Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.P value of multiple associations as the significance measurement.By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.The online version contains supplementary material available at 10.1186/s12967-022-03637-8. The past decade has witnessed great success of global-scale genome-wide association studies (GWASs) in discovering a large number of single nucleotide polymorphisms (SNPs) associated with many diseases and traits in diverse ancestries  and EUR populations. We identified many shared trait-associated genes that had otherwise been missed by JST. We also revealed that population-common genes were generally more evolutionarily conserved than population-specific ones.We make two key improvements of CONTO relative to JST. First, it constructs three-component mixture null distribution by taking the nature of composite null hypothesis test into account . Second,Z score and P value of a gene for the analyzed trait in the EAS and EUR populations to be Z1 and Z2, and P1 and P2, respectively. These gene-level summary statistics can be easily to obtain with SNP-level association results publicly released by GWASs . This alternative hypothesis corresponds to three null hypotheses: (i) H00: the gene is not associated with the trait in either population; (ii) H10: the gene is associated with the trait in the first population but not the second; (iii) H01: the gene is associated with the trait in the second population but not the first. Formally, if defining the hypothesis test according to P values, we haveOur primary objective is to examine whether a particular trait-associated gene identified in one population is also significant in another population throughout the entire genome. The trans-ethnic genetic overlap can be defined in terms of distinct types of summary statistics. For example, the alternative hypothesis implies that both |Under this framework we intend to identify shared trait-associated genes in both populations from the viewpoint of composite null hypothesis testing.Pmax = max  as our test statistic for the detection of trans-ethnic genetic overlap. However, in contrast to JST which uses the 0–1 uniform distribution as its null distribution, we directly build the null distribution of Pmax to correct the conservativeness of JST by borrowing the idea given in [Pmaxp01 is the power of rejecting P2 ≤ α under H01 and p10 is the power of rejecting P1 ≤ α under H10, both of which are estimated via the Grenander method [01, λ10, and λ00 are the proportions for the three sub-null null hypotheses, all of which are calculated with well-established methods for estimating FDR [Like JST, we take given in , which wgiven in . Specifir method ; λ01, λ1ting FDR , 58; seeP values are uncorrelated with each other. Although this condition is guaranteed by the sequential negligibility assumption in the mediation analysis [Z scores by the inverse of a correlation matrix. The cross-population correlation coefficient is calculated with Z scores of null genes  [Z scores can be in turn transformed into two-sided P values based on the normal approximation. Theoretically, this decorrelation strategy maximizes the transformed test statistics and the original ones [https://github.com/biostatpzeng/CONTO.It needs to highlight that the proposed method above implicitly assumes that the two analysis , 59, 60,analysis , 28, 42, > 0.05) , 62. Thenal ones ; therefoZ scores from a given multivariate normal (MVN) distribution under various scenarios. Specifically, for a gene in the first population, we generated its Z scores randomly from MVN, Λ) under H00 with a probability λ00, or from MVN, Λ) under H10 with a probability λ10, or from MVN, Λ) under H01 with a probability λ01, or from MVN, Λ) under H11 with a probability λ11. For the same gene in the second population, we drew its Z scores at random from MVN, Λ) under H00 with a probability π00, or from MVN, Λ) under H10 with a probability π10, or from MVN, Λ) under H01 with a probability λ01, or from MVN, Λ) under H11 with a probability λ11. The magnitude of τ10 (or τ01 and τ11) measures the strength of association, with larger value indicating stronger association signal. For simplicity, we set τ10 = τ01 = τ11 = 2, 3 or 4, and the total number of genes to 10000, 15000, or 20000.We here implemented simulation studies to evaluate the performance of CONTO. Because it is conducted with only summary-level data, we thus directly sampled two sets of Z scores, we transformed them into P values based on the normal approximation. In our simulation, we set Λ to be a two-dimensional identify matrix. We considered three various probability settings with λ11 ≠ 0 to evaluate FDR control and power: (i) λ00 = 0.40, λ10 = 0.20, λ01 = 0.20, and λ11 = 0.20, constructing a highly polygenic but less overlapped genetic architecture, in which 40% genes were related to the trait in each population. and approximately 33.3% of associated genes were shared across populations; (ii) λ00 = 0.80, λ10 = 0.05, λ01 = 0.05, and λ11 = 0.10, building a less polygenic and moderately overlapped genetic architecture, in which 15% genes were related to the trait in each population and approximately 50% of associated genes were shared across populations; (iii) λ00 = 0.90, λ10 = 0.01, λ01 = 0.01, and λ11 = 0.08, generating a sparse but highly overlapped genetic architecture, in which 9% genes were related to the trait in each population, but approximately 80% of associated genes were shared by the trait across populations. Note that, to a great extent, these simulation parameters were selected based on our results of real data applications (see below).After obtaining 3 times for each simulation setting and displayed the average across these replicates for these methods.Besides CONTO, for comparison we also carried out three other composite null methods  null genes which were not associated with the trait in either population; (ii) population-specific genes that were related to the trait in the EAS or EUR population; (iii) population-common genes that were shared across the two populations. To understand the characteristics of these genes in distinct groups, we used several conservation scores to examine the extent to which a particular gene varied across populations, which included phyloP score , phastCoτ10 = τ01 = τ11 = 2)  under the genetic architecture which was highly polygenic but less overlapped  shared across the EAS and EUR populations using JST and CONTO (Table P = 8.75 × 10−4)  Table B–C. ConsP = 1.14 × 10−11), respectively; the corresponding average phastCons scores were 0.132, 0.127, and 0.116 (P = 9.36 × 10−14), and the corresponding average dN/dS ratio were 0.091, 0.096, and 0.097 (P = 6.86 × 10−10).In terms of conservation score analysis, we observed that these population-common genes were often more evolutionarily conserved compared to unique associated genes identified in a single population, which were also more evolutionarily conserved than null genes. The increased conservation pattern in population-common genes was reflected by each of the three conservation scores including dN/dS ratio Fig. A, phyloPAs large-scale medical genomics studies have become increasingly diverse, understanding similarity and diversity of associated genes for complex diseases/traits and consequently the transferability of genetic discoveries across various populations is essential . In the We also discovered a great deal of population-specific associated genes, indicating that genetic association discoveries identified in one population were not always generalized to other ancestral groups although there indeed shows substantial trans-ethnic genetic overlap underlying diseases/traits between distinct populations. These population-specific discoveries also imply the urgent requirement of including sufficient individuals from underrepresented populations in trans-ethnic GWASs so that more ethnic groups can benefit from current medical genomics researches , 40, 78.Our work distinguishes itself from previous studies in three aspects. First, unlike prior studies which often focused only on a single trait , 81, 82,P values using gene-set association analysis methods. Certainly, the used gene-set based method would play a critical role in CONTO. Intuitively, the performance of a gene-set association analysis method is determined by how well its modeling assumption matches the true genetic architecture of a group of SNPs [There are two directions that need to further explore for CONTO in the future. First, when implementing CONTO, it requires us to first generate gene-level  of SNPs , 84, whi of SNPs , 85, 86. of SNPs , 87. Alt of SNPs –55.P-value combining method [P-value method [Nevertheless, the use of robust and powerful gene-set based methods in CONTO is of importance. Prior studies have demonstrated that aggregating association evidence available from diverse sources is an effective strategy for boosting power; such as integrating multiple gene expression prediction models in transcriptome-wide association studies using the harmonic mean g method , and come method  or the ae method , 90. TheSecond, to a great degree, CONTO should be viewed as a qualitative trans-ethnic genetic overlap identification method because it can only offer the determination whether a particular gene is associated with the trait in both populations, but is difficult to give accurate evidence regarding the consistence of trans-ethnic genetic effects across populations. Understanding the effect difference could provide more in-depth insight into the diversity and similarity of genetic architecture underlying the same trait across distinct ancestral groups. To examine the genetic influence of a gene on complex traits in GWAS, prior studies attempted to employ the polygenic risk score calculated based on SNP effect sizes and genotypes of individuals available from external reference panels , 54, 55.In summary, CONTO stands for a powerful method for detecting trans-ethnic genetic overlap across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.Additional file 1: Table S1. Complex traits available from the European and East Asian analyzed in the present study. Figure S1. Estimated false discovery rate under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 10000, and the false discovery rate was calculated as the proportion of non-overlapped associated genes among all identified ones. Figure S2. Estimated statistical power under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 10000, and the power was calculated as the proportion of truly overlapped associated genes among all identified ones. Figure S3. Estimated false discovery rate under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 20000, and the false discovery rate was calculated as the proportion of non-overlapped associated genes among all identified ones. Figure S4. Estimated statistical power under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 20000, and the power was calculated as the proportion of truly overlapped associated genes among all identified ones.


PRDM2, PTGDR, PTPRG, KCND2, ZNF260, CPE, GRID2, SCD5, SPIDR, ZNF407, HCN3, TMEM50A, FKBP1A, TLE4, SP1, SLC44A1, and MYOM3) as putative quantitative trait loci for the 22 growth and linear type traits studied. In our study, specific genes  were found common between the traits studied, suggesting an interplay between the genetic backgrounds of these traits. The fact that four of the proposed genes  are located on sheep chromosome 2 confirms the importance of these genomic regions for growth and morphological structure in sheep. The results of our study are therefore of great importance for the development of efficient selection indices and marker-assisted selection programs, as well as for the understanding of the genetic architecture of growth and linear traits in sheep.In the current study, the genetic architecture of growth and linear type traits were investigated in Akkaraman sheep. Estimations of genomic heritability, genetic correlations, and phenotypic correlations were implemented for 17 growth and linear type traits of 473 Akkaraman lambs by the univariate and multivariate analysis of animal mixed models. Correspondingly, moderate heritability estimates, as well as high and positive genetic/phenotypic correlations were found between growth and type traits. On the other hand, 2 genome-wide and 19 chromosome-wide significant single nucleotide polymorphisms were found to be associated with the traits as a result of animal mixed model-based genome-wide association analyses. Accordingly, we propose several genes located on different chromosomes (e.g.,  Ovis aries) are among the most utilized animals since domestication. The hardy and adaptive nature of the sheep allowed the species to be widely distributed along various environmental settings and diverse terrains, including arid areas, which promoted the diversification of the breeds. Additionally, the variety of resources provided by sheep established the species as an essential component of the global agricultural economy. Indigenous sheep breeds, especially, have a wide range of adaptive traits that have evolved for thousands of generations with natural and artificial selection, which constitute the main pillars of food security and sustainable production in many countries. Likewise, Akkaraman  sheep, a widely raised fat-tailed sheep breed of Türkiye, is known for its hardy nature, capable of surviving and reproducing under extreme conditions [Sheep  arrays, a major opportunity for the implementation of the genome-wide association studies (GWAS) to reveal the QTL underlying traits of economic interest in livestock production has been provided . GWA stuHowever, the scarcity of discovered QTLs, the low overlap among identified loci, and the complex nature of the genetic background of growth and linear type traits emphasize the need for a deep understanding of the genetic architecture of those traits as well as the need for more QTL discovery studies in sheep. Therefore, the main objectives of the present study were to estimate the genomic heritabilities, observe correlations for certain growth and linear type traits of Akkaraman sheep, and implement GWASs to understand the genetic mechanisms behind complex traits with potential implications in the genetic improvement of an indigenous sheep breed.The authors followed the suggestions by the ARRIVE guidelines throughout the study . The expVena jugularis into an EDTA-coated vacutainer. Samples were immediately transferred within a cold chain to the Genetics Laboratory of the International Center for Livestock Research and Training, Ankara, Turkiye, where the DNA extraction and genotyping were executed.Birth weight (BW), weaning weight (WW), and 180 days weight (180DW) records were taken from the lambs. From those, pre-weaning average daily gain (preADG), post-weaning average daily gain (postADG), and 180 days average daily gain (180ADG) were derived. Weaning weight here is the adjusted weight of the lambs at 90 days, while 6 months weight is the adjusted weight at 180 days via linear interpolation. Additionally, when the lambs were at an average of 6 months old, scoring for 11 type traits including body condition score (BCS), tail size (TS), rear legs rear view (RLRV), gigot roundness rear view (GRRV), rump width (RW), rear legs width (RLW), rear legs feet angle (RLFA), gigot roundness side view (GRSV), rear legs side view (RLSV), body length (BL), and chest width (CW) were implemented following also the descriptions provided by ,9,10,11.The recorded environmental factors to be accounted for as fixed effects were sex (two levels), birth type (two levels), herd (three levels), and feed type (two levels). The preliminary statistics, data pruning, and model fitting were implemented via the R statistical environment. Continuous data were checked for normality via the Shapiro–Wilk test, where outliers with observations deviating three standard deviations ± mean for each trait were excluded from further analyses. Furthermore, the heteroscedasticity of variances was tested with the Breusch–Pagan test . The des260/280 > 1.8; A260/230 > 1.5 and a sample amount of >20 ng/µL with a MultiSkan SkyHigh UV/VIS microplate spectrophotometer . After checking for DNA integrity on gel electrophoresis, the samples proceeded to genotyping, which was implemented using the Axiom™ Ovine SNP Genotyping Array (50 K) and the GeneTitan™ Multi-Channel Instrument according to the manufacturer’s guide .DNA was extracted from those collected blood samples with the QIAcube HT automated device using a commercial Blood/Tissue DNA purification kit and the relevant manufacturer’s protocol provided by the same company . The purified DNA was exposed to a quality/quantity check step to ensure that all samples passed the minimum genotyping requirements of Ap-value cut-off = 0.05/number of SNPs). Furthermore, samples with an individual call rate below 90%, identity by state (IBS) of >95%, and those with too high heterozygosity  were also removed from the data. This resulted in 40,439 remaining SNPs passing imposed QC criteria for follow-up statistical analyses.Preceding further analyses, a quality control (QC) step was performed to ensure lower Type-I and Type-II error rates ,30,31. CGenomic heritability estimates and genetic correlations were obtained using univariate and bivariate analyses of the linear animal mixed model, which is given below with the variance-covariance structure of the estimations, on ‘sommer’ R package :y=Xβ+Zu+y refers to the vector containing phenotypic observations, u is the vector of random effects for breeding values, where u~MVN  and are represented on Manhattan plots of each trait, with consideration being given to the relevant chromosome. Two significant thresholds were applied to the SNPs to identify genome- and chromosome-wide significance based on p-values. To avoid increased false positives imposed by multiple testing, Bonferroni correction was utilized to assign both genome- and chromosome-wide significance thresholds, where the representative significance level of 0.05 was first divided by the total number of SNPs which passed QC for genome-wide significance. Later, the representative significance level was divided by the average number of SNPs per chromosome to assess for chromosome-wide significance. Thus, the obtained genome-wide significance threshold was 1.236 × 10−6 and the chromosome-wide was 3.214 × 10−5, respectively leading to 5.90 and 4.49 on the −log10  scale of the Manhattan plots.Quantile–Quantile (Q–Q) plots were used to inspect the inflation of the test statistics due to any possible systematic bias or error, considering the p-value distribution under the null hypothesis of ‘no association’ see . FurtherGenomic positions and nearby genes related to associated SNPs were retrieved from the Oar_v4.0 genome assembly on NCBI Genome Data Viewer . Genes tBW), weaning weight (WW), pre-weaning average daily gain (preADG), 180 days live weight (180DW), post-weaning average daily gain (postADG), six months average daily gain (sixADG), body condition score (BCS), tail size (TS), rear legs rear view (RLRV), rear legs side view (RLSV), rear legs width (RLW), rear legs foot angle (RLFA), gigot roundness rear view (GRRV), gigot roundness side view (GRSV), rump width (RW), body length (BL), and chest width (CW) were evaluated for genetic parameter estimations and GWA analysis. Outliers were accordantly removed following linear model fitting. Sex was not significant for any of the measured traits. Birth type was used as an adjustment for the pre-weaning growth traits, while traits recorded after weaning included finishing as an adjustment factor preceding GWASs. Following quality control, the remaining observations for each trait and its relevant descriptive statistics are given in A total of 17 different traits, including birth weight . In total, 20 different SNPs were found to be genome- or chromosome-wide associated with at least one trait at various p-levels. Curiously, three SNPs showed multiple associations among the analyzed traits (see Genome-wide association studies for 17 growth and type traits see  were undaits see .O. aries chromosomes (OAR) 4, 6, 7, 12, 14, 17, and 19. Among these, the SNP named ‘rs414279727’ on OAR7 was common between weaning weight (WW) and 180 days average daily gain (180ADG) as outlined in rs412278842’ (p = 9.972 × 10−7) on OAR6 as genome-wide significant, while the same SNP was also found to be chromosome-wide significant for the gigot roundness rear view (GRRV), p-value of 7.323 × 10−6. The second genome-wide significant SNP, namely rs424107094 on OAR9, was associated with the rear legs rear view (RLRV) trait with a p-value of 3.510 × 10−7. It is worth noting that four different type traits were associated with five SNPs localized on OAR2. More detailed information on the associated SNPs can be found in There was a total of seven distinct SNPs that were found to exceed the chromosome-wide significance threshold for analyzed growth traits on PRDM2, PTGDR, PTPRG, KCND2, ZNF260, CPE, and GRID2 were associated with growth traits, where PTGDR was suggested for both WW and sixADG. Candidates for linear type traits include SCD5, SPIDR, ZNF407, HCN3, TMEM50A, FKBP1A, TLE4, SP1, SLC44A1, and MYOM3 genes located on various chromosomes. Furthermore, SCD5 was associated with both chest width (CW) and gigot roundness rear view (GRRV), and TLE4 was associated with both rump width (RW) and gigot roundness side view (GRSV). Loci LOC105606997, LOC105609002, and LOC105602367 were identified as candidates for linear type traits. Further information on the associated SNPs, detected genes, and their relative positions are outlined in The Oar_v4.0 genome assembly was utilized on NCBI Genome Data Viewer and UCSC Genome Browser to annotate genomic regions of the associated SNPs and to detect the nearby genes. In total, 17 annotated genes and three uncharacterized loci were suggested as candidates underlying the genetic control of measured growth and linear type traits. Among the identified genes, Growth and linear type traits are central to sheep selection programmes due to their high influence on production, efficiency, and profitability. However, indigenous sheep breeds, such as Akkaraman, strongly suffer from the lack of a comprehensive pedigree, which is fundamental for a traditional selection program. The use of genomic information in breeding decisions helps overcome this problem by maintaining genomic relationships, where these decisions tend to also result in genetic gain ,18,19. THeritability estimates of growth traits ranged from 0.29 (preADG) to 0.52 (180DW). In contrast, linear type traits had heritabilities between 0.07 (BL) and 0.52 (RLW). Both trait groups showed similar trends compared to those estimated in Suffolk except for body length, which was calculated as having almost zero heritability in our study ,15. It irs414279727, rs427724890, and rs412278842 were commonly found between the abovementioned traits. Further details regarding associated SNPs, p-values, candidate genes, and their relative distance are given in One of the most salient manifestations of our study is the significant SNPs discovered to be common among the growth and linear type traits, clearly indicating pleiotropy behind the molecular mechanisms of these traits. Given that there are considerably high genetic correlations between WW-sixADG (0.96 ± 0.01), RW-GRSV (0.96 + 0.03), and GRRV-CW (0.96 + 0.03), the common significant SNPs may be explained by genetic interplay. A total of 20 individual SNPs were associated with the analyzed traits at various levels, where three SNPs PTGDR, was common between WW and sixADG. The prostaglandin D2 receptor (PTGDR) on OAR7 is a G-protein coupled membrane receptor that functions as a transmembrane signaling receptor (GO: 0004871) and an eicosanoid receptor (GO: 0004953). Interestingly, the suggested gene was previously found in a selection signature study conducted on thin- and fat-tailed sheep [PTGDR has also been associated with hot carcass weight and body depth in various cattle breeds [rs423706103 on OAR19 encompasses the intronic variant of the protein tyrosine phosphatase receptor type G (PTPRG) gene. PTPRG carries phosphoric ester hydrolase activity (GO: 0042578) and was associated with preADG in our study. Support for this gene as a functional candidate comes from [rs407771300 on OAR4, which is present at an intronic variant of potassium voltage-gated channel subfamily D member 2 (KCND2). As a transmembrane protein, KCND2 is responsible for protein complex assembly (GO: 0065003), ion transport (GO: 0006811), and the establishment of localization (GO: 0051234). Further, KEGG analysis also suggests that it mediates serotonergic synapses. Concordantly, another SNP within KCND2 was formerly associated with withers height in sheep [rs404771550 and rs399086810, which are associated with postADG and RLRV, respectively, as they identified candidate genes that were zinc finger proteins. Specifically, zinc finger protein 260 (ZNF260) was found approximately 100 bp downstream of SNP rs404771550 and an intron variant of ZNF407 was identified at SNP rs399086810. Zinc-finger proteins (ZNFs) have long been known to be involved in the growth and development of various species [ZNF395, and ZNF641, have already been associated with growth traits in sheep [Analyses of associated growth traits led to the identification of seven candidate genes on OAR4, 6, 7, 12, 14, 17, and 19, of which four of the associated SNPs were intron variants of the candidate genes . It is ied sheep . Additior GO: 00071 and anr GO: 00053. Intermes from , which mon GO: 00234. Furt species . Additioin sheep ,53. Therrs412278842 on OAR6 and rs424107094 on OAR9, were discovered as a result of GWASs for CW and RLRV, respectively. The former SNP was also found to be associated with GRRV on a chromosome-wide level. This SNP is located approximately 100 Kb upstream of the stearoyl-CoA desaturase 5 (SCD5) gene which has a critical role in the fatty acid metabolic process (GO: 0006631) and the lipid biosynthetic process (GO: 0008610). KEGG analysis further validated SCD5’s role in fatty acid metabolism and the biosynthesis of unsaturated fatty acids but added the AMPK signaling pathway. A recent study implemented whole-genome tests for various economically important traits in sheep and suggested SCD5 as a strong candidate for body size [SCD5 was previously proposed to partake in the body size development of grass-fed Brangus steers [scaffold protein involved in DNA repair (SPIDR) of OAR9 as an intron variant. SPIDR is a nucleoplasm protein with key functions such as the regulation of DNA recombination (GO: 0000018), DNA repair (GO: 0006281), regulation of macromolecule metabolic processes (GO: 0010604), response to hydroxyurea (GO: 0072710), and response to stress (GO: 0080134). Apart from the discussed genome-wide significant SNPs, one SNP named rs427724890 on OAR2 showed a chromosome-wide association with both the RW and GRSV type traits. The associated SNP was located only 1–2 Kb upstream from the gene which encodes Transducin-like enhancer protein 4 (TLE4), a transcriptional co-repressor. Functional annotation within KEGG pathways showed that it is involved in Wnt and Notch signaling pathways. As a prominent regulator of the canonical Wnt signaling pathway (GO: 0016055) and cellular biosynthetic process (GO: 0031326), TLE4 was recently suggested to regulate muscle satellite cell quiescence and muscle differentiation by repressing PAX7-mediated MYF5 transcriptional activation in mice [The two genome-wide associated SNPs in our study, namely ody size . Given ts steers , there i in mice .TLE4, transmembrane protein 50A (TMEM50A), and LOC105606997, where TLE4 was mentioned above as it was also found to be associated with RW and GRSV traits. Myomesin 3 (MYOM3) and solute carrier family 44 member 1 (SLC44A1) were candidate genes identified for linear type trait RLSV. Various TMEM’s, including TMEM8B on OAR2, have been suggested to have relationships with body weight and body length in sheep [TMEM50A as a candidate for further investigations [rs405451961 is associated with BCS within our current study and is located within the 10th exon of the hyperpolarization-activated cyclic nucleotide gated potassium channel 3 (HCN3) on OAR1. HCN3 is suggested to regulate biological quality (GO: 0065008), measured by animal size, mass, shape, and color, and to be involved in the regulation of membrane potential (GO: 0042391). KEGG analysis showed that HCN3 is also involved in GnRH secretion.It is worth noting that an abundance of SNPs on OAR2 was associated with various linear type traits. In total, five SNPs on OAR2 were associated with four different linear type traits, including RW, TS, GRSV, and RLSV. As indicated by in sheep ,57. Seleigations . On the igations ,57,59, iSP1 gene is involved in endocrine resistance, cortisol synthesis, parathyroid hormone synthesis, and TGF-β signaling. Furthermore, the PRDM2 gene was suggested to play a role in various metabolic processes, such as lysine degradation. Finally, the CPE gene was suggested to influence Type I diabetes mellitus whereas GRID2 and PTGDR are responsible for neuroactive ligand-receptor interaction. All in all, we have found 20 associated SNPs with 20 suggested candidates, where their biological importance in growth and development is well observed. The discovery of common SNPs between the traits further supports the relatively high genetic correlations among them and indicates a shared genetic background. Although sheep genomics has recently had breakthrough improvements, for instance the increase in genome assembly publications and the number of high-density SNP arrays, sheep growth, and type traits have only undergone a few studies. Therefore, our results are expected to significantly contribute to the potential knowledge of the genetic background of growth and conformation in sheep, which are central to the economic revenue in sheep production systems.The functional enrichment of the identified genes through KEGG pathway analysis determined that the PRDM2, PTGDR, PTPRG, KCND2, ZNF260, CPE, GRID2, SCD5, SPIDR, ZNF407, HCN3, TMEM50A, FKBP1A, TLE4, SP1, SLC44A1, and MYOM3 were proposed as the genes bearing putative QTLs for growth and linear type traits. Certain genes in our study were found to be common between the measured traits, which suggests that an interplay between the genetic backgrounds of these traits exists. Additionally, the fact that four of the suggested genes were found to be located on OAR2 further emphasizes the importance of the chromosome for growth and morphological structure in sheep. Considering the wide influence of these traits on the other traits of economic importance, these results carry important implications regarding marker-assisted selection programs as well as understanding the genetic architecture of growth and linear type traits and pointing out loci for targeted genome-editing/gene-knockout studies.In this study, the genetic architecture of 17 growth and linear type traits were investigated in Akkaraman sheep. Correspondingly, moderate heritabilities as well as high and positive genetic/phenotypic correlations were observed. This indicates that 1) there is enough genetic variance to be exploited for faster genetic improvement of the focused traits and 2) observed genetic and phenotypic correlations among the traits can be used to define the potential of the traits as indicators for early and easy detection of the breeding male/females. Moreover, two genome-wide and eighteen chromosome-wide significant SNPs were found to be associated with the traits as a result of performed GWA analyses. Accordingly, To our knowledge, this is among the first studies concerning genetic parameters and the genetic background of linear type traits and growth in an indigenous sheep breed. Future genomic studies such as those targeting parasite resistance, disease susceptibility, and fleece characteristics should be considered. Together, these data could be used to design a comprehensive breeding program for sheep, specifically for Akkaraman sheep. In turn, the efficiency, profitability, and sustainability of sheep production systems will benefit. Nevertheless, considering the low number of discovered QTLs, it is extremely important to implement more discovery studies with an increased number of animals to shed a light on the complex molecular mechanisms behind growth and type traits.


Cattle temperament has been considered by farmers as a key breeding goal due to its relevance for cattlemen’s safety, animal welfare, resilience, and longevity and its association with many economically important traits . The definition of proper statistical models, accurate variance component estimates, and knowledge on the genetic background of the indicator trait evaluated are of great importance for accurately predicting the genetic merit of breeding animals. Therefore, 266,029 American Angus cattle with yearling temperament records (1–6 score) were used to evaluate statistical models and estimate variance components; investigate the association of sex and farm management with temperament; assess the weighted correlation of estimated breeding values for temperament and productive, reproductive efficiency and resilience traits; and perform a weighted single-step genome-wide association analysis using 69,559 animals genotyped for 54,609 single-nucleotide polymorphisms. Sex and extrinsic factors were significantly associated with temperament, including conception type, age of dam, birth season, and additional animal–human interactions. Similar results were observed among models including only the direct additive genetic effect and when adding other maternal effects. Estimated heritability of temperament was equal to 0.39 on the liability scale. Favorable genetic correlations were observed between temperament and other relevant traits, including growth, feed efficiency, meat quality, and reproductive traits. The highest approximated genetic correlations were observed between temperament and growth traits . Altogether, we identified 11 genomic regions, located across nine chromosomes including BTAX, explaining 3.33% of the total additive genetic variance. The candidate genes identified were enriched in pathways related to vision, which could be associated with reception of stimulus and/or cognitive abilities. This study encompasses large and diverse phenotypic, genomic, and pedigree datasets of US Angus cattle. Yearling temperament is a highly heritable and polygenic trait that can be improved through genetic selection. Direct selection for temperament is not expected to result in unfavorable responses on other relevant traits due to the favorable or low genetic correlations observed. In summary, this study contributes to a better understanding of the impact of maternal effects, extrinsic factors, and various genomic regions associated with yearling temperament in North American Angus cattle. Genetic evaluation schemes have been refined over time concomitantly with consumers, producers, and industry requirements. A recent American Angus Association (AAA) producers’ survey revealed docility (or temperament) as one of the top three traits to be prioritized in US Angus cattle breeding programs . BehavioA wide range of heritability estimates  has beeTherefore, our overarching goal was to better understand the variation of temperament at the yearling age across the US using a large dataset provided by the AAA through the Angus Genetics Inc. , from the non-genetic, genetic, and genomic aspects. The understanding of the non-genetic factors impacting temperament can guide the statistical modeling and provide a better understanding of nurture impacting temperament. Thereafter, up-to-date statistical models and variance components will allow more accurate EBV estimates and a more powerful genome-wide association to understand the genetic background and the biological mechanisms associated with cattle temperament. With that, the specific objectives of this study were to 1) identify the non-genetic effects  influencing yearling temperament; 2) define an optimal statistical model (in terms of systematic and random effects) to be used in routine genetic and genomic evaluations of yearling temperament in North American Angus cattle; 3) estimate variance components for yearling temperament; 4) estimate approximated genetic correlations between yearling temperament and 20 productive efficiency and resilience traits, including calving ease, growth traits, carcass measurements, body conformation, susceptibility to high altitude disease, hair shedding score, and other mature cow measurements; and 5) identify genomic regions and candidate genes controlling yearling temperament in Angus cattle based on weighted single-step genome-wide association studies, followed by metabolic pathway  analyses and a comparison with previously reported QTLs.All the data used in this study are from purebred and commercial Angus cattle registered in the AAA, born between 1990 and 2018, and recorded for temperament score at yearling age. Yearling temperament score is a subjective measurement , and it is recorded from 320 to 440 days of age (yearling) when the animal exits a chute, which is consistent within contemporary groups . TemperaRecords from 675,678 Angus cattle with temperament score at yearling age were available. Few animals had scores of 4 , 5 , or 6 . Therefore, these three categories were combined, which resulted in 83.8% of animals with score 1, 12.9% animals with score 2, 2.8% animals with score 3, and 0.5% animals with score 4+ . A quality control procedure was performed to remove animals with missing information for the systematic effects evaluated and to warranty enough variability within their levels . Thereafter, the herds  exclusively represented by one score  were removed from the dataset. This criterion was used to avoid bias generated by the farmer/evaluator and allow variability within the levels. Additionally, data from animals younger than 320 days and older than 440 days at the time of temperament assessment were also removed. After quality control, there were 266,029 animals comprising 147,671 bulls, 3,332 steers, and 115,026 females with yearling temperament scores 1 (71.9%), 2 (22.2%), 3 (5.1%), or 4+ (0.8%). These remaining animals were born between 2001 and 2018. The number of animals per yearling temperament score after the quality control is shown in CG) and 2) definition of direct genetic, maternal genetic, and/or maternal environmental effects. Concomitantly to the second step, the genetic parameters were estimated.Before consideration of the statistical model to be used, several options were tested for environmental (systematic) and genetic  effects. The statistical models used in this study were defined based on two steps: 1) definition of the systematic effects and contemporary group , CG , and conception type  as categorical systematic effects and calf age deviation from 365 days (CALFDEV) as a linear covariate. AOD was classified following the Beef Improvement Federation Guidelines . CG wasCG, four mixed animal models were tested by incorporating four random effects: CG, additive direct genetic, maternal genetic, and/or maternal environmental effects. The four models and their corresponding components are listed in After defining the systematic model and the optimal AOD and ET and CALFDEV as a linear covariate, w is a vector of CG random effects, CG, w, and CG, additive genetic, and residual variances, respectively. The three models incorporating maternal effects are as follows:Bayesian threshold models based on the Gibbs sampler and Markov Chain Monte Carlo (MCMC) algorithm were used to estimate the genetic parameters and, thereafter, compare the models. thrgibbs1f90 software  was used+Zu+Sp+ewhere pboa package  born between 1928 and 2018 were included in the nd Welch  and Gewend Welch  criteria package  of R sof package . The var package . The dirThe models were compared based on the linear regression method , which elsmeans package  and maternal calving ease (CEM)], body weight , residual average daily gain (RADG), dry-matter intake (DMI), yearling height (YH), scrotal circumference (SC), carcass and meat quality measurements , conformation [foot angle (FOOT)], adaptation [high altitude disease susceptibility—pulmonary artery pressure (PAP) and hair shedding score (HS)], and other mature cow measurements . A complete description of the traits is shown in The genetic relationship between yearling temperament and other productive efficiency and resilience traits is of great importance for the design of breeding programs and development of selection indexes. Due to limited access to the raw datasets and computational limitations, the genetic correlation between yearling temperament and 20 other relevant traits  was asse1st. Calculation of the weights , in which greater EBVs represent higher probability of the animal receiving a score of 1 (docile).3rd. Calculation of the weighted variances:4th. Calculation of the weighted covariance:5th. Calculation of weighted correlation:Finally, the standard errors (± SE) of the genetic correlation metric  and HD50K , and Neogen GeneSeek, including GeneSeek Genomic Profile Low-Density , High-Density , GGPUHD (150 K SNPs), and AngusGS. Both Zoetis and Neogen provided, for genomic evaluation purposes, an imputed  SNP set similar to the Illumina BovineSNP50V2 and Illumina BovineSNP50V3 , respectively, and mapped to the bovine genome assembly UMD3.1. However, the genomic coordinates were converted to ARS-UCD1.2 bovine genome assembly  procedures were applied to remove genotyped individuals with a call rate lower than 90% and pedigree errors. SNP genotypes with a call rate lower than 90%, an MAF lower than 0.01, and a deviance of heterozygous genotype from the Hardy Weinberg Equilibrium higher than 0.15 were also removed. Both autosomal chromosomes and pseudo-autosomal regions of the X chromosome were used in the WssGWAS. SNPs located on approximated pseudo-autosomal regions  represe matrix . The HThe optimal or selected statistical model was used to perform the WssGWAS. The vector of direct genetic effects was assumed to follow a normal distribution, software  with a 1software . Slidingsoftware ]. Genomiwww.animalgenome.org; access date: September 27, 2021] and located within the selected genomic windows were identified. Gene annotation information was retrieved from Ensembl using the biomaRt R package  than naturally conceived animals  to 0.39 (D model) on the liability scale. Maternal genetic effects contributed to 4% of the total variation in yearling temperament on the liability scale . Additiowww.angus.org/Nce/Heritabilities).Weighted correlations between the EBVs of yearling temperament and other key traits is shown in Positive and favorable association between yearling temperament and growth traits , feed efficiency , precocity , and carcass traits  were observed. The remaining traits had low genetic correlations, varying from −0.05  to 0.10 .The GEBVs from the D model were back-solved to generate the SNP window effects and the percentage of the total additive genetic variance explained by them. The WssGWAS was performed using 266,029 animals with phenotype, 69,441 animals with genotype for 42,662 SNPs located on autosomal and X chromosomes, and a pedigree containing 578,821 animals. The distribution of MAF is presented in Eleven independent genomic regions  were identified, in which each genomic region explained more than 0.20% of the total additive genetic variance. All genomic regions together explained 3.33% of the total additive genetic variance for temperament. Relevant genomic regions were located on BTA2, BTA4, BTA8, BTA10, BTA11, BTA14, BTA26, BTA29, and X chromosomes . The ManPLEKHM3, CRYGD, CRYGC, CRYGB, RBP4, MYOF, CEP55, FFAR4, PDE6C, ST14, ZBTB44, ADAMTS8, and ADAMTS15.Twenty-five positional candidate genes were identified surrounding the 11 genomic windows, in which 18 genes were annotated and their biotype was classified as protein coding (17 genes) and noncoding RNA genes . In addition, two overlapped QTLs were annotated for longevity on BTA2, where four genes were identified, including three from the crystalline gamma family  and PLEKHM3 (pleckstrin homology domain containing M3).There were 210 QTLs annotated in an overlap with the genomic windows associated with temperament. p < 0.10). All terms were associated with vision processes. Visual perception (GO:0007601) was enriched by three genes located on BTA2, including PDE6C (phosphodiesterase 6C), CRYGD, and CRYGC. The last two genes from the CRYG family were also enriched in the structural constituent of eye lens (GO:0005212) and eye lens protein terms, in addition to a third CRYG family gene: CRYGB  selected based on their maternal ability and easy-to-handle characteristics  in orderStill, at the maternal level, younger dams raised more docile progenies than older dams. Similar trends were observed in Nellore cattle, where the average of temperament score tended to increase  as the dam gets older . Studiesp < 0.05).In general, bulls have more dominant and aggressive behaviors than females . ControvThe birth-seasonal animal temperament pattern hinges on climate, grazing conditions, production system, and, consequently, on the farm location . For insAnimal behavior has been associated with memory ability, in which positive and frequent human–animal interaction could positively impact future behavioral responses . The creThese findings provide a framework for understanding how sex and extrinsic factors influence temperament at the phenotypic level. Furthermore, it can provide insights to improve farm management to guide future epigenetic experimental studies. Environmental conditions regulating gene expression are well known for many traits, including docility . Hence, The ultimate goal of model definition is to facilitate genetic improvement through more accurate estimation of variance components and breeding values . TherefoAmong the converged models, the DMG model  provided a slightly lower residual variance, suggesting the components are better capturing the total variance. Medium-to-high heritability are in close agreement with previous heritability estimates for cattle temperament using pedigree  or genomIn general, Pearson correlation between EBVs (or EPDs) does not properly represent the genetic correlation, especially between lowly accurate values . Therefowww.angus.org/Nce/Heritabilities). For completeness, we have presented the approximated genetic correlations between all pairs of traits. However, only associations between yearling temperament and the other traits are discussed in this article.Pairwise genetic correlation among all other relevant traits had been performed, and similar direction and scale were observed between genetic correlation using the weighted Pearson correlation and multi-trait animal model, considering the same population  is involved in the immune system . In behavior, visual perception is required for the reception of a stimulus, which is subsequently converted to a physiological messenger, and properly recognized as a signal  but is biologically important is methylation , and out of those, PDE6C was also enriched in the visual perception. In animal models , this gene has been reported in retinol progenitor regions to be highly methylated, which could impact photoreceptor mechanisms and, consequently, the signaling cascades (Another term identified in this study which has no significance (hylation . Alteredhylation . Three gcascades . In summcascades .RBP4 (retinol binding protein gene), in which knockout mice for it showed impairment, reduced activity, and increased anxiety-like behavior  serum transporter in the human body  located in a region explaining 0.49% of the total variation . Some lnFFAR4) located on BTA26 (TOX), FFAR4 receptors have also been reported as triggers to inflammatory responses  genes previously reported to be associated with behavioral traits in livestock overlapped with the genes identified in this study (U6) gene was previously reported to be associated with temperament  in alternative animal behavior. However, more studies are required to elucidate the roles of the genes identified in this study, including in vitro gene knockout and gene editing studies.Finally, is study . Interesperament , maternaperament , and sucperament  in cattlperament  and adreperament  in pigs.function , which svice versa.Our study encompasses a large, diverse, and robust dataset from the North American Angus cattle population. Yearling temperament was shown to be heritable (0.38 ± 0.01), suggesting that genetic gains can be effectively obtained through direct genetic and genomic selection. Additionally, direct selection for yearling temperament is not expected to result in unfavorable effects on other economically important traits. Actually, the long-term selection for growth, feed efficiency, precocity, and carcass traits has a favorable effect on the indirect selection for temperament, or Sex and extrinsic factors affect animal behavior, such as age of dam, additional human–animal interaction, and birth region. Fitting maternal effects are not crucial for breeding value estimation of temperament in the US Angus population, suggesting the use of the reduced model due to its computational efficiency. Many of the genomic regions associated with yearling temperament were enriched in QTL regions linked with milking speed, longevity, and other economically important traits. Pseudo-autosomal regions of the X-chromosome seem to play a role in yearling temperament, as well as RNA gene biotypes, for example, long noncoding RNA genes. Finally, many of the genes were enriched in visual pathways, which in addition to being important for stimulus, have also been linked to cognitive abilities. The SNPs and genomic regions identified in this study can be used when designing customized SNP panels, to be further investigated in functional studies aiming to better understand the biological mechanisms influencing cattle temperament, as well as used for a biology-driven genomic prediction. In summary, the insights from our study will be useful for various practical applications and future research, including the breeding industry, farm management, and behavioral genomics studies.


Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic  and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10−8, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method,  cis-sQTLs, of which 61% were not identified using expression QTLs.A powerful method for splicing quantitative trait loci (sQTL) mapping, THISTLE, is presented and applied to a collection of 2,865 brain samples. Integration with GWAS identifies 244 genes associated via  However, most of the trait-associated variants are of uncharacterized function and the mechanisms through which genetic variants exert their effects on traits are largely elusive. Considering that most of the GWAS signals are located in noncoding regions of the genome, one hypothesis is that genetic variants affect traits through genetic regulation of gene expression4. The effects of genetic variation on messenger RNA abundance  have been studied for more than a decade7 and nearly all genes have been found with one or more genetic variants associated with their mRNA abundance8. These advances have propelled the development of methods13 to integrate eQTL data with GWAS data to prioritize the genes responsible for GWAS signals. However, only a moderate proportion of GWAS signals have been attributed to cis-eQTLs18, likely because of various reasons, including limited power, spatiotemporal eQTL effects that occur in specific tissues or cell types at specific developmental stages, focus on genomic regions in cis and mechanisms beyond the genetic control of mRNA abundance.GWAS have led to the discovery of tens of thousands of genetic variants associated with human complex traits (including diseases)20 and hypothesized to be one of the major contributors to genetic risk of disease20, whereas a recent study showed that the contribution of sQTLs to trait heritability is not statistically significant conditional on eQTLs21. These results motivated us to investigate the role of sQTLs in complex traits using a larger dataset. Depending on how alternative splicing variation is quantified, sQTL mapping strategies can be broadly classified into two categories22, that is, transcript-level27 or event-level31, each favoring different types of splicing events  is another fundamental mechanism of gene regulation but is heavily underexplored compared to eQTLs. Currently, there is no consensus in the literature regarding the relationship between eQTLs and sQTLs. For example, there are observations showing that sQTLs are largely independent of eQTLs32, schizophrenia33 and Parkinson’s disease (PD)35 reported recently. Recognizing the differences between transcript- and event-level sQTL mapping strategies  with genotype data to detect sQTLs and integrated the sQTL summary statistics into GWAS for 12 brain-related traits (including diseases) of large sample sizes  to prioritize genes associated with the traits through genetic regulation of splicing. We benchmarked the role of sQTLs in complex trait variation by the eQTLs identified using the same data.In this study, we aimed to investigate the genetic control of RNA splicing by generating the largest collection of sQTLs to date and describing their role in complex trait variation. We focused our study on the brain because of data availability and the considerable links of sQTLs to neurodegenerative diseases such as Alzheimer’s disease (AD)37), DRIMSeq v.1.18 (ref. 39) and multivariate analysis of variance (MANOVA) (implemented in rrcov v.1.5.5). We first performed simulations with mRNA abundances generated from multivariate normal or Poisson distributions (Section 2 of the 10(P) threshold  threshold was also observed (even more prominently) in the real-data analysis. For instance, in the analysis of the Religious Orders Study and Memory and Aging Project (ROSMAP) data (n = 832), 6,358 genes with 795,592 unique sQTL SNPs (no linkage disequilibrium (LD) clumping) were discovered by THISTLE versus 3,077 genes with 390,497 sQTL SNPs by sQTLseekeR using a P threshold of 5 × 10−8  it is the most commonly used genome-wide significance threshold in GWAS and the default threshold used in summary-data-based Mendelian randomization (SMR) to select instrument SNPs11 (see below); (2) it was more stringent than the permutation-based P threshold in all the 10 datasets and the sQTLs with PsQTL < 5 × 10−8 represented a large proportion of the sQTLs with a false discovery rate (FDR) < 0.05  were lowly expressed in the brain with median transcripts per million (TPM) <0.1  were lowly expressed in the brain  used in our study than that in the fetal brain study (n = 233). To test this hypothesis, we used a downsampling strategy to assess the sGene–eGene overlap in several subsets of data with n ranging from 100 to 1,073. The result showed that the power of either sGene or eGene discovery was proportional to n and that the difference in discovery power between sGene and eGene increased with increasing n /12,794 in this study, with 3,405 being the number of genes that are sGenes only).To assess the relationship between sQTLs and eQTLs, we performed an eQTL analysis using the same data as above  and ideng n Fig. , in lineh n Fig. , which ih n Fig. . The res45, functional annotation data of 113 RNA-binding protein (RBP) binding sites46, 7 histone marks47 and 15 chromatin states47  SNPs21; the results consistently showed comparable levels of heritability enrichment between sQTLs and eQTLs .We next tested whether the brain s work60 . Both sQs work60  and the s work60 . We thenmodel’61 . The resmodel’61 . Consided above . Under ted above . We furtt traits  and 19c,ely Fig. , where tcis-sQTLs to prioritize functional genes for the 12 brain-related traits, we applied the SMR approach11 to test if an sGene is associated with a trait through sQTL and the COLOC9 PP4 statistic to assess whether the sGene-trait association is driven by the same set of causal variants. SMR testing is typically followed by the heterogeneity in dependent instruments (HEIDI) test to distinguish whether the gene-trait association is because of shared or distinct causal variants11. However, the HEIDI test requires signed SNP effect estimates, which are unavailable for the THISTLE sQTLs. Instead, we used COLOC PP4, which requires only GWAS and xQTL P values as a replacement , 270 (244 unique genes) of which showed a COLOC PP4 value >0.8 , 246 (226 unique genes) of which reached PP4 >0.8 in COLOC  for which summary statistics from large GWAS  were available  suggested that the sQTL, eQTL and SCZ GWAS signals are all driven by the same causal variant(s)  apart or on a different chromosome. After cross-mapping filtering70, we identified 1,161 unique trans-sQTL SNPs with P < 1.72 × 10−10 for 53 trans-sGenes by THISTLE and 2,716 trans-sQTL SNPs with P < 2.75 × 10−11 for 186 trans-sGenes by LeafCutter and QTLtools at 5% FDR, with an overlap of 16 genes  to visualize or download the sQTL and eQTL summary statistics generated in this study. These datasets may be helpful for future studies to understand the molecular mechanisms underpinning the genetic regulation of splicing in the brain, identify functional genes and variants for other brain-related phenotypes and improve genomic risk prediction. Our study also informs future analyses to quantify the relationship between sGenes and eGenes, between sQTLs and eQTLs or more generally between any two types of molecular QTLs. We showed that the low-to-moderate sGene–eGene overlap as observed in previous studies20 is due to small n because the overlap is a function of n  for only approximately 42% (= 3980/9389) of the overlapping genes . In the present study, we provided multiple lines of evidence that the role of sQTLs in complex trait variation is largely distinct from that of eQTLs. First, approximately 61% of the sQTL and eQTL signals were distinct  and the University of Queensland Human Research Ethics Committee B .m be the number of transcript isoforms for a gene, n individuals for isoform j and x be a vector of genotypes of a variant. If we define j, we have S being the variance-covariance matrix of For ease of understanding, let us take a gene with two transcript isoforms as an example. If a genetic variant is associated with a splicing event, the variant is expected to be associated with the difference in mature mRNA abundance between the two transcript isoforms Fig. . Hence, V being the variance-covariance matrix of V) can be written as:If we define V) can be written as:The covariance between 71 that j and k, respectively, Pisoform-eQTL > 0.01)71. Under the null hypothesis of no sQTL effect  software (https://yanglab.westlake.edu.cn/software/osca/#THISTLE).The covariance between 73. RNA-seq data from the Mount Sinai Brain Bank cohort were from four brain cortex regions: BM10 ; BM22 ; BM36 ; and BM44 . Generation of the genotype and RNA-seq data and imputation of the genotype data have been detailed elsewhere44. In each cohort, RNA-seq FASTQ data were cleaned using FASTQC and then aligned to the GRCh37 genome assembly by STAR v.2.7.8a74. All the transcripts including those of long noncoding RNAs were included in the analysis. Gene-level transcriptional abundances (as measured by read counts) were quantified using RNA-SeQC v.2.3.5 (ref. 75) and isoform-level transcriptional abundances (as measured by TPM) were quantified using RSEM v.1.3.1 (ref. 36) using transcript annotation from GENCODE v.37. Next, RNA-seq data were filtered with a standard quality control process, for example, retaining individuals with more than 10 million total reads and RNA integrity number > 5.5. Genotyped and imputed SNP data were filtered with standard quality control criteria in each cohort using PLINK2 (ref. 76), that is, genotyping rate >0.95, Hardy–Weinberg equilibrium test P > 1 × 10−6, MAF > 0.01 and imputation information score >0.3. We excluded individuals with non-European ancestry as inferred from principal component analysis , Mayo Clinic and ROSMAP. Data generation has been detailed elsewheren = 269,867)49, educational attainment (EA) 50, smoking initiation 51, neuroticism 59, age at menarche (AAM) 52, schizophrenia (SCZ) 53, AD 54, PD 55, insomnia 56, bipolar disorder (BIP) 57, amyotrophic lateral sclerosis (ALS) 60 and major depression (MD) 58. IQ was assessed using various neurocognitive tests, primarily gauging fluid domains of cognitive functioning49. EA was measured as the number of years of schooling that individuals completed50. AAM is a female-specific trait, referring to the age when periods start. Neuroticism was measured with 12 dichotomous items of the Eysenck Personality Questionnaire Revised-Short Form77.We also included in this study summary statistics from the latest GWAS in samples of European ancestry for 12 brain-related phenotypes, namely intelligence (IQ)  and performed quantile normalization of TPM values across all transcripts in each brain cortex sample. VariancePartition78 was employed to decompose the variation in isoform abundance into components attributable to multiple known biological and technical factors such as study, RNA quality (RNA integrity number) and age at death; probabilistic estimation of expression residuals (PEER)79 was used to generate a set of latent covariates  that can capture variation due to hidden factors. The isoform-level transcriptional abundance after adjusting for the factors identified by VariancePartition and the PEER factors was standardized by a rank-based inverse normal transformation (RINT). Note that as in the GTEx study6, the number of PEER factors used for the adjustment was determined based on the sample size (n) of each dataset: 15 for n < 150; 30 for 150 ≤ n < 250; 45 for 250 ≤ n < 350; and 60 for n ≥ 350. Isoform abundance after adjustment for selected biological/technical factors and PEER factors was used for a linear regression analysis to detect isoform-eQTLs in each RNA-seq dataset, with the first five genetic principal components fitted as covariates. The isoform-eQTL summary statistics from the ten datasets were meta-analyzed by MeCS71, which can account for correlations of estimation errors of the isoform-eQTL effects between datasets, followed by an sQTL analysis with THISTLE. We excluded genes with only one isoform and limited the cis-sQTL test to SNPs within 2 Mb of each gene on either side. To identify eQTLs, we applied a similar quality control and covariate adjustment pipeline as above to gene-level expression data, that is, excluding genes with TPM < 0.1 or read count <6 in more than 80% of the samples, trimmed mean of the M-values normalization, preadjusting for covariates identified by VariancePartition and PEER factors, and RINT , we aligned the RNA-seq reads of each sample to GRCh37 by STAR v.2.7.8a74, with the wasp flag to leverage SNP genotype data to remove mapping biases caused by allele-specific reads80. The alignment results from all the samples across datasets were used as input for LeafCutter to identify excised intron clusters, with default parameters. In each dataset, the intron excision ratio  was quantile-normalized within each sample and then standardized across samples. In total, 273,051 excised introns in 47,600 intronic excision clusters were identified with 43,774 clusters (92.0%) uniquely mapped to 14,085 genes (using the R function map_clusters_to_genes provided by LeafCutter based on transcript annotation from GENCODE v.37), 2,060 clusters (4.3%) mapped to more than 1 gene and 1,766 unmapped clusters (3.7%). The proportions of variance in intron excision ratio explained by the known biological/technical factors were much smaller than those for isoform abundance above , eCLIP peaks of 113 RBP46 binding sites from the HepG2 and K562 cell lines from the ENCODE project81, chromatin immunoprecipitation followed by sequencing peaks for 7 histone modifications  and 15 chromatin states  from the brain cortex sample (E073) of the Roadmap Epigenomics Mapping Consortium (REMC)47. More specifically, we annotated 12,578 and 16,086 unique lead cis-sQTL and cis-eQTL SNPs, respectively in different functional annotation categories based on their physical positions and quantified the proportion of sQTL or eQTL SNPs in each category. To ameliorate ascertainment bias, we sampled at random the same number of cis-SNPs  as ‘control SNPs’, with their MAF and distance to TSS matched with the SNPs in query. This sampling procedure was repeated 1,000 times. We computed the fold enrichment in each functional annotation category as the ratio of the proportion of sQTL (or eQTL) SNPs in a functional category over the mean proportion of the control SNPs in the category across 1,000 replicates. The sampling variance of the fold enrichment can be calculated approximately by the Delta method82  for the 12 brain-related traits. Details of the baseline-LD model can be found elsewhere61. We created a binary annotation for sQTLs and eQTLs, respectively. In brief, we assigned an annotation value of 1 for the most significant sQTL (or eQTL) SNP for each gene and a zero value for the remaining SNPs, resulting in an sQTL annotation category with 10,416 SNPs and an eQTL annotation category with 14,118 SNPs with an overlap of 1,455 SNPs. The LD scores of the SNPs were computed using SNP genotype data of the individuals of European ancestry from the 1000 Genomes Project (phase 3)83 with a window size of 1 cM. Heritability enrichment of a category was computed as the proportion of heritability explained by the category divided by the proportion of SNPs in the category. Considering that SNPs in or near genes explain disproportionately more trait variation than intergenic SNPs63, we also computed the fold enrichment of heritability as the per-SNP heritability for the lead cis-sQTL (or cis-eQTL) SNPs divided by a mean of a distribution generated by resampling MAF- and TSS-matched cis-SNPs. Sampling variance of the fold enrichment of heritability can be calculated approximately by the Delta method82 , τ can be interpreted as the per-SNP heritability of the corresponding annotation category. In the presence of overlaps among the annotation categories, the interpretation of τ is complicated. However, it can still be used to quantify the contribution of an annotation category to the overall SNP-based heritability, conditioning on the other categories.The stratified LD score regressionP values were computed using a one-sided sum of chi-squared test  and pseudo-F test , respectively. The eQTL and LeafCutter and QTLtools sQTL P values were computed using a one-sided chi-squared test. We used 2,443 unrelated individuals of European ancestry for real-data analysis. The sample size was determined by the maximum number of unrelated individuals of European ancestry with both SNP genotype and RNA-seq data. We excluded individuals of non-European ancestry, with <10 million total reads or with an RNA integrity number <5.5 and 1 of each pair of individuals with genetic relatedness >0.05. Standard quality control criteria were applied to clean genetic variants to avoid the inclusion of low-quality variants in the association analyses. We did not use any study design that required randomization or blinding. The scripts to reproduce the main results of this paper are available at Zenodo84.The THISTLE and sQTLseekeR Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01154-4.Supplementary informationSupplementary Figs. 1–24, Tables 1–7 and Note.Reporting SummaryPeer Review File


The aim of this study was to compare three methods of genomic prediction: GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies. These maternal traits were included in the breeding goal for Topigs Norsvin at the time of recording . The study also compares three methods of genomic prediction: GBLUP , number of stillborn piglets (STB), piglet mortality within 3 weeks, i.e. number of piglets dead after birth and until 3 weeks (M3W), total litter weight at 3 weeks (LW3W), sow shoulder lesions (SHL) and the sow's body condition score (BCS). Of the 15,703 sows, 10,306 had records for all six traits. The traits were recorded between 2008 and 2019. Each of the different traits had between 10,611–15,690 phenotypic records .Yield Deviations , and the rest were genotyped on medium density chips: Illumina PorcineSNP60 (60 K) and two Illumina GeneSeek custom chips (80 K and 50 K). All genotypes were imputed using Fimpute v2.2  of the linear regression Y = a + bX, where a is the intercept, Y is the yield deviation (YD) and X is the genomic estimated breeding value (GEBV) of the sows in the validation datasets, estimated with only marker information and not phenotypic records. h2 is the heritability of the trait and was estimated on the full dataset.The accuracy of prediction for all methods was estimated as:2.5y is a vector of the average YD of a sow, 1 is a vector of ones corresponding to the size of y, μ is the mean, Z is a design matrix linking individuals to the phenotype, u is the random effect of the individual animal , where A is the pedigree relationship matrix and e is the residual effect , where D is a diagonal matrix where the diagonals are the inverses of the effective number of records. The same model was used for the GBLUP analyses except that the individual animal effect was modelled as . The variance components used were from the above pedigree‐based estimates. The G‐matrix was calculated using the VanRaden method 1 , where the prior probability of Ii being equal to 1 is denoted by π , e is the residual with variance e ~ N(0 Dσe2), where D is a diagonal matrix where the diagonals are the inverses of the effective number of records and σe2 is the residual variance estimated from the variance component estimations  = Gσpol2, xi is the vector of genotypes for SNP i coded as for BayesC. Ii is an indicator of whether SNP i is in the model in an MCMC cycle or not (0/1) and the prior probability of Ii being equal to 1 is π , e is the residual with variance e ~ N where D is a diagonal matrix where the diagonals are the inverses of the effective number of records and σe2 is the residual variance estimated from the variance component estimations (Table The BayesGC model is as follows:ns Table . The MCMG) as in the GBLUP model. The BayesC term assumes SNPs to have normally distributed effects with probability (π) or an effect of 0 with probability (1 − π).The BayesGC model basically fits the previous two models (GBLUP and BayesC) simultaneously, i.e. it fits a polygenic and a BayesC term. The polygenic effect is fitted using the genomic relationship matrix  σu2. Hence,πgc is the π value used for the BayesGC method. Four different values of q were tested for BayesGC, q = 0.1, 0.5 and 0.9 corresponding to the BayesC term with fitted marker effects explaining 10%, 50% or 90% of the total genetic variance , with the rest of the variance 1 − q explained by the polygenic effect through the G‐matrix. The values of σm2 used are shown in Table σpol2 are shown in Table In the following, we describe how the total genetic variance å et al. . For Bay3The heritabilities of the traits ranged from 0.09 (M3W) to 0.34 (SHL) Table . M3W hadFor STB, there were also minor, but no significant differences in accuracy between the methods, with the highest accuracy achieved by BayesGC_10 (Fr 1/5000) and GBLUP at 0.318 and the lowest accuracy for STB was BayesC (Fr 1/100) at 0.272  Figure  showed aThe regression coefficients for the method yielding the highest accuracy were also the regression coefficient closest to 1 for all the traits except L3W3. L3W3 was the only trait with a regression coefficient above 1, indicating the variance of the GEBV being slightly lower compared to the yield deviations. TNB was the trait with a regression coefficient closest to 1 with a regression coefficient of 0.966 while SHL was the trait with a regression coefficient furthest from 1 at 0.436 . A reference population of 7–11,000 animals was sufficient to obtain GEBV prediction accuracies comparable to the EBVs obtained with progeny testing for Japanese Black cattle  and the ratio between variance explained by markers and variance explained by the polygenic effect (σpol2), where with BayesGC_10, 10% of the total genetic variance is fitted with markers (σm2) and 90% with the polygenic effect (σpol2), BayesGC_50 the variance is split 50/50 and with BayesGC_90, 90% of the total genetic variance is fitted with markers and 10% with the polygenic effect. For the Bayesian methods, the priors on the fraction of variance explained by a single SNP (Fr) seem more important than how much genetic variance is explained by either polygenic effect (σpol2) or the marker effects (σm2), i.e. there are more differences within the methods BayesGC_10, BayesGC_50 or BayesGC_90 than between them.Bayesian variable selection methods use priors, which need to be carefully chosen or hyper‐parameters of the priors estimated as part of the prediction method. The latter would extend the number of MCMC cycles substantially, as these hyper‐parameters converge much slower to their equilibrium distribution than GEBVs. In our study we tried a range of different priors, varying both the number of SNPs to be included in the model through Fr, the emphasis of each SNP through the variance explained by markers . Modifications of routine software packages may be needed to accommodate these per trait alternative G‐matrices.Another way to improve BayesGC could be to expand the software towards multi‐trait analyses as many routine breeding evaluations today are based on multi‐trait models. Expanding the BayesGC model towards multi‐trait analyses is relatively straightforward if one assumes that an SNP with a large effect, is affecting all the (related) traits  compared with the amount of total genetic variance explained by marker effects as a whole , but overall, most traits were robust against varying the prior distributions.The authors confirm that there is no known conflict of interest associated with this publication.


Semen quality plays a crucial role in the successful implementation of breeding programs, especially where artificial insemination (AI) is practiced. Bulls with good semen traits have good fertility and can produce a volume of high semen per ejaculation. The aim of this review is to use an information approach to highlight candidate genes and their relation to bull semen production traits. The use of genome-wide association studies (GWAS) has been demonstrated to be successful in identifying genomic regions and individual variations associated with production traits. Studies have reported over 40 genes associated with semen traits using Illumina BeadChip single-nucleotide polymorphism (SNPs). Semen traits are important for cattle gene improvement programs relying on artificial insemination . The colStudies ,5 have fA genome-wide association study (GWAS) is an approach used in genomic research to associate specific genotype variations with a particular phenotypic trait. Genome-wide association studies evaluate genomes from several phenotypes, looking for genetic markers that can be used to predict the presence of a trait. Once such markers are identified, they can be used to understand how genes contribute to the traits. Genome-wide association studies have several factors that contribute to their successful utility. One example is the phenotypic variation; filtering phenotype data is important to avoid outliers during analysis. The population size is very important for obtaining meaningful results for both phenotypic and genotypic variation; one example is a recent study on 1819 Angus bulls with 50,624 records for a single-step genome-wide association study (ssGWAS) on the following traits: VOL, SCONC, number of spermatozoa (NS), initial motility (IMOT), post-thaw motility (PTMOT), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SECs) . Their fHering et al. 2014) [ [14] repGenome-wide association studies have relied on certain types of single-nucleotide polymorphism markers to obtain information in different populations and species. The SNPs selected in GWASs are either Illumina  or Affymetrix . These two contending technologies have been selected and reviewed and bring unique approaches to measure SNP differences. Affymetrix (chip-based) displays short DNA sequences to detect alleles, whereas Illumina (bead-based) displays slightly longer DNA sequences to detect alleles. A study by Suchocki and Szyda (2015)  reportedThe candidate gene approach is useful for quickly determining the associations of a specific genetic variant with a phenotype and the number of relevant genes governing traits . To dateMany putative candidate genes in both dairy and beef cattle have been identified to be associated with bull semen traits. Studies on genes of biological pathways showed an associated with semen traits, which revealed seven significant biological pathways relating to 127 genes with 1.04% of the genetic variation for the volume (VOL), number of sperm (NS), and motility (MOT)  reportedThe identification of candidate genes provides a better understanding of the distribution of genes that affect traits of economic interest . Hering Some of these genes were reported to have a significant role in spermatogenesis, as shown in Semen traits are easily measurable, and record-keeping for each bull is essential in any AI company. Most of the studies conducted on bull semen traits have focused on several important parameters, such as motility, progressive motility, and morphological abnormalities of sperm ,17,62. TOur review has highlighted genes associated with semen parameters; over 40 genes have been documented in the literature and have been reported by several authors to be associated with semen traits. We have furthered the understanding of how the length of the marker highlights the position of a gene to influence a trait in chromosomes. Marker efficiency is vital in candidate association studies in which Illumina is the most widely used marker.


NAM-A1 gene that have been previously associated with early senescence. Together, our results show the value of utilizing the data routinely collected during national variety evaluation process for examining breeding progress and the genetic architecture of important traits. There are now a rich variety of genomic and genotypic resources available to wheat researchers and breeders. However, the generation of high-quality and field-relevant phenotyping data which is required to capture the complexities of gene × environment interactions remains a major bottleneck. Historical datasets from national variety performance trials (NVPT) provide sufficient dimensions, in terms of numbers of years and locations, to examine phenotypic trends and study gene × environment interactions. Using NVPT for winter wheat varieties grown in the United Kingdom between 2002 and 2017, we examined temporal trends for eight traits related to yield, adaptation, and grain quality performance. We show a non-stationary linear trend for yield, grain protein content, Hagberg Falling Number (HFN), and days to ripening. Our data also show high environmental stability for yield, grain protein content, and specific weight in UK winter wheat varieties and high environmental sensitivity for HFN. We also show that UK varieties released within this period cluster into four main population groups. Using the historical NVPT data in a genome-wide association analysis, we uncovered a significant marker-trait association peak on wheat chromosome 6A spanning the  These historical datasets are, however, incomplete by design because of, for example, changes in the number and specific set of varieties trialed and changes in the field sites used from year to year. Previous studies have analyzed NPVT data for wheat and barley in the United Kingdom . Registered varieties are subsequently introduced (or maintained on) the UK Recommended List (RL) after undergoing independent non-statutory NPVT managed by the Agriculture and Horticulture Development Board . The NL serves as variety registry while the RL is used as a reference by farmers for variety selection. https://ahdb.org.uk/knowledge-library/recommended-lists-for-cereals-and-oilseeds-rl-harvest-results-archive; Accessed: 2021 December 13). We focused our study on data for eight traits including yield, adaptation and grain quality traits. Yield and height data were collected from treated and untreated trials. The treated trials included management for diseases (fungicide spray) while the untreated trials did not include disease management. Both trials were managed under standard husbandry practices including the application of plant growth regulator (PGR), herbicide, fertilizer, and pest control management as recommended by AHDB. Details of the AHDB RL trial protocol is accessible at: https://ahdb.org.uk/ahdb-recommended-lists-for-cereals-and-oilseeds-2016-2021 (Accessed: 2021 December 13). Before analyses, we filtered the dataset to remove observations with unknown locations or from locations where trials were abandoned. Varieties that were trialed in a single year were also omitted. The nomenclature of varieties, locations, and counties were standardized in cases where different designation or acronyms were used for the same variety, location or county across different years. After filtering, the distribution of the observations obtained for each of the eight target traits resembles a bell curve suggesting normal distribution .We downloaded result files for the NVPT for winter wheat in the United Kingdom from 2002 to 2010 and 2012 to 2017 from the AHDB website containing harvest result for the RLs for cereals and oilseeds  to the NVPT data using restricted maximum likelihood estimation. The model was implemented using the lme4 package in ijkY is the historical performance of variety i in year j at location k. µ is the overall mean performance of all varieties, iv is the effect of variety i, jy is the effect of year j  and jkS is the effect of location k within year j. ijke is the residual variance arising from factors not accounted for in the model including variety × year interaction. As our main interest was the performance for each variety, the variety effect was fitted as fixed factor while the year and site (nested within year) were fitted as random factors. This is slightly different to the strategy used by post hoc TukeyHSD was used to evaluate and compare significant difference in EVM of varieties belonging to different end-use groups. The lstrend function implemented in the R lsmeans package  regression to examine phenotype stability . The orickage FW . Only vackage FW . The FW ckage FW .R2 > 0.8) within each 20-SNP window using PLINK 1.9  were genotyped using the Axiom-35K array  was determined by kmeans clustering using a range of k. The k with the minimum Bayesian Information Criterion and that minimizes overlap between groups was selected as the optimum k. To increase the accuracy of grouping, 50 iterations of the kmeans clustering algorithm were run and the population group to which a variety was most frequently assigned was selected. Also, the cross-validation function  was used to select the optimum number of principal components to use for DAPC.Population structure analysis was done using discriminant analysis of principal component (DAPC) as implemented in the Adegenet  package . For thiR package  using the GAPIT3  package . K repreMaterials and Methods), we retained 52,152 observations for these eight traits from 133 winter wheat varieties (Supplementary Table S1). These 133 varieties were phenotyped in at least two years across a combined 162 locations, with a subset of 95 locations being used for evaluations in two or more years. We analyzed historical data set of the UK RL NVPT from 2002 to 2017. We focused our analyses on six traits of agronomic and economic importance: yield, plant height, days to ripening, Hagberg falling number HFN; , grain pUsing a LMM that accounted for variation arising from the different years and locations of the trials, we derived EVM for each variety for each trait (Supplementary Table S2). Correlation analysis using the EVM captured expected patterns of relationship between the measured traits . We obseP < 0.0001). Conversely, grain protein content and HFN showed small but significant decrease over time . These include the UK Flour Groups 1–4, hereafter referred to as UFG1–4. The UFG1 and UFG2 varieties have superior grain quality (grain protein content and HFN) and are used for breadmaking. UFG3 varieties are often used for biscuits and cakes, whereas UFG4 varieties usually have high yield potential but inferior grain quality and are mainly used for animal feed. As yield and protein content are important measures for these end-use classifications, we examined how the temporal trends observed for these traits varied for the different end-use groups. Expectedly, UFG4 varieties showed higher yield while the breadmaking varieties (UFG1-2) show higher grain protein content , we selected 4039 high-quality markers dataset (Supplementary Table S4) including 1623, 1841, and 589 markers on the A, B, and D sub-genomes, respectively (Supplementary Table S5). Using these genotypic data, we examined the population structure within the UK wheat collection. DAPC analysis revealed four distinct population groups , we examined the association between the population groups and end-use groups . The “Claire” (Pop2) and “Robigus” (Pop4) population groups contain mostly (98%) UFG3 and UFG4 varieties used for biscuit/cakes and feeds, respectively. While the “Cadenza” (Pop1) population group mostly (71%) contain UFG1 and UFG2 varieties used for breadmaking.NAM-A1 gene  that is associated with variation in senescence in European wheat cultivars (We next examined if the EVM obtained from the NVPT could be used for trait mapping through (GWAS). Only 104 varieties with both phenotype (from the 2002 to 2017 NVPT) and genotype data were used for GWAS. Our GWAS analysis identified a region on the short arm of chromosome 6A with significant marker-trait association (MTA) for days to ripening . The dayultivars . The 11 Using historical data from UK NVPT, we examined phenotypic trends in winter wheat varieties trialed between 2002 and 2017. Our analysis highlights a linear increase for yield (treated and untreated) and days to ripening, and a linear decrease in protein content and HFN. Given that the model used to analyze this data adjusted for variation arising from locations across years, and that agronomic practices are largely consistent in the NVPT, this linear trend can be attributed mostly to genetic improvement of varieties over time. P < 0.0001) higher than in treated trials (59.5 kg/ha/year) across the 15-year period. We observed that the rate of yield increase in untreated trials (152 kg/ha/year) is significantly (Puccinia striiformis) “Warrior” race in 2011 (P < 0.001) increased threefold from 123 kg/ha/year before the emergence of the “Warrior” race to 372 kg/ha/year after the emergence of the “Warrior” race (P = 0.2697) comparable in the treated trial before and after the emergence of the “Warrior” race  nature of the NVPT also allows for examining varietal adaptability across multiple environments. Despite the unbalanced nature of the NVPT dataset, results from our yield stability analyses using this dataset largely agrees with findings by Despite this variation in yield stability, we generally observed year-to-year stability in yield (0.35–1.34) and protein content (0.23–1.77) in most of the varieties irrespective of their end-use group. This is likely attributable to the fact that we mainly examined data from RL trials that are comprised of varieties which had been previously screened for distinctness, uniformity, and stability during National Listing trials. Despite this “pre-screening,” almost all the varieties show high environmental sensitivity for HFN (FW coefficient: −0.28 to 6.03). TaMFT and TaMKK3-A (TaMMK3-A in reducing PHS in UK germplasm (HFN is inversely related to α-amylase activity within the grain. High α-amylase activity caused by incidences of pre-harvest sprouting (PHS) and/or pre-maturity amylase (PMA) reduce the bread-making potential of wheat grains. Both PHS and PMA are known to be highly environmental dependent: PHS is induced by wet raining conditions during harvest maturity while PMA is mostly caused by low or high-temperature shock around grain physiological maturity . The envi.e., released varieties used as direct parents in crosses) used to generate the UK RL varieties released between 2002 and 2017 are from the United Kingdom (Supplementary Table S7). This narrow geographical source of variety parents is consistent with the assertion of Our analysis reveals that three modern wheat varieties made major contributions to the development of winter wheat varieties released in the United Kingdom between 2002 and 2017. These include Cadenza (Pop1), Claire (Pop2) and Robigus (Pop4), which are themselves UK varieties released in 1992, 1999, 2005, respectively. In fact, more than 75% of the variety parents  and feed (UFG4). This is not surprising as Claire and Robigus themselves are biscuit-making (UFG3) varieties. Varieties from the Cadenza population group are mostly used for breadmaking (UFG1 and UFG2). This is consistent with the fact that Cadenza is a breadmaking variety. One probable explanation for this association is that breeders tend to make crosses with varieties from the same end-use groups to ensure that the gene combinations underlying the traits in the target end-use groups are preserved in their progenies . This suggests that the choice of parents is an important determinant of the end-use class of varieties.NAM-A1) that have been previously associated with natural variation in time to senescence—synonymous to the days to ripening trait examined in our study. NAM-A1 from a worldwide wheat collection, and suggested functional roles for these polymorphisms. NAM-A1 result in delayed peduncle and flag leaf senescence. Similarly, NAM-A1 mutants showed significant delay in senescence. Given the large interval covered by the MTA peaks for days to ripening on chromosome 6A , we cannot rule out the possibility that other gene(s) underly these days to ripening effect. Nonetheless, the co-localization of our GWAS peak with a known locus for the target trait highlights the usefulness of this historical dataset for quantitative trait mapping.We identified significant MTA peaks spanning a gene (Due to the type (gene-based SNP) and limited number of markers used, we acknowledge the limitation of this study to more precisely define the population groups represented in UK winter bread wheat collection to a high resolution. Also, beside the MTA for days to ripening, we did not identify robust MTA for the other traits. This could be due to three main reasons. First, the sample size used in our study limits the statistical power of our GWAS analysis. As our study focused on a defined breeding period (2002 – 2017) our sample size was limited to the number of varieties trialed in this period. Using the functions described by https://ahdb.org.uk/knowledge-library/recommended-lists-for-cereals-and-oilseeds-rl-harvest-results-archive (Accessed: 2021 December 13). As data for different traits are combined in these original files, we reorganized the files to separate the data for each trait into separate files. The reorganized files are available at Zenodo: https://doi.org/10.5281/zenodo.4761528 (Accessed: 2021 December 13). All Supplementary figures and tables cited, as well as the pedigree file for Helium visualization, are provided as Supplementary Files available with this article. Supplementary material is available at figshare: https://doi.org/10.25387/g3.14946033 (Accessed: 2021 December 13).The original data files for the trials described in this study can be downloaded from the AHDB website at: