our study was moderate in comparison to the large values (0.49 to 0.87 with a pooled heritability of 0.65) estimated by Su et al.  for theiAll our heritability estimates for rainbow trout reproductive traits  were in general close to those of Gall and Neira  in Coho Regarding correlations between reproduction traits, the high correlation we estimated between EN and SW was also observed in Coho salmon . HoweverConsidering genetic correlations between egg size traits and SD, our estimates were consistent with the estimate (0.51) in Su et al. ’ study wRegarding the correlations between SD and female body weights, our results were in contradiction with the significant positive genetic correlation (0.51) reported by Su et al.  but veryAlthough female body weights did not significantly correlated neither to egg quantity traits  or egg size traits  in our study, significant positive genetic correlations were found for PW with EN (0.47), egg size (0.51) and egg volume (0.67) in US lines of rainbow trout . Gall anEarlier studies indicated that selection for body weight at any age would result in a correlated response for body weight at other ages including age at slaughter and age at spawning for females  and thatThe credibility/confidence intervals associated with the QTLs were large in most cases, which precluded the meaningful identification of potential underlying candidate genes that may explain the phenotypic variations observed for female reproduction traits. Nevertheless, based on the functional information given in the human gene database GeneCards® , 22 and Fitness traits such as spawning date and body weight are major factors in the life history of salmonid fishes. Despite the fact that some recent QTLs studies have focused on growth traits in rainbow trout , 19 the p-value < 10− 6 at the genome level) SNP was found for SD on Omy6. It corresponds to the only significant QTL for SD under GBLUP analysis and this single SNP explained over 7% of the genetic variance under BayesC approach. The SNP Affx-88,920,061 is located within the importin-11 gene  that plays a receptor role in nuclear protein import. The phenotypes observed in a MGI mouse strain (ID MGI:5617259) with a mutation in this gene may help to understand the effects of a variant allele may have on spawning date in rainbow trout. The double mutant homozygote is a pre-weaning lethal phenotype in mouse and the heterozygote exhibits decreasing levels of iron and glucose levels in the blood. In addition, the heterozygote male had an abnormal eye (lens) morphology. However we may wonder whether the association is a spurious one due to a very high rate of mendelian errors at this SNP position when comparing genotypes from progeny and their parents. Indeed, 104 mendelian errors were observed at this SNP while the medium number of mendelian errors was only 3 for the 4067 SNPs  with at least one mendelian error detected.In our study, the highest significant  and Omy12  were detected, confirming that the two traits are biologically very close. The QTL on Omy2 explained 3.2 and 3.0% of the genetic variance for EN and SW, respectively. The QTL on Omy12 explained about 2.5–2.7% of the genetic variance for each trait. For EN, a third QTL was detected on Omy8 that explained 1.4% of the genetic variance. No obvious gene candidate could be proposed for this QTL. For SW, a third QTL was detected on Omy2 that explained 1.9% of the genetic variance and a last QTL was detected on Omy1 that explained 1.3% of the genetic variance. These last two QTLs for SW had large credibility intervals (> 1 Mb) and no obvious candidate gene could be proposed. Concerning the common peak SNP for the QTL on Omy2 shared by EN and SW, its location is in-between the MPRD gene (cation-dependent mannose-6-phosphate receptor-like) and the PHC1 gene (polyhomeotic-like protein 1). This PHC1 gene is a homolog of the Drosophila polyhomeotic gene, which is a member of the Polycomb group of genes. It is a component of a Polycomb group (PcG) multiprotein PRC1-like complex, a complex class required to maintain the transcriptionally repressive state of many genes, including Hox genes, throughout development . MGI homFor egg size traits measured by EW and ED respectively, we will focus on the three QTLs on Omy1 showing the highest consistency of results across GWAS Table . Among tRegarding EW, there was a strong evidence for a distinct QTL explaining 1.2% of the genetic variance, in the region spanning from 60.498 Mb and 64.633 Mb on Omy1 with the peak SNP very close to 64.633 Mb in an uncharacterized protein (LOC110527930). No candidate gene could be proposed within this QTL region, but in the close vicinity of this QTL region, let us mention the presence of the prkg2 gene (located between 64.660 and 64.676 Mb on Omy1) whose role is important in oocyte maturation in mammals and zebrafish . This prC. elegans mutants have an egg-laying defect and reduced brood size with 21% displaying embryonic lethality whilst 28% arrest at the larval stage [Regarding ED, there was evidence for another QTL on Omy1 that explained nearly 2% of the genetic variance in the region spanning between 70.848 Mb and 71.813 Mb. The peak SNP (at 71.813 Mb) is close to the position (71.675–71.699 Mb) of the WAPLA gene (wings apart-like protein homolog) which is a straightforward gene candidate for explaining this last QTL on Omy1. Indeed this gene is a regulator of meiotic chromosome structure and function, playing a role in sister chromatid cohesion, cohesin association with chromatin, DNA double strand break repair and polar body positioning following meiotic divisions during oogenesis , 34. Wor2 = 0.4) in a pig line when increasing by 130% the training population size (starting from 550 sows) in a Large White line with effective population size estimated to be about 100 [The implementation of genomic selection in a breeding program for female reproduction traits would allow a gain in accuracy of 16 to 32% (depending on the trait studied) compared to BLUP selection when considering a reference population of about 1100 individuals. For a smaller reference population of 670 individuals, genomic selection will also be more efficient than pedigree-based selection. In the literature, several studies in salmonids , 35–38 rbout 100 .We showed a severe loss of accuracy when the degree of kinship between the training and the validation populations was less related and this was more important for BLUP than GBLUP. It is well known that close relationships between animals in the training and validation sets increase the accuracy of genomic predictions compared to the ones derived for an independent validation population . Under sIn our study, all female reproduction and weight traits were moderately heritable with spawn weight showing strong and favorable genetic correlations with number of eggs in the spawn and individual egg size traits (egg average diameter or weight). On the contrary, number of eggs in the spawn was uncorrelated to egg size traits and female body weight (just before or after spawning) was not genetically correlated to any of the reproduction traits. Therefore it is unlikely that selection for growth will induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs.The GWAS results suggested that female reproduction traits are highly polygenic. Only six QTLs over the 19 identified across the five traits studied explained at least 2% of the genetic variance. These results suggest that gene-assisted selection will be useless for improving reproduction traits. However, genomic selection based on a reference population of only one thousand individuals related to candidates may improve the efficiency of BLUP selection from 16 to 37% depending on female reproduction traits. Cross-validation test of genomic prediction highlighted the clear increase in prediction accuracy compared to that of pedigree-based prediction in almost all population samples. The accuracy of GBLUP was the highest when training and validation sets were closely related but the relative advantage over pedigree-based prediction within a population was the largest when relationships were more distant.Phenotypes were collected at 2 years of age in females from two successive cohorts produced in 2014 and 2015, hereafter named C1 and C2, and composing the 9th generation of selection of the trout breeding company “Viviers de Sarrance”. Those cohorts constituted the broodstock of the company and came from two related paternal cohorts S1 and S2 and from different groups of dams D1 and D2 of the same maternal cohort produced in 2011 , its post-spawning weight (PW), the weight of the total egg mass hereafter called the spawn weight (SW), the length of 50 eggs aligned along a graduated rule, the weight and the number of eggs in a sampling spoon of 2.5 ml, the spawning week number in the calendar year and the presence of overmature eggs in the spawn. All performance records which were more than 4 standard deviations from the mean in absolute value were considered as outliers and discarded from the study. Analyzed traits were the raw phenotypes FW, PW and SW and four derived phenotypes: egg average diameter (ED), egg average weight (EW), egg number of the spawn (EN) and spawning date (SD). ED was calculated as the length of 50 aligned eggs divided by 50. EW was derived as the ratio of the weight of eggs to the number of eggs in the sample of 2.5 ml. EN was calculated as the ratio of SW to EW. SD corresponded to the rank of the week number within the spawning period, with discrete values ranging from 1 (for the first week) to 5 (for the 5th and after weeks) within cohort. In total, the phenotypes of 1517 fish were considered in the study  were genotyped for 57,501 SNPs (Single Nucleotide Polymorphism markers) with the Axiom™ Trout Genotyping array  at the Ip-value > 0.0001 and a minor allele frequency higher than 0.05 were retained for the analysis. All missing genotypes for the 29,799 SNPs were imputed using the FImpute software [Quality controls of genotyped SNPs were performed as described in D’Ambrosio et al.  in partisoftware .Mixed linear BLUP - animal models were used to get the estimated breeding values (EBV) and to estimate genetic parameters based on pedigree information.yi of the 1346 fish:βi, ui and ei are the vectors of fixed environmental effects, genetic additive effects and residual effects explaining the performance of all phenotyped animals, respectively. X and Z are the incidence matrices for βi and ui, respectively.For any trait i among the six reproduction traits considered in the study, the following statistical model was considered to describe the vector of performance For all the traits, the cohort group (C1 or C2) was considered as the main fixed effect. For the traits FW, SW, EN and ED, an additional fixed effect due to the presence of overmature eggs was significant and therefore considered in the models. The week of spawning had also a significant effect on the traits FW, PW, SW, EN, EW and ED and was considered as a covariate factor nested within cohort.Because the targeted breeding goal was to increase egg production at a constant weight of female, additional genetic and genomic analyses of SW and EN were performed considering those two traits adjusted (SW* and EN*) for female weight (considered as a covariate factor within cohort).ui corresponded to the breeding values of 15,265 individuals related through the pedigree relationship matrix A.Tracing back over 8 generations the pedigree of the 1346 phenotyped fish, the vector To estimate all variance components by Average Information Restricted Maximum Likelihood algorithm , eight mThe EBV were estimated using BLUPf90 package and the variance components using AIREMLf90 program .G [A. The performance modeling is the same as for the BLUP model described in eq.  is an analogous approach to BLUP considering a genomic related matrix G  in placed in eq. , but onlG matrix and the genomic estimated breeding values (GEBV) were estimated using BLUPf90 package [The  package .âi) from the genomic breeding values d is the vector of weights associated with the SNP effects.Genome-wide association studies (GWAS) were performed considering the GBLUP models to identify QTL. The postGSF90 module  of the B10 for a SNP of this region was equal or greater than 5.0 (which corresponds to a chromosome-wide significance threshold of 1% derived as -log10(0.01/(n/30)) after Bonferroni correction with n = 29,799 the total number of SNPs included in the analysis). The peak SNP was considered as very significant when its -log10 was at least equal to -log10(0.01/n) = 6.5, corresponding to a genome-wide significance threshold of 1% after Bonferroni correction. Considering a drop-off value of 1.5 log unit of p-value [10 greater than the peak value −1.5.A region of the genome was considered to be a QTL when the -log p-value , the QTLp = 300 < n to correctly estimate p SNP effects simultaneously. We used the BESSiE software [A Bayes Cπ strategy  was usedsoftware  to perfoi, the probability of the ith SNP to have a non-zero effect: The degree of association between each SNP and phenotypes was assessed with the Bayes Factor (BF) that involves π and PAs proposed by Kass and Raftery , the logOncorhynchus mykiss Annotation Release 100 (GCF_002163495.1).Candidate genes located within the confidence or credibility intervals estimated using either GBLUP or BCπ analysis were listed from the NCBI To assess the BLUP or GBLUP selection efficiency, cross-validation tests were performed for different scenarios varying the training population size and its relatedness to the validation population. Efficiency was assessed by the accuracy of selection and the inflation coefficient of EBV as a measure of selection bias. The accuracy of selection was derived as the correlation between (G) EBVs of individuals in the validation set and their corrected phenotypes (adjusted for the covariate and fixed effects) divided by the square root of the trait heritability . The infTo test the training size effect, 40 replicates of Monte-Carlo ‘leave-one-group-out’ cross-validation tests  were runTo test the relationship effect between the training and validation populations, the C1 and C2 fish were alternatively used as the training and validation sets, the scenarios T1 and T2 corresponding to C1 and C2 fish in the training sets, respectively Table .Additional file 1 Supplementary Figure 1. Manhattan plot of QTL detected under GBLUP-based GWAS for female reproduction traits. The red line corresponds to the genome-wide significance threshold at 1% after Bonferroni correction and the blue line corresponds to the chromosome-wide significance threshold at 1% after Bonferroni correction. SD: spawning date; SW: spawn weight; EN: egg number; EW: average egg weight; ED: average egg diameterAdditional file 2 Supplementary Table 1. Mean and standard deviation over 40 replicates (in brackets) of selection accuracy (r) and inflation coefficient (b) of EBVs and GEBVs for training scenarios T+ and T-.SD: spawning date; FW: female body weight; SW*: spawn weight (adjusted for FW); EN*: egg number (adjusted for FW); EW: average egg weight; ED: average egg diameter. Supplementary Table 2. Selection accuracy (r) and inflation coefficient (b) of EBVs and GEBVs for training scenarios T1 and T2. SD: spawning date; FW: female body weight; SW*: spawn weight (adjusted for FW); EN*: egg number (adjusted for FW); EW: average egg weight; ED: average egg diameter.


Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals , P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals , P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention. Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability. In a standard GWAS, such associations are usually evaluated for many genome-wide single-nucleotide polymorphisms (SNPs), one at a time, based on data from a large number of individuals. For most complex traits and diseases, the effects of a single SNP are small, and the proportion of phenotypic variance explained by genome-wide significant SNPs is likewise small4. Therefore, an increasing interest lies in the prediction of future phenotypes for such traits from combined effects of a large number of genome-wide SNPs, as known as a whole-genome approach to genetic prediction8. There have been a number of such approaches that jointly model all or most of common variants across the genome. For instance, genome-based residual maximum likelihood (GREML)10 can be used to estimate SNP-based heritability, i.e., the proportion of phenotypic variance explained by genome-wide SNPs. Best linear unbiased prediction (BLUP) can fit a genomic relationship matrix to estimate the genetic effect on the phenotype of each individual, and this method has been termed genomic BLUP (GBLUP)15. Linkage disequilibrium score regression (LDSC)16 use aggregated effects from GWAS summary statistics of genome-wide SNPs to estimate SNP-based heritability and predict the future phenotypes of target sample for complex traits20.Genome-wide association studies (GWAS) have uncovered many common variants associated with complex traits22. This convention has generally been extended to genomic prediction studies, which use similar population-based designs as GWAS24. However, the purpose of prediction should be clearly distinguished from that of heritability estimation. The aim of genomic prediction is to maximise phenotypic prediction accuracy. Unlike for the estimation of heritability, it is not critical to disentangle additive genetic effects from other common family effects in a genomic prediction context. In fact, such family effects could be a valuable source of information to improve prediction accuracy. Therefore, excluding close relatives for phenotypic prediction may not be well justified.Most existing GWAS use population-based designs, in which close relatives are typically excluded or devalued from the analyses to avoid bias due to common family effects, i.e., biased SNP effects or inflated SNP-based heritability due to confounding between additive genetic and family effects. Especially when estimating narrow-sense heritability based on the genome-wide SNPs, individuals with pairwise genomic relationships >0.05 are usually excluded28. In these studies, it was shown that prediction accuracy depends on the effective number of chromosome segments (Me), also known as the number of independent SNPs. Me is a function of effective population size (Ne) and it decreases when the number of high relationships between reference and target samples increases, which improves the phenotypic prediction accuracy28. Several studies have shown that family information is useful for polygenic risk prediction31. However, to our knowledge, there is no large-scale study to verify the efficiency of using relatives in polygenic risk prediction and its implications for clinical practice.Theoretical studies have demonstrated that information from close relatives could improve prediction accuracy, even in the absence of familial environmental effectsTo predict the polygenic risk scores (PRS) of a new person for which we have DNA data, all available data in the biobank should be utilised, including any phenotypes of individuals that have a pedigree relationship with that person. Here, we use UK Biobank data and show an efficient polygenic prediction when we use relatives in a PRS approach to predict phenotypes of complex traits. We perform GWAS for 50 human complex traits including 12 disease traits using genotype and phenotype data in the reference data and use the estimated SNP effects to obtain PRS for the target sample. We investigate the contribution of information from the relatives of the target sample in polygenic risk prediction. The 50 traits are further categorised into three groups of mental, physical and lifestyle traits, and we assess the prediction performance for each group as family effects varies between the categorised traits. In addition, we extend our approach to integrate phenotypes of ungenotyped relatives of the target sample to explore whether this can further increase the prediction accuracy. We show that the efficiency of polygenic prediction with close relatives, despite a 44-fold lower in sample size, is equivalent or even higher (depending on traits) than that with unrelated individuals. This result suggests that polygenic prediction integrating family information will be a useful tool for precision health and preventive medicine.Me and the sample size of reference data set32 (see ‘Methods’). The lower the Me value, the higher the prediction accuracy is. Me is a function of effective population size, and can be empirically estimated from the variance of genomic relationships between the reference and target samples27 (‘Methods’).Genomic prediction accuracy, defined here as the correlation coefficient between the phenotypes and estimated PRS, can be determined theoretically by heritability, 33. These traits can be categorised as mental, physical and lifestyle traits. Trait name, type and SNP-based heritability estimated based on various information sources are shown in Supplementary Table 33 . We randomly divided these unrelated individuals into discovery (80%) and target data sets (20%). We refer to this design as a ‘large-scale design’ . The proportion of relatives, hence the variance of relationship, was thereby increased in the small-scale design, compared to that in the large-scale design . We also estimated Me and heritability using Eq. (36 (see ‘Methods’).In the target data set, prediction accuracy was empirically calculated as the correlation between the polygenic scores based on SNP effects estimated in the discovery data setsing Eq.  (‘Methodsing Eq.  ('MethodH-matrix37 (‘Methods’) that include the relationships between genotypes and ungenotyped individuals as well. We fit the H-matrix in a linear mixed model to obtain polygenic risk scores for the target individual using BLUP approach38, which is referred to as HBLUP40. We compare the prediction performance of HBLUP and GBLUP that is based on genotyped individuals only and analogous to PRS approach.Finally, we show how to utilise ungenotyped individuals to increase prediction accuracy further. Ungenotyped siblings of a target individual may have phenotypic information for the trait of interest that is useful to predict the target individual. In practice, the known pedigree relationships between a genotyped target individual and ungenotyped relatives of the target individual can be used to construct a (inferred) realised relationship matrix for all individuals, including ungenotyped relatives (see ‘Methods’). This realised relationship matrix is named as e is not surprising because the number of substituted individuals for the analyses with close relatives in the large-scale design was small , 2.80  and 4.67  when comparing the prediction accuracy of the analyses with third-, second- and first-degree relatives, respectively, to that of the analysis with unrelated individuals only .In the small-scale design, we observed significant fold changes of 2.62  or unrelated (TB) to the reference samples, which were considered in the same prediction analysis for a fair comparison .When reference sample size increased from 5000 to 10,000 or 15,000 for the analysis with first-degree relatives, the prediction accuracy increased further  of the target individuals in the reference data. When adding 2nd or 3rd degree relatives, a higher number of relatives are required to obtain the same Me value , 1.94  and 2.28-fold change  in the comparison of the empirical prediction accuracy to the theoretical prediction accuracy for the analyses with third-, second- and first-degree relatives, respectively, in the small-scale design , 1.38  and 1.41 fold change  for third-, second- and first-degree relatives, respectively , as expected. However, when using analyses with close relatives, the difference between the large and small-scale design became negligible, i.e., no difference for the analysis with first-degree relatives    Fig. , which iP-value from a two-tailed paired t test = 1.48E-05) and 1.81  when using unrelated individuals in the large-scale design and first-degree relatives in the small-scale design, respectively  in the top 0.05% of PRS , we assumed that only a random half of the sample was genotyped, and the other half was not genotyped. Among the genotyped individuals (n = 3000), 2000 and 1000 individuals were used as discovery and target sample, respectively, in the prediction using the PRS approach or GBLUP (see ‘Methods’). We also used HBLUP that could additionally utilise the information from the ungenotyped relatives (n = 3000) of genotyped individuals (n = 3000). The prediction performances across the methods, PRS, GBLUP and HBLUP, were compared. Figure P-value from a two-tailed paired t test = 0.48). When including ungenotyped individuals, HBLUP outperformed other methods. For instance, with 3000 ungenotyped individuals, the prediction accuracy achieved by HBLUP with additional ungenotyped individuals was better than GBLUP with only 2000 genotyped individuals   Fig. . The pre09) Fig. .Fig. 7PrMe that required fewer independent parameters to be estimated27. Moreover, this higher prediction accuracy was also probably due to the fact that close relatives share some unknown (unmodeled) factors in addition to additive genetic effects, which may be dominance, gene-by-family interaction and familial environmental effects. It was also shown that the analyses with second- and third-degree relatives outperformed the analysis with unrelated individuals although they were less efficient to improve the prediction accuracy, compared to first-degree relatives.We demonstrated that the polygenic prediction utilising close relatives between reference and target samples outperformed the analyses with unrelated individuals only by using the small-scale design. Compared with the analyses with second- or third-degree relatives, or unrelated individuals, a higher prediction accuracy was observed from the analysis with first-degree relatives, which was because of a lower value of 3, which can be considered biased according to the concept of narrow-sense heritability that includes the additive genetic effects only. However, this bias should not be an issue when predicting the future phenotypes of target sample  because such nonadditive genetic and common environmental effects can be a valuable source to improve the prediction accuracy42. Indeed, family history has been widely used as a biomarker to predict disease risk44, and it can also be used to increase the power to identify causal variants in GWAS47. We consider that our method is a more systematic approach to utilise information of family history as well as within-family segregation48.The approach of including close relatives will be most useful in applications where accuracy matters more than delineating between causal genetic effects and other effects. It is known that family-based heritability estimates can be inflated if nonadditive genetic effects or common environmental effects shared between close relatives are confounded with additive genetic effects49. This suggests that polygenic prediction should be based on information from close relatives particularly for lifestyle and behavioural traits.The prediction performance with close relatives varied, depending on traits. For example, the prediction accuracy from the analysis with first-degree relatives in the small-scale design (a discovery sample size of 5000) was significantly higher than the prediction accuracy with unrelated individuals in the large-scale design  for lifestyle  traits such as drinking, smoking and qualification. However, this was not observed in mental or physical traits. This observation agrees with a previous study showing that educational achievement is more similar in dizygotic twins, compared with a mental trait such as neuroticism scores51. For instance, Khera et al. reported that the top 2.5% (high-risk group) identified by PRS was at fourfold changed risk compared with the remaining 97.5% for coronary artery disease, which is a similar predictive power when comparing carriers and non-carriers of a rare monogenic mutation associated with increased cholesterol7. Our finding emphasises on an implication of using close relatives that can increase the prediction accuracy substantially, compared with the existing PRS approaches. In the near future, it is likely that more close relatives can be genotyped, and this information should be efficiently used in clinical care.Previous studies reported the potential of PRS in clinical practice41. However, to our knowledge, this has never been verified in human population studies in the context of polygenic risk prediction. Here, we explicitly verified the approach that could enhance the predictive power, using a large-scale human biobank data. We show that phenotypic information of ungenotyped relatives can be useful in polygenic risk prediction, which may have important implications in clinical practice.We investigated if the predictive power increased when using ungenotyped relatives of target individual in polygenic risk prediction. Utilising information of ungenotyped relatives has been widely used in the genomic prediction of economic traits in other species, such as cattle52. Thirdly, although it is well established how to obtain the theoretical prediction accuracy based on genotypic infromation53, or based on pedigree information 54, there is no unified theoretical approach to derive the expected prediction accuracy that can be applied to combined genotyped and ungenotyped samples, i.e., in HBLUP framework. Lastly, HBLUP is computationally demanding, which prevents using the ungenotyped relatives of target individuals in a large-scale data. It is required to develop an efficient HBLUP method, i.e., based on summary statistics.There are a number of limitations in this study. A potential caveat of our analysis is the limited number of relatives in the data . This limitation obscured the actual predictive power as the number of relatives of each target individual should have been more than that from the UKB data that only include genotyped samples. It may be possible to trace the information of relatives of the genotyped individuals in the UKB data, and HBLUP can be used to integrate the information even though relatives do not have genotypic information, which is, however, beyond the scope of this study. We anticipate that the number of relatives will increase as the scale of biobank data increases. Another limitation is that the number of lifestyle traits is only four and a further study may be required to confirm the finding about the prediction of lifestyle traits. It is also noteworthy that our study focused on individuals with European ancestry only. More studies on other ethnicities will be desirable. This caveat has been recently raised when a PRS application comes to clinical practice52 and commercial genotyping databases 55 have clinical measures or can be linked with existing national clinical databases with relative information available. Moreover, prenatal genetic tests with information from close relatives have shown a prospective to provide insights for several phenotypes57. In the near future, it is likely that there is a high probability of finding genotyped close relatives of a random sample58, and the prediction of their (future) phenotypes benefits from the information of already known genotypes and phenotypes of their relatives. Here, we show how to use the information of relatives and highlight the importance of their phenotypes and genotypes in polygenic risk prediction. Our findings will have a useful implication for future investigations into precision health and preventive medicine.Polygenic risk scores based on genome-wide SNP information will provide useful information to predict the future phenotypes of target individual, which allows an early prevention of complex diseases. The cost of genome-wide genotyping has been dramatically reduced in the last decade, and multiple genotyping services are publicly available. In fact, genomic databases such as biobank datasets UK Biobank’s scientific protocol has been reviewed and approved by the North West Multi-centre Research Ethics Committee (MREC), National Information Governance Board for Health & Social Care (NIGB), and Community Health Index Advisory Group (CHIAG). UK Biobank has obtained informed consent from all participants. Research Ethics approval was obtained from University of South Australia Human Research Ethics Committee (HREC).59. For each individual, a trained nurse or an automatic device undertook a series of anthropometric measurements and surveys. In our study, a stringent quality control protocol was applied. SNPs were excluded according to the following criteria: INFO score < 0.6, MAF < 0.01, Hardy–Weinberg Equilibrium P-value < 1E-7 and missingness > 5%, one SNP was randomly chosen to keep if there is duplicated SNPs. We only used SNPs from HapMap 3 due to their reliability and robustness to bias in the estimation of narrow-sense heritability. In terms of individuals filtering, individuals with a genotype calling rate <0.95 were excluded. We performed analyses on the samples of white British only. These filters remained 1,133,273 SNPs and 408,218 individuals. In addition, we removed ambiguous or duplicated SNPs. We calculated the discordance rate between imputed genotypes of the first and the second release of UKB data, and individuals and SNPs with a discordance rate larger than 0.05 were removed. Moreover, we excluded individuals whose first- or second-principal components exceeded 6 standard deviations from the population mean (white British). We also randomly excluded one individual from any pair of related individuals with a genomic relationship larger than 0.05. After these QC steps, 288,837 unrelated individuals and 1,130,918 SNPs remained.The UK Biobank (UKB) enrolled 488,377 individuals and 92,693,895 imputed SNPs across autosomesP-values from a Wald test <0.05) reported by Neale lab33. The number of individuals with available information ranged from the lowest number of 237,191 individuals with heel bone mineral density (BMD) T-score (automated) (UKB ID = 78) to the highest number of 407,938 individuals with alcohol intake frequency (UKB ID = 1558). These 50 traits could be categorised into mental, physical and lifestyle traits.We analysed 50 complex traits  unrelated individuals only, (2) inclusion of first-, (3) second- and 4) third-degree relatives according to their kinship coefficients. In each analysis with relatives, we substituted the same number of unrelated individuals with the first-, second- or third-degree relatives available in the data. The kinship coefficients and (genomic) relationships used to classify the analyses in the large-scale design were derived in the following process.60. Level of relatedness is determined by kinship coefficient which is defined as the probability that a pair of randomly homologous alleles are identical by descent. Kinship coefficient is inferred by KING software version 2.161. Relatedness thresholds and the proportion of close relatives substituted in the sample of unrelated individuals for the analyses of first-, second- and third-degree relatives are described in Supplementary Table Genomic relationship matrix is computed by PLINK version 1.9With 288,837 unrelated individuals of pairwise genomic relationship < 0.05, we identified 279,020 individuals with available phenotypic information on average. For the large-scale design, we partitioned these unrelated individuals into a proportion of 80%  and 20%  for discovery and target samples, respectively. To compare with analysis with unrelated sample, we replaced unrelated pairs with close relatives identified by kinship coefficient. We introduced the first-, second- or third-degree relatives  unrelated individuals only, (2) first-, (3) second- and (4) third-degree relatives, in a similar manner as in the large-scale design, but with a small sample size. For the analysis with unrelated individuals only, we randomly selected 6000 individuals from 279,020 unrelated individuals. For the analyses with each level of relatedness, we used a graph and network analysis toolt test.To compare prediction accuracies between analyses, for instance, using first-degree close relatives in the large-scale design against small-scale design, we computed the mean fold change across a variety of different traits with its 95% confidence interval and assessed the statistical significance level whether the fold change was significantly different from 1 with a two-tailed paired Polygenic Risk Score (PRS): The phenotypes of each trait were adjusted for batch information, centre, sex, age and population stratification , using a linear regression. The pre-adjusted phenotypes were used for the following GWAS and PRS analyses. We estimated SNP effects by conducting GWAS for each of 50 traits using the discovery sample of 223,215 and 5000 individuals in the large-scale and small-scale design, respectively. PRS were calculated for the target individuals , as the sum of the risk alleles weighted by the estimated SNP effects from the GWAS using the discovery sample only. Then, we obtained the correlation between the PRS and pre-adjusted phenotypes in the target data set. For these analyses, we used PLINK version 1.960 and PRS were computed using PRSice version 2.1.1135.Genomic best linear unbiased prediction (GBLUP): We used GBLUP64 to generate polygenic score for each individual utilising the genomic relationships between individuals. GBLUP fits a genomic relationship matrix that is estimated based on the 1,130,918 genome-wide SNPs, which can be written asG is genomic relationship matrix, W is the matrix for individual genotypic information coded as 0, 1 or 2 and M is the number of SNPs. This analysis is conducted by MTG2 version 2.1565.A matrix-based best linear unbiased prediction using pedigree information (ABLUP): ABLUP can be used to estimate polygenic scores, fitting A matrix that is based on pedigree information only without genotypic information.Polygenic prediction for ungenotyped relatives (HBLUP): In the small-scale design, we randomly chose 3000 individuals in the discovery set to set as missing genotyped. We first reconstructed the pedigree from genotypic information by PRIMUS66 version 1.9.0 to obtain pedigree. After reconstructing pedigree, we removed individuals with ambiguous information identified by PRIMUS. With the reconstructed pedigree, we computed genomic-pedigree relationship matrix (H-matrix) from genomic relationship matrix (G) and numerator relationship matrix (A)41. A matrix was solely based on pedigree information. H-matrix is computed as follow67:37. We applied a BLUP approach to calculate polygenic scores fitting the H-matrix (HBLUP). All calculations were computed in MTG2 version 2.1565.h2), the number of effective chromosome segments (Me) and the sample size in the reference data set (N)32. Me can be empirically estimated as the inverse of the variance of genomic relationships between the discovery and target sample28. In the large-scale design, we estimated Me from samples who were available for standing height trait (UKB ID 50), and used the estimated Me to obtain theoretical prediction accuracies for the 50 traits. This was because the empirical estimation of Me was computationally demanding and samples available for other traits were mostly overlapping and homogeneous with those for standing height trait. To obtain the theoretical accuracy of genomic prediction, we used Equation 132. Pasanuic68 and Dudbridge53 et al. introduced a theoretical prediction accuracy when using random-effects model  although it is not substantially different from Eq. (1).The theoretical accuracy of genomic prediction can be derived, taking into account heritability  shows that Me plays a key role in the prediction performance in addition to h2 and N. A smaller number of independent chromosome segments can be estimated more accurately with the same number of records. Me is a function of effective population size and can be empirically estimated as27Gij is the genomic relationship between individual i in discovery and individual j in the target sample28. It is expected from Eq. (2) that including high relationships in G  reduces the values of Me, hence increases the prediction accuracy.Previous studiesMe value is also changed. We considered various numbers of relatives of a single-target individual in the discovery sample in both small- and large-scale design to analytically quantify how Me values were changed and to assess the prediction accuracy using Eq. (When adding relatives of a single-target individual in the reference data set, the variance of genomic relationships between the target individual and reference sample is changed, hence sing Eq. . In the 36 from a sample with familial relationships included both additive genetic effects and remaining family effects. We define two kinds of models and heritabilities asyj, gj, fj and ej is the phenotypic value, additive genetic effects, and familial effects, and residual effects for the jth individual, respectively. Similarly, Heritability can be estimated using unrelated individuals, whose covariances would be determined by the additive genetic effects only, therefore it is a narrow-sense heritability. On the other hand, family-based heritabilitymentclass2pt{minim16 to estimate narrow-sense heritability as it is appropriate to deal with a large number of individuals while estimating family-based heritability could be done by GREML10 using MTG2 version 2.1565 because of a small number of close relatives.We used LDSCFurther information on research design is available in the Supplementary informationPeer Review FileReporting Summary


The purpose of this study was i) to identify the characteristics of carcass traits in Chikso by gender, region, age at slaughter, and coat color using the carcass data collected from the nationwide pedigree information and coat color investigation, and ii) to estimate genetic parameters for breed improvement.A linear model was used to analyze the environmental effects on the carcass traits and to estimate genetic parameters. Analysis of variance was performed using TYPE III sum of squares for the unbalanced data provided by the general linear model procedure. Variance components for genetic parameters was estimated using REMLF90 of the BLUPF90 family programs.Phenotypic performance of carcass weight (CW), eye muscle area (EMA), and backfat thickness (BF) in Chikso were lower than those of Hanwoo. This is a natural outcome because Hanwoo have undergone significant efforts for improvement at the national level, a phenomenon not observed in Chikso. Another factor influencing the above outcome was the smaller population size of Chikso compared to that of Hanwoo’s. The heritabilities of CW, EMA, BF, and marbling score in Chikso were estimated as 0.50, 0.37, 0.35, and 0.53, respectively, which were was higher than those of Hanwoo.Based on the genetic parameters that were estimated in this study, it is expected that the carcass traits will improve when the livestock research institutes at each province conduct small-scale performance tests and the semen is provided to farmers after selecting proven bulls using the state-of-art selection technique such as genomic selection. Bos taurus coreanae) has been raised as beef cattle with a yellowish-brown coat. Other cattle such as Chikso (Korean Brindle Cattle), Heugu, and Jeju Black were reported as domestic beef cattle breeds by the Domestic Animal Diversity Information System of the Food and Agriculture Organization [In South Korea, Hanwoo  in South Korea, in cooperation with the Korea Animal Improvement Association (KAIA) and the regional district livestock research institutes, is investigating the pedigree information and coat colors of Chikso cattle in the country since 2013. Their projected goal is to establish Chikso as a breed and lay the foundations for industrialization [The purpose of this study was i) to identify the charac teristics of carcass traits in Chikso by gender, region, age at slaughter, and coat color using the carcass data collected from the nationwide pedigree information and coat color investigation, and ii) to estimate genetic parameters for the breed improvement.The data used in this study was collected by NIAS with the KAIA and the eight regional district livestock-related research institutes that investigated the pedigree information and coat colors of Chikso in the country in 2013. The photographs of the cattle were taken and classified into seven levels according to the standards set by the KAIA; the first level is when the brindle pattern is strongly expressed throughout the body; the second level is when the brindle pattern is expressed in the whole body but the expression level is weak; the third level is when the brindle pattern is expressed partially; the fourth level is no brindle pattern expression with a yellowish brown coat color; the fifth level is no brindle pattern expression with black coat color; the sixth level is no brindle pattern expression with a mixture of yellowish brown and black coat color; the seventh level is black color in eyes, nose, legs, and tail with a yellowish brown coat color [When the Chikso were slaughtered, grading results from the Korea Institute for Animal Products Quality Evaluation (KAPE) were derived based on the individual identification numbers. The grading results include the date at slaughter, breed, sex, slaughter house, grading date, CW, eye muscle area (EMA), backfat thickness (BF), marbling score (MS), meat color score, fat color score, texture, maturity, meat yield score, meat yield grade, final grade, etc. For statistical analysis, the individuals born from 2004 to 2016 which received a “pass” grade were used. The individuals that couldn’t be categorized with coat color were removed from the data. In addition, interquartile range criterion was applied for the removal of outliers. The following linear model was used to analyze the environmental effects on the carcass traits and to estimate the genetic parameters.y is CW, EMA, BF, and MS. μ is the overall mean, sex is the sex of Chikso , loc is the farm location, byear is the year of birth, bseason is the season of birth, sage is the age at slaughter, cc is the coat color, and e is the error.where Analysis of variance was performed using TYPE III sum of squares for the unbalanced data provided by the general linear model procedure . VariancAmong a total of 3,074 Chikso born between 1995 and 2017, 336 individuals (9.07%) culled before slaughter and 2,738 individuals were slaughtered between 2009 and 2018. There were 2,982 (80.51%) individuals with a “pass” grade, 2 (0.08%) with a “fail” grade, and 383 (10.34%) with “undetermined” due to the absence of graders in some of the slaughter house. 2, the average BF was 11.7±5.3 mm, and the average intramuscular MS was 3.4±2.01.Least square means (LSM) of sex and region effects show ing significant differences in all carcass traits are shown in The 2017 Hanwoo (Korean native cattle) grading results reported by KAPE  showed t2, respectively. These are lower than the EMA means of Hanwoo bulls, steers and cows slaughtered in 2017 reported by KAPE [2. The LSM of BF in steers and cows were 12.32 mm and 11.92 mm, which were not significantly different, and the LSM of BF in bulls was 5.04 mm. These statistics observe a similar tendency to the average BF in Hanwoo steers and bulls reported by KAPE [In EMA, the LSM of bulls, steers and cows was 87.43, 83.94, and 76.14 cm by KAPE , which s by KAPE , which w by KAPE . These s2 and 83.70 cm2 in Chungnam and Gangwon. The highest BF was 8.38 mm and 8.46 mm in Jeonnam and Gangwon, while the highest MS was 3.73 and 3.34 in Gyeongbuk and Gyeonggi, respectively. Gangwon, Chungbuk, Jeonbuk, and Gyeongbuk (Ulleung) produced their own semen and distributed them; however, the genetic ability of the distributed semen was not confirmed, and it was high proportion of the farmers that produce semen by themselves or do natural service, which seems to show inconsistent performance on regional carcass traits.Lee and Choy  comparedThe CW, EMA, and BF were not significantly different under the presence of brindle with black color on a yellow background, but MS was significantly influenced. The highest LSM of EMA in the 3rd level of coat color was 3.22 and the highest LSM of EMA in the 4th level was 3.20.Lee et al  comparedThe livestock-related research institutes at each province are producing semen based on coat color and distributing it to farmers. Therefore, semen with greater carcass traits performance should be provided to them.Carcass traits of steers are directly related to profit in the Chikso industry. The CWs increased continuously with an increase in months of age at slaughter. The EMA changed little until 31 months of age and slightly increased at 32 months of age.The BF increased until 31 months of age and decreased at 32 months of age.In the case of MS, the steady increase was observed based on months of age at slaughter, such as 3.7 at 29 months of age, 4.1 at 30 months of age, 4.3 at 31 months of age, 4.7 at 32 months of age.Phenotypic performance of CW, EMA, and BF in Chikso was lower than those of Hanwoo. This is a natural outcome because of two factors: first, there has been a consistent, large scale effort to improve Hanwoo unlike Chikso, and second, Chikso has a smaller population size. The heritabilities of CW, EMA, BF, and MS in Chikso were estimated as 0.50, 0.37, 0.35, and 0.53, respectively, which were higher than those of Hanwoo. Based on the genetic parameters that were estimated in this study, it is expected that the carcass traits will improve when the livestock research institutes at each province conduct small-scale performance tests and the semen is provided to farmers after selecting proven bulls using the state-of-art selection technique such as genomic selection.


In order to assess sources of variation related to Polverara breed plumage color (black vs. white), we carried out genome-wide analyses to identify the genomic regions involved in this trait. The present work has revealed new candidate genes involved in the phenotypic variability in local chicken populations. These results also contribute insights into the genetic basis for plumage color in poultry, and confirm the great complexity of the mechanisms that control this trait.FST) scan to uncover the genomic regions involved. A total of 37 animals (17 white and 20 black) were genotyped with the Affymetrix 600 K Chicken single nucleotide polymorphism (SNP) Array. The combination of results from GWAS and FST revealed a total of 40 significant markers distributed on GGA 01, 03, 08, 12 and 21, and located within or near known genes. In addition to the well-known TYR, other candidate genes have been identified in this study, such as GRM5, RAB38 and NOTCH2. All these genes could explain the difference between the two Polverara breeds. Therefore, this study provides the basis for further investigation of the genetic mechanisms involved in plumage color in chicken.Through the development of the high-throughput genotyping arrays, molecular markers and genes related to phenotypic traits have been identified in livestock species. In poultry, plumage color is an important qualitative trait that can be used as phenotypic marker for breed identification. In order to assess sources of genetic variation related to the Polverara chicken breed plumage colour (black vs. white), we carried out a genome-wide association study (GWAS) and a genome-wide fixation index ( Over the last century, erosion of livestock genetic resources has been observed as a result of the massive replacement of low-productivity local breeds with highly productive ones. These local breeds are nonetheless an important reservoir of genetic diversity, each with specific characteristics. Local animal genetic resources might indeed be characterized by specific heritable phenotypes potentially relevant for current or future use in breeding programs . SeveralIn Italy, there are numerous known local chicken breeds whose overall conservation status is nevertheless critical; with the abandoning of farming in marginal areas and the advent of industrial-scale chicken breeding, highly specialized chicken lines have replaced the less competitive local breeds . The intAs a consequence of their features (phenotypic differentiation and common genetic background), these two populations provide an interesting model to study the genomic regions underpinning their phenotypic diversity, in particular the plumage color.FST) scan to identify genomic regions that may explain the phenotypic differences observed between PW and PB.Alongside the advance of high-throughput genotyping arrays, molecular markers and genes associated to phenotypic traits or diseases in chickens have been identified through genome-wide approaches ,9,10. InThe collection of blood samples was conducted as part of routine health screening by qualified veterinarians following guidelines established by Institutional Animal Care and Use Committee (IACUC). Blood samples were collected from ulnar veins from 37 unrelated animals belonging to the Polverara White (PW) (n = 17) and Polverara Black (PB) (n = 20) chicken breeds . The aniQuality control procedures were performed for the genotype data using PLINK 1.9 . The folsnpassoc R package [N (N being the number of tested SNPs). We performed a genome-wide association study (GWAS) using the univariate case-control model (PW vs. PB) implemented in the  package , specifiFST case-control analysis was performed using the –fst functionality in PLINK 1.9 [FST differences were defined considering the SNPs falling in and above the 99.98th percentile distribution [The LINK 1.9 , by compribution ,4.qqman [p- and FST values of each SNP were plotted as a function of its position along each autosomal chromosome. The overlapping genomic regions identified by both approaches were further explored to identify linked candidate genes using the Genome Data Viewer (https://www.ncbi.nlm.nih.gov/genome/gdv/browser/genome/?id=GCF_000002315.4) developed by NCBI. To investigate the biological functions and the phenotypes that are known to be regulated by each annotated gene, we conducted a comprehensive literature search, including information from other species. Pair-wise Linkage Disequilibrium (LD) was estimated as the genotype correlation coefficient (r2) [2 measures were obtained using the–r2–ld-window 99999–ld-window-r2 0 command in PLINK v1.9 [A Manhattan plot of the results was generated using the R package qqman . p- and ent (r2) . For allINK v1.9 . LD valuAfter quality control (see above), the final number of SNPs retained for the analysis was 283,893 and no animal was discarded due to poor quality genotyping.p < 0.0001 (−log10 (p) = 9.45) located on eight autosomes , located on six different autosomes   b. In agrFST, we identified a total of 40 significant markers distributed on GGA 01, 03, 08, 12 and 21  around peak SNPs, which corresponded to median rentified .Potentially, there is much unrecognized beneficial genetic variation in local autochthonous animal breeds and populations . As visuFST) have been used in this study [AX-75379450) was located within the TYR gene, while a total of 10 significant SNPs were concentrated in a very small interval of 0.27 Mb within the GRM5 gene. TYR codes for a key enzyme in melanin biosynthesis and it has been accepted as a major gene involved in plumage color in chickens [TYR showed the greatest level of differential expression in the skin of black versus white chickens [GRM5 and TYR genes. On GGA1, there were three other significant markers close to the RAB38 gene whose products is a Ras-related protein. Ras-related proteins are critical regulators of cellular membrane trafficking [RAB38 gene acts in a functionally redundant way in regulating skin melanocyte pigmentation and controls the post-Golgi trafficking of tyrosinase (TYR) and tyrosinase-related protein 1 (TYRP1) [RAS4A, located in the region of a significantly associated SNP [AX-77109855 and AX-77109898) were both located within the NOTCH2 gene. A recent study [NOTCH2 contributes to the regulation of melanocyte homeostasis. Furthermore, NOTCH2 cooperates with c-kit signaling during embryogenesis, and they cooperate to regulate melanocyte homeostasis after birth [TYR gene, it can be hypothesized that variants of the GRM5, RAB38 and NOTCH2 genes could be related to plumage color in chicken. A previous GWAS for plumage color [AKT3, KRT7, PAP2 and DDX6 genes. Yang et al., [SHH and NUAK genes, while Johansson and Nelson [EDN3 gene is associated with dark pigmentation in two local chickens breeds. The authors did not observe any association with the candidate genes here reported. A possible reason for the lack of correspondence among studies may be the different breeds used in the comparison (and their plumage color), the array density and the statistical approaches. In this study, we have reported as candidate loci for chicken plumage color the genomic regions obtained combining the results from two different approaches applied to PW and PB. Despite the phenotypic differences, the two populations share a common genetic background [TYR, are consistent with results reported from previous studies on chicken plumage color. All of the above is likely to have helped us circumvent potential biases linked to false positive signals: the identified genes should therefore be considered rather robust results, which can contribute to explain the genetic contribution to phenotypic differences between PW and PB.Based on the greater power of analysis in limiting the number of false positive signals when more than one methodology is adopted in parallel, two different approaches  . Moreovent study  reporteder birth . Therefoge color , using ad Nelson  reportedckground ,20. MoreFST. Significant markers on GGA01 were close to candidate genes involved in feed conversion ratio (NOX4) [TMEM135) [KLF15, a gene associated with chicken growth and carcass traits [The most obvious phenotypic difference between PW and PB is the plumage color, and a number of genes involved in the determination of this phenotype have been detected in this study. It should also be pointed out that other known genes have been identified by combining GWAS and o (NOX4)  and feedTMEM135)  in chicks traits . It is lIn poultry, plumage color is an important qualitative trait that can serve as marker useful for breed identification. Although the chicken genome is well studied, not all the genes affecting plumage color are described. Based on previous studies in other species, the present work has revealed new potential candidate genes involved in the phenotypic variability of color in local chicken populations. These results contribute insights into the genetic basis for plumage color in poultry, and confirm the great complexity of the mechanisms that control this trait. Additional research will be necessary to refine the presented results and further investigate the molecular mechanism underpinning plumage color.


Despite the various existing studies about nonsynonymous single nucleotide polymorphisms (nsSNPs), genome-wide studies based on nsSNPs are rare. NsSNPs alter amino acid sequences, affect protein structure and function, and have deleterious effects. By predicting the deleterious effect of nsSNPs, we determined the total risk score per individual. Additionally, the machine learning technique was utilized to find an optimal nsSNP subset that best explains the complete nsSNP effect. A total of 16,100 nsSNPs were selected as the best representatives among 89,519 regressed nsSNPs. In the gene ontology analysis encompassing the 16,100 nsSNPs, DNA metabolic process, chemokine- and immune-related, and reproduction were the most enriched terms. We expect that our risk score prediction and nsSNP marker selection will contribute to future development of extant genome-wide association studies and breeding science more broadly. Genetic variants can be classified into several categories, including single nucleotide polymorphisms (SNPs), small insertions and deletions, and structural variants  program  is defined as ‘a field of computer science that evolved from studying pattern recognition and computational learning theory in artificial intelligence and can make predictions on datasets . ML is cML methods consist of computational algorithms to relate all or some of a set of predictors to an outcome. The algorithms attempt to balance two competing interests: bias and variance. In ML contexts, bias is the extent to which the predictions correspond to the true values. Variance represents the sensitivity of the predictions to perturbations in the input data. Even though it is impossible to quantify a model’s bias and variance separately, the two values can be summarized by loss functions. The aim is to reduce both bias and variance simultaneously  pigs, Duroc (DUR) pigs, Jeju Native pigs (JNP), Jeju Native Black pigs (JNB), Korea Native pigs (KNP), Korea wild boars (KWP), Landrace pigs (LDR), Yorkshire (YKS) pigs, and Yucatan miniature pigs (YMP)). The procedure for producing the sequencing data was as follows: FastQC software was used to perform a quality check on the sequencing data . PCA is a technique for reducing the dimensionality of datasets, increasing interpretability but simultaneously minimizing information loss .To predict whether SNPs were nonsynonymous, we used the SnpEff program with the reference genome version Sscrofa 11.1. SnpEff is a variant annotation and effect prediction tool that can be used to identify differences like amino acid changes , nsSNP features should be ordered and selected using some statistical criteria. Here, we set the criteria to be the NsSNPs represent the deleterious effects of translated proteins. These deleterious effects can influence individual survival or risk. Our goal was to select the nsSNPs that significantly affect an individual’s risk among the hundreds of thousands of variants. Thus, we attempted to select nsSNP features through ML (scikit-learn package) . The QTL regions in which the ratio of the selected nsSNPs/total nsSNPs was greater than 200-fold were depicted.Investigating the traits related to each nsSNP and its deleterious effect can be interesting. To survey the characteristics of the selected nsSNPs related to pig traits, the QTL regions encompassing the selected nsSNPs were inspected. The QTL data were retrieved from the pig QTL database  spanned 51 (± 124) to 79 (± 111) (Supplementary Table 1). The number of nsSNPs per chromosome ranged from 2,238 to 11,507. The maximum distance varied from 1,281,567–4,015,421, and the average distance (± SD) spanned 9423  to 35,704  (Supplementary Table 2). The number of SNP markers and nsSNPs per chromosome tends to co-vary across the board. In this study, the correlation between SNP markers and nsSNPs per chromosome was 0.997 . In the VEP program, the nsSNP effects are predicted to be ‘deleterious,’ ‘deleterious with low confidence,’ ‘tolerated,’ or ‘tolerated with low confidence’ based on SIFT score. The number of nsSNPs that were predicted to be ‘deleterious’ was 21,559, and the number that were predicted to be ‘deleterious with low confidence’ was 10,010 (Supplementary Table 3).We filtered nsSNPs from the SNP marker data using the SnpEff program. SnpEff predicts variant effects, which are indicated as annotation impacts. These annotation impacts, such as frameshift, stop-gain, and missense variants, are presented in the ANN field of the vcf file , toll-like receptor , calcium- and integrin-binding 1 , erythropoietin-producing hepatoma receptor A2 , and colony stimulating factor 2 .GO analysis was performed using the selected 16,100 nsSNP markers . The moswww.genecards.org).NMI encodes a protein that interacts with N-MYC and C-MYC. These proteins are two members of the oncogene MYC family. NMI also interacts with all STATs, except STAT2, and augments STAT-mediated transcription in response to the cytokines interleukin-2 (IL-2) and interferon-gamma (IFN-γ). NMI is linked to the JAK-STAT cascade and negative regulation of type I IFN production GO terms. TLR3 encodes a member of the toll-like receptor family, which plays a key role in pathogen recognition and activation of innate immunity. They recognize pathogen-associated molecular patterns and mediate the production of cytokines that are necessary for immunity development. CIB1 encodes a member of the EF-hand domain-containing calcium-binding superfamily, which interacts with many other proteins. These include platelet integrin alpha-IIb-beta-3, DNA-dependent protein kinase, presenilin-2, focal adhesion kinase, protein kinase D, and p21-activated kinase. CIB1 is linked to the type II diabetes mellitus and leukocyte count GO terms. EPHA2 belongs to the ephrin receptor superfamily of the protein tyrosine kinase family, which has been implicated in mediating developmental events, particularly in the nervous system. EPHA2 is linked to the protein kinase activity and protein tyrosine kinase activity GO terms. CSF2 encodes a cytokine that controls the production, differentiation, and function of granulocytes and macrophages and is associated with eosinophil count and inflammatory bowel disease. CSF2 is linked to the cytokine activity and growth factor activity GO terms  was based on the SIFT score of each nsSNP. Thus, the accuracy of our analysis was primarily dependent on SIFT score estimation.Our method was based on the ‘from genome to genome’ concept rather than ‘from phenotype to genome’ like classic genome-wide association studies (GWASs) .In the scikit-learn package, multiple linear regression can be accompanied by nsSNP marker selection. Before performing multiple linear regression, we attempted to preprocess nsSNP sorting by NsSNPs have deleterious effects and are represented by SIFT scores. Given this knowledge, we predicted the total risk score using the SIFT scores of nsSNPs in various pig breeds. Furthermore, nsSNP markers that best explained the total risk score were selected using ML. In addition to the utility of the total risk score, the selected nsSNPs can serve as SNP markers for future GWASs and breeding research.