An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.The online version contains supplementary material available at 10.1007/s00122-023-04431-w. The phenotypic superiority of a hybrid relative to its progenitors was first observed by Darwin , describHybrid breeders progressively structured the parental lines into heterotic groups to avoid evaluating crosses between closely related inbred lines. This process prevents inbreeding depression and therefore maximizes hybrid performance. These groups were developed and improved in parallel based on the performances of the inter-group hybrids, following the principle of reciprocal recurrent selection Hull . Groups Differences in QTL effects between ancestry groups were reported in human populations  detect additive and dominance QTL involved in hybrid performances, (ii) highlight loci in which allelic effects depend on the heterotic group they originate from and (iii) reveal the existence of ancestry effects, beyond the allelic state at individual SNPs. We used two different hybrid panels to test this approach: a Dent-Flint factorial panel and a diallel panel issued from admixed Dent-Flint inbred lines. This method was applied to performances measured in several trials to evaluate the co-location of QTLs through environmental variations and for different traits involved in phenology (anthesis and silking dates), grain moisture and grain yield. We benchmarked our approach with a classical GWAS model not accounting for the allele origin effects. We discussed the interest in admixed hybrid panels breaking the structure between heterotic groups for identifying loci involved in heterotic group complementarity, whose effects are hidden by the genetic structure when using a factorial hybrid panel.In this study, two hybrid panels were developed following the procedure described in Fig. Het1 panel 304 Flint inbred lines and 300 Dent inbred lines were collected from the Flint panel , and the second one was composed of the group of intermediate and late flowering genotypes (flowering time between 212.5 and 228 days after January 1st).Het2 panel 321 admixed lines between the two heterotic group origins were generated from 202 Het1 hybrids (among the 325) using doubled haploidization method  were used as checks and were repeated in all environments (3.8 times on average for Het1 and 2.9 times for Het2).Each panel was phenotyped for four traits: two phenological stages  and two traits associated with yield (grain moisture (HUM) in percentage of fresh weight and grain yield (GY) at 15% grain moisture at harvest in q/ha). Several plots were discarded following experimenter recommendations and after filtering plots based on plant density (performances of plots with a number of plants lower than the median minus 15 plants were discarded). Outlier observations were then filtered out. The percentage of discarded observations for outliers represented 2% (on average across environments and traits) for Het1 and 1% for Het2 of the total observations retained by field experimenters. In a given environment, each hybrid was evaluated 1.1 times on average.h with check status t in the precocity group f, at row r and column c, f, r with c with h with Local environmental effects were corrected within each environment using a model adapted to each panel. The following model was applied on Het1:Dent and Flint lines were genotyped with a 600K SNPs Affymetrix Maize Genotyping array :Specific interaction terms for the additive and dominance effects were added to the model to extend it to a multi-environment context (multiple environments model or MULTI model). We defined environment-specific variances for the error.i at the marker m coded as follows:Kinship matrices were computed following Vitezica et al. Kinship matrices for additivity . In addition, we identified the Dent or Flint origin  of the local genetic background surrounding the alleles. Both information was combined into 4 SNP-Origin (SO) alleles: 1D, 0D, 1F and 0F. For example, 1D corresponds to the allele 1 observed in the Dent local genetic background  strategies were applied to every locus using SO genotypes as marker effects: a single-environment GWAS (Gso-MONO) and a multiple environments GWAS (Gso-MULTI).m:h, with the SO genotypes c for the marker m and the replicate r in the environment e, c. The ten SO genotypes effects are redundant with the general mean For a marker y Fisher  in the cIn this formalism, the genetic value can be decomposed into biological additivity and dominance gene actions Fisher . AdditivIn the presence of genetic structure, the biallelic model of Fisher  may be aFrom these definitions, we defined the mean additivity and dominance effects over the different genetic backgrounds as:Differences between within genetic background additivity or dominance effects reveal particular associations between SNP alleles and allelic origins. Two effects Similar to additivity and dominance, two “origin” effects were defined as an effect of the number of Dent origin alleles, To complete the description of SO genotypes, We used the previous equations to define contrasts for testing the different additivity and dominance effects Table . In addim was fitted by leaving out the chromosome carrying m from the kinship matrices calculation . The contrasts were tested using Wald tests. For the two models, Gso-MONO and Gso-MULTI, markers were discarded if their minor SO genotype was carried by less than 5 individuals. Corrections for multiple tests were applied to control the false discovery rate  was used as a benchmark. For a marker All statistical analyses (variance partition and GWAS) were performed using the MM4LMM R package  between the significant marker and all the other markers within the LD window. The relation between LD values and genetic distances between markers was then inferred using the Hill and Weir regression model (Hill and Weir 2 threshold of 0.1 was used to estimate the LD extent.The linkage disequilibrium (LD) was calculated . We adapted a method proposed by Negro et al.  to buildi.e., different environments, into a single meta-QTL (referred to in the following sections as QTL) if their intervals overlapped.Significant markers with overlapping LD windows were clustered into the same QTL, and their LD windows were used to define the QTL interval. To compare the different models and panels, we merged the QTLs detected with the MULTI and the different MONO models, After correction by the average values of the checks across the different environments (Table S4), Het1 and Het2 panels displayed on average similar hybrid performances for the flowering times  but differed for GY  and Hum .Heritabilities were calculated with single and multiple-environment models. Het1 single-environment heritabilities ranged between 0.91 and 0.99 for FloM and between 0.55 and 0.79 for GY (Table S3). The heritabilities evaluated with the multiple-environment model ranged between 0.98 (FloF and FloM) and 0.88 (GY) (Table S4). Het2 showed equivalent results with single-environment heritabilities between 0.79 and 0.97 for FloM and between 0.67 and 0.82 for GY (Table S3). Multiple-environment heritabilities varied between 0.95 for FloM and 0.79 for GY (Table S4). Overall, heritabilities calculated in the multiple-environment model were superior to the single-environment heritabilities.Large differences between environments were observed for all genetic components of variance in the MONO model (Table S5). The partition of the genetic variance highlighted the preponderance of additivity relative to other effects for all traits and environments. Across environments, the average ratio of additivity variance over the sum of all genetic variances was high in both panels (from 85% (GY) to 91% (FloF) for Het1 and from 73% (GY) to 86% (FloM) for Het2). The average ratio of dominance variance over the sum of genetic variances was inferior to 4% in Het1 for all traits. A higher ratio was observed in Het2 for all traits (up to 17% for GY). The average ratio of epistatic variances across environments was of the same order of magnitude for all traits (from 8% (FloF) to 12% (Hum) in Het1 and from 10% (GY) to 15% (FloF) in Het2).MULTI model confirmed the preponderance of additivity in the main genetic variance  Fig. . The perGenotype-by-environment interaction effects  were observed for all traits . The average AxE variance was always inferior to the additive variance for all traits and panels. Interestingly, the average DxE variance was always superior to the dominance variance. This tendency was stronger in Het2 than in the Het1 panel.The GWAS analysis with the SO model consisted of testing for each marker different effects using contrasts between the corresponding SO genotypes (see “GWAS models” section). As the number of SO genotypes differed from one contrast to another, the number of candidate markers ranged from 324,343 to 471,147 markers for Het1 and from 62,955 to 446,478 markers for Het2 (Table S9-12). Two nominal FDR levels were considered . As expected in GWAS, the distribution of the P values was similar to a uniform law for most of the contrasts . Two exceptions were observed for the two contrasts related to the “origin” effects, A standard “additive and dominance” GWAS model (referred to as the Gad model) was used as a benchmark to evaluate the interest of the “SNP-Origin” GWAS model (Gso model). The QTLs were compared within each panel between Gad and Gso models for additivity  were identified with one of the two models only. In Het2, the proportion of QTLs detected by the two models was much lower than in Het1. A large part of the additive QTLs was only detected with the Gad model (43 to 100%), whereas the dominance QTLs were mostly found by the Gso model (79 to 100%).To illustrate the similarities and differences between the Gad and Gso models, we selected representative markers for FloF in the GWAS conducted for environment SOU17 for the Het2 panel Fig. . We reprMarkers A and B illustrate QTLs detected with both the Gad and Gso models. Marker A showed a strong significant additive Markers C and D illustrate the differences between the Gad and Gso models and the consequences of ignoring within background effects in GWAS. Marker C has a strong P value of 2.47e-7) with a significant difference between the DF background and the other backgrounds of 2.5 days.In addition to new QTLs for additivity and dominance, the Gso model allowed us to investigate the origin effect itself. Marker E was spotted for a significant additive effect Table S16). Only one association for Four contrasts were tested in Het1: additivity and dominance in the “Dent-Flint DF” genetic background, t1 Table , Fig. S4As in Het1, we identified a large number of additive QTLs , QTLs were mostly found in a single environment. Only 3 QTLs were identified in at least two environments, and all of them were also found with the Gso-MULTI model for the flowering traits . One of these QTLs, located on chromosome 8 at position 119 – 124 Mbp, was shared by both flowering traits. No dominance QTL was identified in more than two environments. Gso-MULTI did not reveal any additional QTL compared to those found with the Gso-MONO model.As in Het1, only a few QTLs co-locating between environments were identified in Het2. Additive QTLs were identified in at least 2 environments for FloF (1 QTL), FloM (3 QTLs) and Hum (2 QTLs) (Table S18). Interestingly, among them, several additive QTLs were detected for both flowering time traits (FloF and FloM) on chromosomes 4 and 8. No dominance QTLs were identified as co-locating between environments. The additive effect of the origin showed several co-locating QTLs for FloF (4 QTLs). No co-locating QTLs were identified for Most of the strong co-locating QTLs identified in Het2 with the Gso-MONO model were confirmed with the Gso-MULTI model. Conversely, Gso-MULTI identified new QTLs undetected with Gso-MONO, e.g., 5 additive QTLs and 7 dominance QTLs for FloF.We compared the four contrasts that were common to Het1 and Het2: Variance partition and GWAS results provided some insight into the genetic architecture underlying the traits analyzed in this study. High heritabilities were found for all traits. These high values were due to the large genetic diversity present in the Dent and in the Flint inbred line panels used to derive the two hybrid panels. This large initial diversity explains the large genetic variances found (Tables S3-5) that led to the high trait heritabilities. For both panels, the majority of the genetic variance of all traits was due to additive effects (Tables S5-S8). The additive variance was important for the flowering traits, with values comparable to previous reports . Several other studies pointed out the importance of the contribution of overdominance QTLs to heterosis , whereas the GWAS revealed mostly dominance QTLs for this trait. This discrepancy can be explained by two reasons. First, the effects tested in the variance partition and the GWAS model differed. The variance partition is based on the statistical QTL effects  as shown by Buckler et al. , Romay eDifferences in QTL effects between environments are expected in the presence of GxE interactions. Bernardo (2008) suggested that GxE interaction is one of the principal explanations for the inconsistency between the results of GWAS analysis of complex traits. For all traits and in both panels, we showed that most detected QTLs were specific to one single environment. In particular, almost all the dominant QTLs for grain yield were identified in only one environment. In addition to the presence/absence of detected QTLs between environments, we also observed variation in the amplitude or the sign of the effects . Boer et al.  found siTo evaluate the interest of admixed panels in GWAS, we compared two hybrid panels, one obtained from a classical factorial crossing design between heterotic groups and one obtained from a diallel crossing design between admixed lines. These panels were related to each other since they were both issued from the same founder lines. The relatedness between the two panels and the use of the Gso model based on biological effects should have led to the detection of some common QTLs in the two panels. However, all the QTLs detected with an FDR of 0.05 were specific to one of the two panels. This can be explained by a limited detection power in both panels, leading to the detection in each of a limited set of QTLs. Indeed, by relaxing the FDR level at 0.2, we found more QTLs shared by the two panels (Table S19), including an additive QTL corresponding to well-known flowering time QTLs vgt1 and vgt2 on chromosome 8. As Het1 only displayed Dent-Flint genotypes, i.e., four genotype classes, the number of observations in each genotypic class was higher than in Het2, where the total number of observations was split into ten genotypic classes. Therefore, for the contrasts that can be detected in both panels, the detection power was expected to be higher in Het1 than in Het2. In addition to this power issue, the panels were evaluated in different environments and in different years. We showed previously that different environments could lead to different QTL results , whatever the panel and this effect might be even stronger when comparing two different panels. Lastly, an essential difference relied on the ability to test unique effects with the Gso model in Het2. The admixture within the hybrid genotypes allowed us to test the effects according to the genetic background and of the origin effect itself.Beyond their relevance for GWAS, admixed inbred lines raise issues in terms of hybrid breeding since they deviate from the heterotic patterns used by breeders. Het2 hybrid indeed had on average lower performances than Het1 hybrids for grain yield, which can be explained by the potential inbreeding depression associated with Dent/Dent or Flint/Flint segments. Creating lines from commercial hybrids, which typically leads to admixture, is nevertheless a frequent practice in developing countries to complement these created from open pollinated varieties . Therefore, the number of estimated parameters differed between Gad and Gso models, with 2 parameters for Gad and 4 to 10  in Gso. The exploitation of all the degrees of freedom available in the Gso model allows testing effects  unavailable in the Gad model (see part "Gso effects: an indicator of the heterotic group complementarity and differentiation").We compared the Gso GWAS and a more traditional Gad GWAS, not accounting for the allelic ancestry Table  and 6. WQTLs only with the Gad model can be due to the lower power of the Gso model in case of lower representation of genotypic class in the Gso model compared to the Gad model or QTLs, which are strongly differentiated between the heterotic groups. In this last case, the QTLs cannot be tested with the Gso model due to the filter on the genotypic classes. By testing effects overall the genetic backgrounds, the Gad model is more parsimonious in terms of estimated parameters allowing a gain of detection power compared to effects nested within genetic backgrounds  a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) within group genetic mutations in QTL regions and/or (iii) local epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups.vgt1 and vgt2) shows unbalanced allelic frequencies between heterotic groups, underlying the differentiation of the heterotic groups.Previous articles reported within genetic background QTLs . But testing the origin effect of another locus with balanced frequencies in the vicinity is equivalent to testing the hidden SNP dominance effect of the causal locus. An origin dominance effect, therefore, indicates the proximity of highly differentiated QTLs with dominant effects that are involved in the complementarity between heterotic groups.Major regions for the additive effect of the origin Supplementary file 2 (DOCX 20 KB)Below is the link to the electronic supplementary material.


Chenopodium quinoa Wild.), are the main crops cultivated to satisfy the growing world demand for plant protein for food and feed  Merr.] is the most cultivated legume in the world  in several grain legumes , which is relevant for mechanical harvesting. They present a schematic representation summarising the model of HFP, and how and which other traits, factors, and genes interact and influence HFP. To minimise the loss of pods, the HFP must be higher than that of the blades of most combine harvesters. Here, the genetic control, morphology, and variability of HFP in legumes were revised, and there was an attempt to unravel the diverse terminology for this trait in the reviewed literature. Only a few publications have described a QTL analysis in which candidate genes for HFP with confirmed gene expression have been mapped. The authors found no information about simple and efficient markers associated with HFP, which could be used for marker-assisted selection for this trait.In the mechanical harvest of grain legumes, the architecture of the plants is relevant. Pisum sativum L.) is a protein crop cultivated worldwide, both for human food and animal feed. Some varieties of field pea improved through breeding for yield and disease resistance were studied by Zhao et al. Four yield trials representing the advancement stages of the breeding program for the breeding lines were assessed through grain yield, aerial high-throughput phenotyping (NDVI), and bacterial blight disease scores. Low-to-moderate broad-sense heritability (0.31–0.71) and narrow-sense heritability (0.13–0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The study suggested that NDVI is a more useful trait for predicting grain yield, with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models. Wrinkled vining pea genotypes comprising historical varieties and breeding lines were studied by Alemu et al. to detect molecular markers associated with agronomic traits relevant to pea production. Marker-trait associations (MTAs) were investigated using 6,902 quality single nucleotide polymorphism (SNP) markers generated from the diversity arrays technology sequencing (DArTseq) and genotyping-by-sequencing (GBS) sequencing methods. They reported several novel MTAs for different crucial traits with agronomic importance in wrinkled vining pea production for the first time, and these candidate markers could be easily validated and integrated into the active breeding programs for marker-assisted selection.Pea  breeding lines grown under field conditions to reveal their agronomic performance and nutritional quality value. Only four genotypes of the 282 included in the study combined both good agronomic performance and high nutritional quality. Correlation analysis indicated that the studied traits were significantly intercorrelated, with a negative correlation between protein content and Zn concentration. Positive correlations were observed between grain filling time and the micronutrients Zn, Cu, and Mn and macroelements K and Mg. A low positive correlation was also recorded between protein and Fe concentrations. Positive correlations observed between main agronomic and nutritional quality traits could help in the simultaneous genetic improvement of these traits.Phaseolus vulgaris L.) is the main grain legume cultivated in the world for direct human consumption due to its nutritional properties. The dietary fibre (DF) content in dry bean was studied by Brick et al. with the objective of measuring the content of soluble and insoluble fibre and oligosaccharides in a diverse panel of 275 bean varieties. Insoluble dietary fibre constituted the highest portion of total DF (54.0%), followed by soluble dietary fibre (29.1%) and oligosaccharides (16.8%). Mean total DF and all components did not differ among genotypes grown in two field environments. These results indicate that value could be added to dry bean by cultivar-specific food labelling for protein and components of dietary fibre. Cominelli et al. reviewed the antinutritional factors, nutritional improvement, and future food use of common bean, including new common bean genotypes with reduced antinutritional compounds discovered through the exploitation of the natural genetic variability of common bean and the application of induced mutagenesis. The purpose of this Perspective paper is to first highlight the current advances in mutant bean characterisation and explore further advantages and disadvantages of these bean mutants and their potential use in innovative foods. A view is provided on future research directions to specifically explore further advantages and disadvantages of these bean mutants, their potential use in innovative foods, and their utility as a valuable genetic reservoir of combinations to assess the true functional role of specific seed bioactive components directly in the food matrix.Common bean . Advances in relevant areas, such as the genetic aspects of breeding, grain harvesting, crop diseases, nutritional qualities, and adaptation to environmental conditions, are compiled.AMR: Writing – original draft, Writing – review & editing. FS: Writing – original draft, Writing – review & editing. DB: Writing – original draft, Writing – review & editing. APR: Writing – original draft, Writing – review & editing.


The relevance of the expected value of the proportion of Arabian genes (EV%AG) in different horse breeds participating in the eventing discipline, and the way this factor should be included in equine genetic evaluations for eventing, have not been studied in depth in the current literature. A total of 1089 horses participating in eventing competitions (8862 participation records) were used for this study. The significance of the EV%AG in the different scores obtained was studied, and was found to be significant for the three exercises that make up the discipline of eventing . Five genetic models were computed to study the importance of the EV%AG in the pedigree of 10,375 horses. The best-fitted models following the DIC criterion were those including age as covariate, sex, breed, level, EV%AG and event as systematic effects, rider, animal and residual as random effects and variance heterogeneity, concluding that the best way to model the EV%AG effect seems to be by considering the variance heterogeneity. Dressage and show jumping heritability ranged from 0.10 in approach C to 0.21 in D. The estimated heritability for the cross-country trait oscillated less, between 0.07 and 0.01 in approach D.The Arabian horse is a generally reliable sport horse, and continues to be a remarkable endurance horse, so the relevance of the expected value of the proportion of Arabian genes (EV%AG) in horses participating in eventing could be a relevant factor. A total of 1089 horses participating in eventing (8866 records) were used. A GLM revealed that the EV%AG was significant in dressage, show jumping and cross-country. A BLUP genetic evaluation was computed with five genetic models (without the EV%AG (0) using as a covariate (A), as a fixed effect (B), with variance heterogeneity, and in genetic groups without (C) and with (D)). Dressage heritability ranged from 0.103 to 0.210, show jumping ranged from 0.117 to 0.203 and cross-country ranged from 0.070 to 0.099. The lowest DIC value was used as a criterion of fitness. The best fits (those which included variance heterogeneity) showed fewer than two points of difference in DIC values. The highest average estimated breeding value in dressage, show jumping and cross-country was found for horses with an expected value of the proportion of Arabian genes of 0%, ≥1% to <25%, and 100%, respectively. Therefore, the best way to model the EV%AG effect seems to be by considering the variance heterogeneity. Horses have been used for centuries for warfare, transportation or farming, in all of which physical performance is particularly relevant. In the 20th century, certain horse breeds have increasingly been used and bred for competitive sports disciplines such as show jumping, dressage and eventing .CompositThe Arabian horses are commonly believed to be among the oldest and most influential horse breeds in the world . HistoriSelection processes and mating are influenced by conscious decisions, usually with the sole intention of achieving short-term aims in intensively managed domesticated species . In mostIn spite of this, while many authors have studied the impact of genetics on performance in different equine sport competitions ,22,23,24Eventing competitions consist of three different exercises, with the winning horse having the best average total score over the three.In the dressage exercise, the horse has to make a pre-arranged series of dressage movements on a marked track in which different variables are evaluated by two or three judges with a score ranging from 1 to 10. The scores for the different variables are then averaged and calculated on a scale of 1–150. The show jumping exercise consists of an obstacle contest on a track, with a fixed time limit. The score is calculated from the penalties incurred by each participant for faults in jumping over the obstacles, added to any penalties they may have obtained for exceeding the time allowed: the fewer penalty scores obtained, the better the performance. All the scores are converted to positive scores by assigning the highest score (125 points) to the animals with the best performance . The cross-country exercise is an obstacle race on a country track. The score for this exercise is also based on penalty scores which are then converted by assigning a score out of 200 to the performance, according to the number of penalties.A total of 409 Spanish eventing competitions celebrated between 2004 and 2021 were used for the analysis. These competitions had 8 different levels of difficulty, with each competition involving one or more of these levels of difficulty.For the purposes of the present study, the dataset was reduced by deleting animals with both parents unknown or with any missing records in any exercise. After this step, the final database included 8862 records of 1089 horses  with an average age of 7.48 ± 3.30 years old. The final pedigree dataset contained 10,375 horses , with horses belonging to eight different breed groups . The EV%First, a basic descriptive statistical analysis of the three eventing exercises score was carried out, and the significance of the EV%AG factor (5 levels: 0% (440 horses); >0% to <25% (276 horses); ≥25% to <50% (243 horses); ≥50% to <100% (59 horses) and 100% Arabian horses (71 horses)) was studied, together with its influence on the scores obtained on each exercise , using a multivariate general linear model (GLM). All the other systematic factors were also studied in a multivariate GLM. Next, a Fisher post hoc test was carried out to highlight the differences between the least-squares means score for the three eventing exercises, depending on the EV%AG effect. Phenotypic Pearson correlations were also made among the three eventing exercises studied. Statistical analyses were performed using Statistics for Windows software v.11 [The estimation of the genetic parameters was carried out with a BLUP genetic evaluation based on a multivariate animal model, with the scores obtained for dressage, show jumping and cross-country as traits. Five different genetic models were compared using each trait, and genetic parameters were estimated for all of them. The different approaches  could inA heterogeneous variance model was used to allow the residual variance to be estimated, based on EV%AG. Such differences in variance may occur if the mating of the horses is not planned in a natural way. Here, residual variances can be considered heterogeneous, and can be divided according to the EV%AG into five subclasses for all the horses studied. This involves the classification of arbitrary subclasses, within which the variance is assumed to be constant, and where the change in the residual variance is continuous over time . These dAll of the models were analyzed using a Bayesian approach via Gibbs sampling with the GIBBSF90+ module of the BLUPF90 software . The Giby is the vector of observations, * indicates the parameters associated with residual variance, b and b∗ are vectors of the systematic effects, u and u∗ are vectors of the additive genetic effects, r and r∗ are vectors of a rider random effect, and xi, zi, and wi are incidence vectors for systematic, animal, and rider random effects, respectively. Finally, εi∼N. The genetic effects u and u∗ were assumed to be Gaussian.The equation in matrix notation for the model  to be solved for a hypothetical trait considering all of the possible random effects was:u) for the heterogeneity linear model with genetic groups. In order to analyze the distribution of the animals with best estimated breeding values using genetic model D, only the 20% lower and upper breeding values with a reliability ≥ 50 were used here.The lowest deviance information criterion (DIC) value  was usedThe mean score of the three traits studied ranged from 99.99 to 187.47 for both dressage and cross-country . The shop < 0.05) differences according to the horse’s EV%AG  can be considerably lower when compared with single-trait breeding goals, so the genetic correlations can indicate the extent to which the traits of interest can be improved simultaneously . Our resIn composite breeds with Arabian genes, the selection of mare and stallion for breeding is influenced by the stud preferences . Consequently, this choice is made to improve the global performance of the future foal having a relevant effect on other genetic parameters. In dressage, the average breeding values were higher in horses with no expected value of the proportion of Arabian genes than in the other horses, which could be due to the fact that most of these animals belonged to the Pura Raza Española breed, which is selected to perform best in dressage competitions ,52. ThesIn contrast, the highest estimated breeding values for the cross-country exercise were found in Arabian horses. As reported previously, these animals are usually bred for the endurance discipline , which cAccording to our results, the best way to model the expected value of the proportion of Arabian gene effect to estimate genetic parameters and to determine the animals’ breeding value for the eventing discipline seems to be by taking into account the heterogeneity of variance.


Sus scrofa chromosome 7) that was specifically associated with waist circumference. These findings contribute to our understanding of the genetic mechanisms underlying body conformation traits in crossbred commercial pigs.In this study, a genome-wide association study (GWAS) was conducted on 1518 Duroc × (Landrace × Yorkshire) commercial pigs to investigate the genetic basis of body conformation traits. The traits analyzed included body length, body height, chest circumference, abdominal circumference, and waist circumference. The researchers used two statistical models, a mixed linear model (MLM) and a fixed and random model circulating probability unification (FarmCPU), to identify significant genetic variants associated with these traits. A total of 60 significant single nucleotide polymorphisms (SNPs) were discovered in the crossbred pigs. Furthermore, the researchers identified a novel significant quantitative trait locus (QTL) on chromosome SSC7  on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include  Body conformation traits are critical indicators of swine breeding objectives that directly reflect physical size, body structure, and development ,2,3,4,5.ITGA11, TLE3, and GALC might play a role in the body conformation traits based on single and multi-trait GWASs in two Duroc pig populations [Sus scrofa chromosome 7 (SSC7) explained 9.09% of the genetic variance for body length and 9.57% of the genetic variance for body height in Large White pigs [The pig QTL database Release 49) currently reports 35,384 QTLs associated with 716 traits, including one hundred and fifty-eight, two hundred and twenty, thirty-five, thirty-seven, and two QTLs related to body height (BH), body length (BL), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC), respectively  currentl. Zhou etBased on previous research, most GWAS studies have employed mixed linear models (MLM), a commonly used method for addressing population structure and genetic relatedness when deciphering the genetic architecture of complex traits in livestock . HoweverThis study aimed to detect new genetic variants and identify candidate genes for body conformation traits by utilizing MLM and FarmCPU-based genome-wide association studies in 1518 crossbred commercial pigs. This approach was taken as most previous GWAS analyses of body conformation traits were limited to a single method and only focused on purebred pigs, leading to fewer candidate genes being identified and no causal mutations found. Overall, the findings suggest that genetic factors play a significant role in determining body size and shape traits in pigs, and identifying the associated genes and pathways may contribute to improving pork production and understanding obesity in humans.All animals used in this study were handled following the specifications for the care and use of experimental animals established by the Ministry of Agriculture of China. The ethics committee of South China Agricultural University  approved this study especially. The experimental animals were not anesthetized or euthanized in this study.The experimental animals were derived from a commercial crossbred population of pigs. In brief, 84 Duroc males were mated with 397 Landrace × Yorkshire females, resulting in a large cohort of offspring (757 boars and 764 sows). All pigs were maintained under consistent feeding conditions and raised at four farms operated by Wen’s Foodstuffs Group Co., Ltd. in Guangdong, China. After reaching a fattening weight, 1518 pigs born between 2018 and 2019 were processed for phenotype recording with an average body weight of 115 kg in 13 batches. All the pigs were measured on the following traits: BH (body height) was measured from shoulder to ground; BL (body length) was measured from the midpoint of the ear to the tail [p-value > 10−6 for the Hardy–Weinberg equilibrium test were included. Furthermore, all SNPs located on the sex chromosome and unmapped regions were excluded, following our previous study [The genomic DNA of each pig was extracted from ear tissue via a standard phenol/chloroform method and was diluted to 50 ng/μL . The 151us study ,21,22. AIn this study, we used Pearson’s correlation coefficient to calculate the phenotypic correlation  . The SNPPopulation stratification is a major confounding factor that can affect the reliability of GWAS. To account for this, we performed a principal component analysis (PCA) using the qualified SNPs to investigate the population structure of DLY pigs and added them as fixed effects in our analysis. A Q–Q plot was generated using the R software to assess the population stratification level.In the present study, the MLM was performed by using GEMMA software (version 0.98.5)  for fiveive PCAs ; α is a j-th candidate SNP; j-th candidate SNP; and The GAPIT (version 3.0) R package ,27 was up-value was defined as p < 0.01 in the GWAS results, and M refers to the total number of qualified SNPs of crossbred pigs.To reduce the number of false negative results, the false discovery rate (FDR) was used to determine the threshold ,30. FDR The software Plink v1.90  and HaplSus scrofa 11.1 from the Ensemble genome database, accessed in October 2022 [p-values < 0.05, as determined by Fisher’s exact test and Benjamini–Hochberg correction, were selected for further exploration of genes involved in biological pathways and processes.According to our previous findings, the LD in DLY pigs is typically low, with an R-squared value of 0.2 and a genetic distance of around 200 kb . Howeverber 2022 . To gainber 2022 . Enricherg>0.77) , suggester areas ,36.In our results, the Q–Q plot with genomic inflation factors (λ) showed no systematic inflation of test statistics for both methods of GWAS . A totalINTS10, KIRREL3, and SOX21, were identified by both GWAS methods. The most significant SNP was MARC0052457 (p = 5.75 × 10−8) on SSC11 upstream of SOX21, explaining 1.3% of the phenotypic variance for body length. Moreover, individuals with genotype GG had a 2.51 cm increase in body length compared to individuals with genotype AA  in 150 Laiwu pigs [SOX21 have reduced growth and increased energy expenditure. Moreover, further research and investigations are needed to elucidate the role of SOX21 and its potential impact on height regulation. BMP2 (bone morphogenetic protein 2), another candidate gene for body length, has also been identified as a promising candidate gene for carcass length in pigs [BMP2 plays a role in bone development and is associated with body length [BMP gene by transcriptomic profile analysis of porcine cartilage tissues [BMP2 can induce chondrogenic differentiation, osteogenic differentiation, and endochondral ossification in stem cells [BMP2 also regulates early myogenesis and could inhibit proliferation or induce presumptive muscle cells to undergo apoptosis, thereby inhibiting muscle development [BMP2, confirm that this gene is likely to be the causal gene for body length in pigs.Body length and height are typically not prioritized as target breeding traits for pigs; nevertheless, these morphological features may significantly impact the value of a sow during purchase . We deteotype AA C. Recentiwu pigs . Mice la in pigs . Zhou ety length . Li et a tissues . BMP2 caem cells . BMP2 alelopment . Our resMAP4K3, SOD3, and FAM160B1, as shown in MAP4K3 mutants display phenotypes of low nutrient availability, such as reduced growth rate, small body size, and low lipid reserve [SOD3 was considered a potential candidate gene for BMI in Yorkshire pigs [Using both MLM and FarmCPU-based GWAS methods, we identified three SNPs associated with body height: DIAS0000802 on SSC 3, WU_10.2_8_18963576 on SSC 8, and ALGA0081919 on SSC 14. Nearby the significant SNPs, we identified potential candidate genes  reserve . SOD3 waire pigs .ATL2, SPRED2, and SEC16B. KEGG pathways and GO terms were enriched for candidate genes related to calcium signaling, such as positive regulation of cytosolic calcium ion concentration and calcium-mediated signaling  to the Golgi apparatus, is closely associated with growth and obesity [Chest, waist, and abdominal circumference are highly genetically correlated traits that determine animal body size and serve as indicators of fatness and leanness. For chest circumference, 13 SNPs reached the significant threshold, and these 13 significant SNPs were found to be in close proximity to 11 protein-coding genes. The MLM-based GWAS detected seven significant SNPs, and the FarmCPU-based GWAS detected ten . Interesignaling . Calciummference . SPRED2 rocesses . SPRED2-ype mice . The gen obesity . Many st obesity ,51,52.p-value = 2.42 × 10−5), which explained 0.3% of the phenotypic variance of AC. GO terms were enriched for candidate genes involved in urate metabolic processes and actin filament network formation  on SSC9, which explained 1.63% of the phenotypic variance of WC. These significant SNPs were annotated to 12 coding genes, including three promising candidate genes: CAB39, GRP, and ABCD4. The enriched GO terms included activation of phospholipase C activity and threonine catabolic processes (CAB39) rs6722579 have a higher risk of abdominal obesity  than those who do not carry the SNP [GRP stimulates the release of gastrin and other gastrointestinal hormones, which affect food intake and may lead to anorexia, bulimia, and obesity if the gene is lacking [ABCD4. This region contained six SNPs, four of which were located within ABCD4, and the other two SNPs were located upstream or downstream of ABCD4. The top site of the haplotype block explained the largest variance of phenotypic variation in SNPs, reaching up to 1.8%. Moreover, individuals with genotype CC had a 2.51 cm increase in waist circumference compared to individuals who carry genotype AA  for waist circumference was detected on SSC7 within an 84 kb interval. We expect these findings can help scholars understand the genetic basis of porcine body conformation traits and could be applied in pig breeding programs.This study performed two GWAS methods for five conformation traits in crossbred commercial pigs. As a result, we identified 60 SNPs significantly associated with five body conformation traits. Furthermore,


Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits.The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits  to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect. With the launching of the Human Genome Project and the International Hapmap Project, scientific research on human diseases has been revolutionized by comprehensively understanding the human genome and its variations. Studies show that inheritance and genetics are crucial factors for the overwhelming majority of diseases. Therefore, researchers have been continuously exploring the associations between complex traits like human diseases and genetic variants in recent years . In the etc. Traditional genome-wide association studies (GWAS) focus on only one genetic variant and one trait at a time, which are not applicable to deal with the gene mapping problem of multiple traits (MTs)  . TherefoAlthough the above-mentioned methods can deal with the mapping problem of MTs in a certain degree, these methods focus on independent individuals. Family members have more chances to share same causal mutations, so the genetic heterogeneity is relatively small in family data, and it is a good strategy to test for the associations between genes/variants and MTs based on family design . To dateτ theory to detect the associations between MTs and RVs using family data, and consider the statistical property of the new statistic. The new method has certain adjusting function to correct the effect of population stratification. Extensive simulation studies are carried out to illustrate the performance and robustness of the proposed method. The GAW17 data set is also applied to demonstrate its applicability.In this article, we propose a nonparametric test statistic based on the generalized Kendall’s n nuclear families of trios are considered, and suppose q traits are of interest. Denote Ti = T as the q trait values of the ith family, where DTi = (DTi(1), DTi(2), ⋅⋅⋅, DTiq)T, MTi = (MTi(1), MTi(2), ⋅⋅⋅, MTiq)T and OTi = (OTi(1), OTi(2), ⋅⋅⋅, OTiq)T represent the trait value of father, mother and child of the ith family, respectively. The traits considered here can be binary, ordinal or quantitative. Each subject has been genotyped at m RVs in a genomic region. Denote Gi = T as the genotypic score matrix at the m RVs of the ith family, where DGi = T, MGi = T, and OGi = T represent the genotypic score vector of father, mother and child of the ith family, respectively. DGil, MGil, OGil ∈ {0, 1, 2} are the number of minor alleles, and the frequency of minor allele is less than 5%. The main goal of this article is to test hypotheses as follows: H0: no association between MTs and RVs versus H1: Not H0.Data of F and genotype kernel function K for family data, where F is designed to measure the trait difference between families i and j, and K is used to measure the genotype difference between families i and j . Then based on the idea of generalized Kendall’s τ, we propose a U-statistic kth trait is binary or quantitative, the function fk(⋅) is defined as kth trait ordinal, then k = 1, 2, …, q.Inspired by the idea of i and j, and nDGil represents the total number of observed genotype Gil for all n fathers at variant l, and nDGjl has an analogous explanation. Similarly, kernel function for mother genotypes nMGil, nMGjl, nOGil, nOGjl also have analogous explanations. The kernel functions are beneficial to integrate the weak signals from each RV.We define Secondly, in order to calculate the U-statistic from the sample and construct an overall association test statistic based on family data to test the associations between MTs and RVs, we give the following proposition for the U-statistic.Proposition: Based on the above definitions of kernel functions, the U-statistic can be simplified as Proof: Replace the kernel functions F and K in formula , K and K into formula , it is obvious that Fij =  − Fji, Fii = 0. Therefore, According to the definition of T = , E(U|T) and Var(U|T) are the conditional expectation and variance of the U-statistic given T, respectively. Let pl be the minor allele frequency (MAF) of the lth RV, then under the null hypothesis and the assumption of Hardy-Weinberg Equilibrium, the conditional expectation and variance of the U-statistic given T have the following forms E(U|T) and Var(U|T) are given in the p of i.e., p = rank(Var−1(U|T)). For convenience, the proposed nonparametric method based on family data for multiple-trait association testing is referred to as “Nonp-FAM”.Finally, we give the overall test statistic based on the above U-statistic to test association between MTs ad multiple RVs in family-based data. We design a variety of simulation settings to evaluate the performance of the new proposed method (Nonp-FAM). At the same time, we compare Nonp-FAM with another competing method MAX-FAM  that is Sample size: For the methods based on independent individuals, we set the sample size to be 600. For family data of trios, 200 independent families are considered.RVs and genotypes: Assume that the number of RVs in the genetic region we considered is m (= 20 and 40) for each individual. For methods based on independent individuals, according to Hardy-Weinberg equilibrium, the frequencies of genotypes  at locus l are (1 − pl)2, 2pl(1 − pl) and pl is drawn from U, l = 1, 2, ⋅⋅⋅, m. Then the genotype can be randomly generated according to the probability distribution. Based on the generated genotype , the corresponding genotypic score Gil  can be recorded. For family of trios, genotypes of the parents are randomly generated, and the genotypes of the offspring are generated by the transmission of the parents’ alleles.Proportion of causal variants: Assumed that a total of 12 causal variants out of the 20 RVs, and 20 causal variants out of the 40 RVs are assigned. The specific form is denoted as: L′ =  if m = 20 and L′ = ,   if m = 40, where the element 1 indicates that the corresponding locus is causal variant, and 0 indicates that it is non-causal variant.Traits: Two traits are considered in the simulation studies. First, two-dimension continuous traits k = 1, 2, where Gi = . Set the parameters μ = 0, γk = βk × L, where βk represents the risk factor, take β1 = 0, 0.2, 0.4, 0.6, 0.8, ɛi1, ɛi2)T ∼ N, where Σ = Significance level: The significance level α = 0.05 is considered in the simulation studies.After generating the simulation data, we evaluate the estimated type I error rates and powers for all the above-mentioned four methods by 1000 replications. We compare the performance of these methods in each simulation scenario.α is given by α = 0.05 is . In In order to better demonstrate the advantages of the Nonp-FAM method in detecting the associations between MTs and RVs, we design a variety of simulation settings to compare the powers for the four methods. The simulation results of power comparison for the four methods when the traits are two binary, two ordinal, and mixture of binary and ordinal traits are listed in β1 and β2), the powers of all methods show increasing tendency. This is because higher risk factors correspond to higher heritability. The above experimental results show that the proposed Nonp-FAM method performs effectively when the sample size is 200 independent families. To verify the performance of the proposed method with different sample sizes, the simulations with a sample size of 120 independent families were additionally conducted, and the results are shown in We can draw several conclusions from the i.e., the mixture of binary and ordinal traits. The parameters to generate data are same as those in the previous simulation design. We calculate the powers of Nonp-FAM under the simulated data with population stratification and without population stratification  for 100 families). The comparison results are shown in Family-based association studies are particularly valuable because of the consideration of family history for the disease of interest, and population stratification is still a big concern in population-based association studies. Here, we design an additional experiment to demonstrate that the Nonp-FAM method has the advantages in analyzing data with population stratification. In the new simulation, we consider 100 families of trios with population stratification effect consists of three parts: 30 families whose RV frequencies are randomly drawn from interval , 30 families whose RV frequencies are drawn from , and 40 families whose RV frequencies are drawn from . Meanwhile, we consider two traits which are generated similar to Scheme 3, As we can see from Furthermore, we conducted extended simulation experiments with three ordinal traits, in which the causal RVs only affect the first two traits and there is no genetic association between the RVs and trait 3. The generating process of simulated data is omitted here. We performed 1000 replications to calculate the powers of two family-based methods (the Nonp-FAM method and the MAX-FAM method) at the significance level of 0.05, and the simulation results were listed in https://www.gaworkshop.org/). The GAW17 data set consists of real sequence information on a large number of genes from the 1000 Genomes Project (http://www.1000genomes.org) and simulated phenotype data. There are two kinds of data sets: one is 697 unrelated-individual data genotyped at 24,487 autosomal SNPs in 3,205 genes and the other is family data distributed in 8 extended families with the same sample size. The range of MAFs of common and rare variants is from 0.07% to 25.8%. As an example, we consider three related quantitative risk traits Q1, Q2, and Q4 in the data set as the MTs. The GAW17 reported that traits Q1 and Q2 were influenced by nine and 13 genes, respectively. Here we select the ARNT gene with 2236 variant loci, which is located on Chromosome 1 and has significant main effect on trait Q1  . The causal SNPs C1S6533 and C1S6540 in the ARNT gene  were further selected from the 2,236 variant loci. Forty loci were composed as a genetic region to be tested at one time (RNT gene  are in tτ, we construct the U-statistic using family data and then build the overall the test statistic In the recent decades, a vast majority of genetic variants have been identified to be associated with human complex traits, and RVs play a vital role in the association studies. Also, considering MTs in genetic association studies can boost statistical power. In this article, based on the idea of generalized Kendall’s In fact, the proposed method can be used to analyze family data with other structures, not only the trio structure in this article. The key of the new approach is constructing ideal trait kernel functions and genotype kernel functions between two families. This method can be extended to fit other family structures by adding information of other children in the correspond kernel functions. However, more general methods to construct kernel functions still need to be considered to face the association studies of pedigree data whose structure is more complex.The new method is a non-parametric method, and it is robust to any types of MTs. The dimension of MTs has certain impact on the calculation of the test statistic In our method, we mainly consider the situation that all the RV loci are supposed to be independent. In practice, some loci are dependent and some loci may have interaction with certain environmental factors, and these conditions can be added in the theoretical part of our method. In fact, before using the Nonp-FAM method, all pair-wise linkage disequilibrium coefficients can be calculated first to verify the assumption of independence, like we did in our example. If some loci are not independent, we suggest collapsing the dependent RVs into ‘super variant’ by weighting the genotypes of these loci, so that the rest RVs will be independent of the ‘super variant’, then the proposed method can be used. The distribution of the proposed statistic 10.7717/peerj.16040/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16040/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16040/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.16040/supp-4Supplemental Information 4Click here for additional data file.


The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population.We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort.p=3.4 × 10−9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant  and through the GWAS catalog . Polygenic score (PS) weights for each ancestry are available via the PGS catalog .Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal (The online version of this article (10.1007/s00125-023-05912-9) contains peer-reviewed but unedited supplementary material. SLC16A11 gene identified as the main genetic contributor to type 2 diabetes in the Latino population ..p=7.0 × p=0.1547) . After conditioning for the rs4945979 variant, the statistical significance of our identified variant remained essentially the same .Although the replication results did not show statistical significance, the direction of the effect was consistent with the discovery effect , 3-hydroxybutyrate (p=1.01 × 10−5) and acetoacetate (p=3.3 × 10−5)  and higher WHR (p=0.03)  or IFN-γ+IL-1β   with our Latino TOPMed-derived PS, suggesting that they could provide orthogonal information and improve the overall predictive performance. We developed a PS that incorporated GWAS data from the three ancestries using PRS-CSx [We then developed a PS for type 2 diabetes in Latino people using our TOPMed imputed GWAS meta-analysis data. This PS explained 1.6% of the type 2 diabetes status variance Fig. a, which  PRS-CSx , a methop=3.7 × 10−19)    score; previously, it was only possible to detect these variants by relying on more expensive sequencing technologies. While ascertaining variants at frequencies <0.1% may still require whole-genome sequencing (WGS) or WES, we estimate that the power to identify associated variants may be limited unless we undertake sequencing efforts with sample sizes orders of magnitude larger than our study. For example, for MAF<0.1%, the effective sample size required to reach statistical power to detect associations with an effect of OR>2.0 is above 170,000 individuals , and lies within c islets .HDAC2 expression negatively correlates with insulin gene expression  and positively correlates with IAPP expression, which encodes for a satiety hormone  [HDAC2 expression.Histone deacetylase 2 (HDAC2) is a histone deacetylase involved in gene transcription repression. HDACs play a regulatory role in insulin signalling, beta cell function and pancreatic endocrine cell development. At low glucose levels, HDAC2 is recruited to the insulin promoter to downregulate its expression . In huma × 10−7)  HDAC2 al × 10−7) , 56. We Because there are no comprehensive phenome-wide association data to guide the interpretation of variants enriched in Latino populations, we aggregated phenotypic glycaemic and cardiometabolic data from 26,400 Latino individuals to follow-up the identified variants. We found that rs1016378028 risk allele carriers have higher levels of ketone bodies, which are produced through the breakdown of fatty acids and serve as an alternative energy source to glucose. Uncoupled hepatic production of ketone bodies may be a pathological consequence of relative insulin deficiency in diabetes . While tWe leveraged our GWAS results and existing publicly available data to develop an improved PS for Latino ancestry. PSs developed in a particular ancestry group poorly transfer to other populations, exacerbating disparities between populations. We provide an improved PS for the Latino population, by using a combination of GWAS and LD data from East Asian, European and our Latino GWAS. This PS showed a similar performance to the previously reported in European ancestry  with indLeveraging new resources to reanalyse Latino data, such as imputation with the TOPMed panel, proved to be successful in identifying additional type 2 diabetes-related loci. We acknowledge that the TOPMed panel allows the testing of an increased number of variants and additional evidence will be needed to confirm associations at the standard GWAS significance. Further efforts are needed to increase the power of discovery and to follow-up on novel findings in diverse populations. Until then, translation of identified genetic variation-to-function and application to the clinic in Latino populations will remain highly compromised compared with the resources available for European populations. In this study we gathered a high number of Latino samples with extensive biomarker and clinical characterisation; however, larger sample sizes are still needed to achieve sufficient statistical power to detect low-frequency variants. Efforts must be expanded to build shareable resources with a high representation of different ancestries, enabling ancestry-specific effects to be interpreted within the local ancestry context, which is instrumental to identify causal genes, to improve the biological mechanistic insight and to develop targeted therapies.Overall, this study confirms the superior imputation performance of TOPMed, representing a cost-effective and unique opportunity to analyse low-frequency and rare genetic variants in Latino samples at scale. It also presents the largest type 2 diabetes GWAS meta-analysis performed in individuals of Latino ancestry imputed with the TOPMed reference panel. Despite the sample size being orders of magnitude smaller compared with studies performed in other populations, the novel discoveries presented here suggest that more novel genetic associations and new biology of type 2 diabetes will be revealed as the sample size of discovery samples, reference panels and large-scale biobanks with phenome-wide data increase in studies including non-European populations.ESM 1(PDF 1096 kb)ESM 2(XLSX 109 kb)


While we did not observe EAS–EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for πc differences related to hematological and metabolic traits . For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains  with the strongest πc difference with respect to respiratory traits . In EUR, pairwise comparisons showed multiple πc differences related to the endocrine category , where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median πc = 0.01%) with the strongest difference with respect to psychiatric phenotypes . Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant . These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (The online version contains supplementary material available at 10.1186/s40246-023-00514-3. Genome-wide association studies (GWAS) are improving our understanding of the predisposition to human traits and diseases, providing insights into their underlying biological mechanisms . HoweverIn the present study, we leveraged genome-wide association statistics previously generated  from Bioπc), the variance parameter for non-null SNPs (σ2), and residual effects not captured by the variance of effect sizes (a)  test and conducted post-hoc analyses using Dunn non-parametric test for the pairwise comparisons. The application of non-parametric tests permitted us to avoid issues related to the distribution of the variables investigated and to the presence of possible outliers.We analyzed genome-wide association statistics previously generated from BBJ, and a UKB-FinnGen meta-analysis . These iπc estimate was 0.50% in both EAS and EUR , while no difference was observed with respect to σ2 and a parameters . For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains .For the within-population analyses, we investigated whether there are differences among traits related to different phenotypic categories  and a parameters , but not for σ2 . For πc, nine pairwise comparisons showed differences surviving multiple testing correction , where these traits showed a low proportion of susceptibility SNPs . For parameter a, we observed four pairwise comparisons surviving FDR correction  that showed higher residual effects than dermatological traits , respiratory phenotypes , and neurological outcomes . The dermatological category also showed lower residual effects than metabolic traits .In EUR, significant differences among phenotypic categories were observed with respect to a: KW statistic = 8.79, p = 3.03 × 10–3), but not for πc and σ2  and urogenital phenotypes . To follow up the results of the within-population analyses, we tested EAS-EUR πc differences within the three categories that showed statistically significant enrichments . A similar trend was also observed for metabolic traits . Conversely, the endocrine category had a higher proportion of susceptibility SNPs in EAS (endocrine median πc = 0.50%) compared to EUR .Comparing EAS and EUR, we observed a statistically significant difference with respect to residual effects not captured by the variance of effect sizes . Results of the pairwise comparisons were consistent between the two analyses  with five FDR-significant differences shared between them .In EAS, both GV%1M KW statistic = 21.95, p = 1.24 × 10–3; GV%5M KW statistic = 20.91, p = 1.91 × 10–3), the only FDR-significant pairwise difference was between endocrine and gastrointestinal categories  with respect to specific health domains, we observed ancestry-specific patterns.In EAS, there was strong enrichment for metabolic and hematologic categories when testing differences with respect to the proportion of susceptibility SNPs. Specifically, metabolic and hematologic traits showed a lower degree of polygenicity  than phenotypes related to several other categories . The polygenicity of complex traits is primarily driven by purifying selection where new mutations with large effects tend to be removed from the population while variants with small effects are more likely to become common , 7. AnceWith respect to the lower polygenicity of hematologic traits in EAS compared to other health domains, there is limited information regarding which dynamics may be responsible. However, there is consistent literature regarding the impact of human evolutionary history on shaping the variation of genes related to hematologic phenotypes. For example, adaptation to malaria strongly influenced the genetics of hematologic traits through a systematic positive selection of protective alleles that is likely to be partially different across ancestry groups . DenisovIn EUR, endocrine traits showed a lower polygenicity degree than other complex phenotypes . The endocrine system plays a key role in many aspects  that were essential to the success of the human species . Indeed,In our study, we used genome-wide association statistics generated from the meta-analysis of UKB and FinnGen data to maximize the sample size and obtain information regarding as many phenotypes as those available in BBJ. UKB-FinnGen GWAS meta-analyses have been performed by several studies –33, becaBeyond the link between human evolution and the genetics of complex traits, the ancestry-specific polygenicity patterns have implications for the translation of genetic information into clinical care. Indeed, there are ongoing efforts into defining PGS to stratify disease risk . For cera). This reflects the potential systematic bias in variance estimates due to effects such as population stratification or cryptic relatedness [a . This suggests that ancestry and category differences are primarily driven by variability in the proportion of susceptibility SNPs per trait. Further studies will be needed to confirm this hypothesis across more diverse cohorts and a large number of phenotypes.Most of our findings were related to the proportion of susceptibility SNPs per trait, which is informative of the polygenicity degree of the phenotypes investigated. However, we also identified significant between- and within-population differences in the residual effects not captured by the variance of effect sizes , biomarkers, and prescription category . UKB is https://finngen.gitbook.io/documentation/v/r3/methods/phewas/quality-checks). A total of 357,658 and 135,638 EUR participants were investigated from UKB and FinnGen.To avoid biases due to population stratification , BBJ gen2, sex, age × sex, age2 × sex and the top 20 principal components as covariates. For sex-specific diseases, age, age2, and the top 20 within ancestry principal components were included as covariates, and only controls of the sex to which the disease is specific were used. BOLT-LMM (v.2.3.4) [The genome-wide association statistics were generated following the analytic approach described previously . Brieflyv.2.3.4)  was usedv.2.3.4) . A totalhttps://pheweb.jp/downloads in November 2021.The genome-wide association statistics used in the present study were downloaded from πc, the proportion of susceptibility SNPs per trait; (ii) σ2, the variance of non-null SNPs; and (iii) a, the residual effects not captured by the variance of effect-sizes  [preprocessing function, SNPs were also removed if: (i) their effective sample sizes were less than 0.67 times the 90th percentile of the per-SNP sample size distribution; (ii) they were within the major histocompatibility region (excluded because of its complex LD structure); and (iii) they had extremely large effect sizes (per-SNP effect χ2 > 80). In line with GENESIS developers’ recommendations [genesis function was used to implement the two-component model, which assumes that the distribution of effects for non-null SNPs follows a single normal distribution. The same preprocessing . According to simulation analyses [N < 25,000) and this bias can some bias can persist even in much larger sample sizes . For this reason, we decided to implement only the two-component model to avoid possible biases due to the sample size of the input GWAS.The GENESIS R package  was usedflation) . As recoflation) , the GWAflation) . Using tndations , we applanalyses , models projection function in GENESIS [The expected proportion of genetic variance explained by susceptibility SNPs reaching genome-wide significance considering projected sample sizes equal to 1,000,000 and 5,000,000 by applying the  GENESIS –50 and tWe tested within- and between-population differences of the descriptive statistics related to the polygenic architecture of complex traits using non-parametric tests. This permitted us to avoid issues related to the distribution of the variables investigated and to the presence of possible outliers. The KW test was used to compare differences across multiple groups  in a single analysis. To follow up KW results, we used the Dunn test to perform post-hoc pairwise comparisons. To account for the number of pairwise comparisons, we applied FDR multiple testing correction. KW and Dunn tests were performed using the rstatix R package.Additional file 1: Supplemental Tables.Additional file 2: Fig. S1. GENESIS workflow of GWAS data preprocessing.


Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S1 parent plants, and in the main season we evaluated spaced plants of S0 cross progeny and S2+ (S2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) , 5.0% (CST), 10.5% (EAngle) and −10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability ( It is important to increase the rate of genetic gain in self-pollinating crops to meet future demand for grains, and the most promising way to increase the rate of gain is to shorten selection cycles . This maPisum sativum L.), with production of over 14 million tonnes on more than seven million hectares globally [One of the most important grain legume crops worldwide is field pea  evaluated in the same experiment. In a novel approach to test the value of the PBV generated by MLMM, we predict the genetic gain of traits and achieved parental coancestry in the next cycle with optimal contribution selection (OCS) [The aim of this paper is to improve accuracy of PBV in non-inbred progeny of field pea by exploiting the information available in correlated low to moderate heritability traits. This is one of the first studies to use MLMM in crop breeding for this purpose. We hypothesize that accuracy of PBVs for these correlated low to medium heritability traits will be higher in MLMM than in a univariate linear mixed model (LMM), and compare the accuracy of PBVs in Son (OCS)  based onFor definition of abbreviations, please see 0 progeny was 0.826 ± 0.04, which is slightly higher than 0.805 reported from univariate analysis in Cowling et al. [0 progeny was 0.850 ± 0.04. The PBVs obtained from this model were given appropriate economic weights in a selection index, and the index was used to generate an optimised mating design for Cycle 3 based on OCS  variance components and narrow-sense heritability for each trait . In all  > 0.50) .The analysis of all 10 traits in the MLMM in ASReml-R achieved convergence with a US variance structure for additive genetic effects and residual effects, while omitting any other form of random effect. However, US variance structures were modified in all iterations of the run to maintain positive definite values . Hence, the MLMM  improvedo −0.02) . The staThe optimized MLMM allowed for covariance of additive genetic effects and residual effects between traits. Covariances of additive effects were converted to correlations . CorrelaStrong positive additive genetic correlations were found among traits BM, Br, and DTF (≥0.54) and among the stem strength traits SB, CST, and SD (≥0.59), whereas the strongest negative correlations occurred between EAngle and IL (−0.90) and between IL and SD (−0.58), that is, shorter internodes were associated more upright growth and with broader stems . Lower AThe positive genetic correlations between ABS and SB, and ABS and CST, indicate that stronger stems were associated with higher disease levels, which hinders selection for both strong stems and ABS resistance. SB was positively correlated with DTF which indicates that stronger stems were associated with later flowering.0 progeny significantly increased from 0.799 in the univariate LMM to 0.841 in the optimized MLMM , which was slightly higher than accuracy of PBV in S2+ progeny in univariate LMM (0.835) and demonstrates a significant benefit of MLMM over univariate LMM for S0 progeny. Furthermore, the average accuracy of PBV for S2+ progeny significantly increased to 0.875 in the optimized MLMM , where the base of the stem was fixed and the top was pressed downwards by the flat end of the force gauge, following the methods in Niklas . SB has Another component of theoretical column buckling strength is the second moment of area . Future 0 progeny from 0.799 to 0.841 across all traits in the optimized MLMM over the corresponding univariate LMM (0 progeny in multivariate models was higher than the accuracy of PBVs in selfed (S2+) progeny in univariate models. We conclude that selfing to S2 or higher within breeding cycles is not essential to achieve significant genetic gain for these low heritability traits. We achieved annual cycles of S0 recurrent selection with high accuracy of PBV based on an optimized MLMM of several correlated low to medium heritability traits. Recurrent selection on S0 progeny avoids the cost of selfing and longer breeding cycles when selection occurs on inbred lines. This supports the suggestion of Cobb et al. [In this population, SB, SD, and CST had low narrow-sense heritability , but theiate LMM . To put 0 or clones thereof. Benefits of MLMM were also shown in genomic prediction in inbred wheat lines [The optimised MLMM benefited from correlations among additive effects and residuals across traits which improved the data structure in the model and increased the accuracy of PBV, as originally shown by Thompson and Meyer . Similarat lines .0,1 family selection [Our study is one of the first applications of the single step MLMM with pedigree information in self-pollinating crops, and our results support the use of MLMM for several low to medium heritability traits measured on non-inbred progeny to help achieve rapid genetic gain. In this study, we achieved a predicted genetic gain in the next cycle of −3.8% in ABS, +5.0% in CST, +5.1% in SD, and +7.8% in EAngle, while controlling DTF and IL and achieving low parental coancestry of 0.12 . Rapid aelection ,54, and election .0 recurrent selection [2 or higher selfing generations (S2+). Augmenting S0 recurrent selection with S2+ selfs of parent plants improved connectivity of genetic relationships between and within cycles, and improved the accuracy of PBV [2+ selfs of parent plants in augmented S0 recurrent selection, as this provides inbred lines ready for commercial evaluation after two cycles. This simplifies the proposed two-part strategy (population improvement and product development) for breeding self-pollinating crops [Selection  may be ay of PBV . It is ang crops  by combiOur study was based on measurements made in the field, but the accuracy of PBV in non-inbred progeny could also be increased by including additional traits from high-throughput phenotyping in controlled environments to improve the accuracy of PBV for all traits .Genomic relationship information is expected to improve accuracy of PBV especially when combined with pedigree information ,56,58,59The inclusion of multiple correlated traits increases the complexity and number of calculations required to converge the MLMM. In this study, we ran our models outside the R environment to overcome the CPU limitations that appeared in the 10-trait model with ASReml-R. Attempting to fit a model with more traits will likely result in convergence errors, so it is worth exploring different approaches of multivariate analysis for large datasets. One alternative would be to divide the analysis into two stages, where estimates of the fixed effects of genotypes (BLUES) are obtained in univariate analyses and are later analyzed in a multivariate BLUP analysis e.g., . In animAnimal breeders increased the rate of genetic gain by exploiting BLUP models which improved the accuracy of PBV in highly heterozygous individuals , but BLUThe MLMM that we implemented here is relatively simple to adopt and involves little additional cost to the breeding program. Pedigree information can be recorded and recalled in an appropriate data base at low cost. Genomic data can be added in the form of a genomic relationship matrix if and when available. The challenge remains to implement MLMM in small plot trials grown at many sites per cycle across multiple cycles of selection, with measurements of GY, lodging, and other commercial traits. So far, this has only been achieved with univariate analyses, one trait at a time, across all sites and cycles .Progeny evaluated in the previous cycle (Cycle 2) of this field pea experimental population  were sel1 seed harvested from S0 individuals was used for crossing and selfing of each parent plant, and each crossing and selfing was recorded explicitly in the pedigree at the individual plant level [Optimal contribution selection (OCS) based on this selection index was implemented in software MateSel  Supplem, which int level .0) along with selfs of the parent plants at S2 or higher selfing generations (S2+) were harvested and prepared for the Cycle 3 field trial, which forms the basis of this study.The resulting mating list generated by MateSel comprised 150 crosses and was based on 60 Cycle 2 parental genotypes . Crosses0 genotypes (from crosses), 612 S2+ genotypes (selfs of parents used in crosses) and 320 control plants (replicates of 15 control varieties), resulting in a trial with 1600 single plants  deep, based on a spatially-optimized partially replicated design using the R package DiGGer . The pope plants a. The fiPlants were scored for several traits during the growing season from June to November 2019. Ascochyta blight score (ABS) was measured as the number of nodes on the main stem from the base of the plant with stipules or leaves showing ascochyta blight disease symptoms at first flower, with lower values indicating higher resistance. Other recorded traits included days to first flower (DTF) and number of basal branches (Br). Stem angle above horizontal (EAngle) was assessed before the flowering stage b. After To assess stem strength, the first 15 cm of each main stem was detached at harvest and measured for mean internode length of the first four nodes (IL) and stem diameter (SD) at the center of the third stem internode. Compressed stem thickness (CST) was measured using a digital force gauge  to apply a force of 10 N at the center of the third stem internode, recording CST as SD less displacement (mm) at 10 N a. LastlyFirst, all traits were analyzed in a univariate linear mixed model (LMM) where the baseline model accounts for the randomization and is further extended to account for spatial variability in the field trial. Design blocks and genotypes were treated as random effects, and the residual model assumed independent and identically distributed error effects. To account for spatial variability within the trial, in particular the local stationary trend, a first-order separable autoregressive correlation process (AR1 × AR1) for ranges and rows was included, if significant, in the model, following Gilmour et al. . In the , and The initial LMM was defined as:A complete pedigree file of the population back to founders was based on the pedigree in Cowling et al.  and updaEach successive model was evaluated based on changes in log-likelihood, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Comparisons between models were made through a log-likelihood ratio test. The significance of fixed effects was assessed by a Wald test. Random terms were retained when they significantly improved the log-likelihood of the model. Narrow-sense heritability (The second approach was to fit a multivariate linear mixed model (MLMM) across traits, constructed by specifying a matrix of combined traits as the response . DiffereIn MLMM, the process began by fitting a baseline multi-trait model, using diagonal variance structures (DIAG) for the genotype and residual terms, which assumes that traits are independent (covariance between traits is zero) and have separate variances. This is comparable to fitting parallel univariate models. Second, the MLMM was improved by assuming an unstructured covariance model (US), instead of a DIAG variance structure, for the genotype and residual terms, permitting the estimation of covariances for these terms between traits . FinallyN traits, and For each trait present in the model, the AR1 × AR1 structure and other range or row random effects for each trait were fitted separately and retained in the final model only if significant. This means that it was possible to test and fit these terms in a trait-by-trait case. For example, the AR1 × AR1 structure was modelled only for ABS, DTF had a random row effect and GY had both a random row and range effect. Likewise, the non-additive genetic term was included in the MLMM or omitted depending on its significance for each trait and was included as either a US or DIAG structure, based on the significance of the covariance term . The final multivariate model was defined as:However, The covariances of trait effects estimated in the model were transformed to correlations using the variance value for each trait and their covariance:As in the univariate process, all fitted MLMM were evaluated in terms of log-likelihood, AIC, and BIC, using a log-likelihood ratio test to compare models. The significance of fixed effect terms was assessed by a Wald test. Random terms were retained if they produced a significant improvement in the log-likelihood of the model. The models presented in this study were fitted using the R package ASReml-R version 4  . For theI, i obtained from the model prediction, i and The accuracy of PBV obtained from the final models was calculated as the correlation between the true and predicted breeding values based on the approach of Mrode :(5)rpi=1he model .0 and S2+ were compared across models.The average accuracy of PBV of groups of progeny SPBVs for each trait were obtained from the optimized MLMM. An optimized selection index for each individual was calculated from the sum of weighted PBVs across traits, where the weights for each trait were based on the tactical desired gains approach  and calc0 recurrent selection on non-inbred progeny. Significant genetic gains in several low heritability traits were predicted in the next cycle with low rates of population inbreeding based on a mating design generated with optimal contribution selection. The results show that the information contained in correlated traits can be exploited to increase accuracy of selection in early generations of field pea breeding.The accuracy of predicted breeding values for 10 low to medium heritability traits in non-inbred progeny of field pea increased in an optimized multivariate linear mixed model over a univariate model, making it feasible to undertake annual cycles of S