D1 is derived from European type breeds , D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F The onset was the landmark publication on genetic mapping of QTL for growth and fatness by . Up to dIn the beginning of pig QTL experiments, the mapping was carried out using crosses between outbred lines and the statistical test employed was linkage analysis. This approach has proven to be efficient for pinpointing numerous QTLs in pigs , 3. Howe2 resource populations jointly has been proven to be a suitable approach 15] and fth/14th rib and CRCL [cm] is measured from the first cervical vertebrae to the pubis symphysis. The methods of measurement and the calculations employed for D1, D2 and D3 were in conformity with the performance testing directive of the Central Association for German Pig Production  is the daily weight gain in the fattening period, the BFT [mm] is calculated as the average of three measurements: shoulder fat depth, back fat depth and loin fat depth, the MFR [ratio] is the fat area in relation to the meat area at 13oduction , 17. Tab2 individuals were genotyped with Illumina PorcineSNP60 BeadChip . SNP chromosomal positions were based on the current pig genome assembly (Sus Scrofa build 11.1 provided by Swine Genome Sequencing Consortium on NCBI). Genotypes were filtered with respect to the following quality control (QC) criteria: i) removing SNPs with a minor allele frequency less than 5% and ii) excluding individuals and SNPs with call rates lower than 90%. The process of quality control was carried out using Plink  Kb up to [4900–5000] Kb was calculated and finally used to compute correlation of phase between two populations according to the formula [Dk and Dk′, rij across all SNP i and j within interval p for Dk and Dk′, accordingly, is denoted with The extent of linkage disequilibrium (LD) in the D1, D2, D3 and D1D2D3 populations was characterized in detail by . Of furtr2 , which i formula :RDk,Dk′RYR1 status was also incorporated as fixed effect for ADG, BFT and MFR traits. Weight at slaughter was included as a covariate for all traits except for ADG. The residual values of the phenotypes were subsequently used. The Genome-wide Complex Trait Analysis tool (GCTA) [A general linear model was used to pre-adjust the animal phenotypes with the following fixed effects: sex, stable and slaughter month class . The phenotype pre-adjustment analysis was carried out with R . For D2 l (GCTA)  was utily* = xb + u + e, where y* is the phenotype corrected for systematic environmental effects and genetic effect (i.e. the RYR1 status for D2 and D3), x is the genotypes of the marker, b is the additive effect size (fixed effect) of the candidate SNP to be tested for association, u is the random polygenic effect given by the construction of the genomic relationship matrix (GRM) and e is the residual effect.Single marker association tests were performed using the GCTA tool for the individual populations . A mixed linear model analysis including the candidate SNP (MLMi) was set up as follows: y*, in this case, consisted of pooled pre-corrected phenotypes from D1, D2 and D3. From the unique genotype file, constructed based on the merged common SNPs among the three populations, the GRM was assembled and then the marker genotypes tested for association. Given the fact that D1D2D3 contains strong familial relatedness (due to full-sib families) and weak population stratification, observed in a multidimensional scaling analysis by [For the joint design (D1D2D3), the same MLMi was used. The lysis by , the mixlysis by . Neverthp values.A meta-analysis aims to statistically combine the information from multiple independent studies and therefore to increase the power and reduce the false-positive results . From thqqman R package [pgenome-wide ≤ 0.05. Because Bonferroni correction acts in a stringent manner, an additional nominal significant level was used for which the threshold was set up to p ≤ 5x10−5. The R package qvalue [q value for each association test. The FDR q value of the significant SNP with the largest p value provided an assessment of the proportion of false positives among the significant SNPs.Manhattan plots for D1, D2, D3 and D1D2D3 GWAS as well as for the MA results were created via  package . By usine qvalue  facilitaClusters incorporating strong evidence for trait-associated chromosomal regions were defined based on the LD structure and the significant SNPs similar to . A clustThe jvenn tool  was usedThe clusters identified were explored using BioMart tool , the EnsAll genotyped animals passed the quality control procedure, i.e. 1,785 in D1, 304 in D2 and 291 in D3. The final autosomal number of SNPs was 44,457, 40,738 and 37,145 for D1, D2 and D3, respectively. Based on the reference genome assembly (Sscrofa 11.1) the average physical spacing between adjacent markers was 50,872 bp in D1, 55,495 bp in D2 and 60,821 bp for D3. For both the joint design and the meta-analysis, 31,299 SNPs were used with an average physical distance among adjacent markers of 72,161 bp.r2 value for adjacent markers was 0.40, 0.44, 0.45 and 0.38 for D1, D2, D3 and D1D2D3, respectively. The correlation of phase Average genome-wide analysis . The remrG = 0.77 in D1, rG = 0.53 in D2 and rG = 0.83 in D3, while for BFT and CRCL a mild to strong negative correlation were observed .The SNP-based heritabilities calculated were moderate to high, ranging from 0.31 for CRCL in D3 to 0.74 for CRCL in D2 . The hSNp values for all SNP markers of each trait was visualized via a Manhattan plot  2, 7, 8, 13 and 16. For BFT, 299 (with 148 above genome-wide significance level) variants were identified on the following SSC: 1, 2, 4, 5, 7, 10 and 13 and for MFR, 223 (with 137 above genome-wide significance level) SNPs located on SSC 1, 2 and 12. Lastly, for CRCL, 368 (with 193 surpassing genome-wide significance level) SNPs on SSC 1, 7, 13, 16 and 17 were discovered via the GWAS and the MA. The top significant variants in all the five analyses conducted are presented in 10p value and the associated q value. The genomic regions with genome-wide significant and nominal significant SNPs . The summary statistic from the D1, D2 and D3 association analysis was further used for the MA step. The global view of ant SNPs  were assant SNPs .The concordance of the nominally significant variants identified was assessed via Venn diagrams . The metLinkage disequilibrium between markers and quantitative trait loci is fundamental for conducting a successful genome-wide association study. In order to disentangle variants associated with complex traits, the LD pattern in the populations under investigation must be evaluated. This particular analysis was carried out by  for the 2 populations are established from genetically divergent breeds of Asian (Meishan) and European origin  as well as the European Wild Boar ancestor. Introgression of Asian pigs into the European stocks has been well documented during the 18th and 19th century, fact which led to the existence of Asian haplotypes within the European commercial breeds for traits such as backfat and litter size [The linkage phases between SNPs and the causative mutations that underlie the detected QTL are not always identical across populations. The D1, D2 and D3 Fter size . Therefoter size . The phaMeta-analysis of genome-wide association studies results can increase the power to detect association signals by increasing sample size. The use of this approach grew substantially in the genomics field in the last decade as the scientific community recognized the value of collaborating to combine genetic resources , 8–10. Tp values was assessed. The correlation value between JA and MA p values was higher than 0.7 (p < 0.05) for all traits suggesting that the significance levels were similar in the two analysis. Moreover, the p values from the individual population association studies were also compared with the results from the joint and meta-analysis. It was observed that the D1 p values were the most correlated to the JA (strong persistence of LD phase) and the MA p values, while D2 and D3 showed low levels of correlation. A limitation when assessing the agreement of the individual designs with the JA and MA is that the correlation only considers common variants between all five analyses. Some variants which could be highly associated in the individual populations might be disregarded due to being monomorphic in the others; however the correlations value gives a valuable overview at a genome-wide scale of the majority of the SNPs .One of the main objectives of this study was to compare the meta-analysis with the joint analysis, in which the common individual level genotypes are combined into a single dataset before the association study. Therefore, the agreement among the From the joint analysis summary statistic a total of eight clusters were assigned and from the meta-analysis output, six . ClusterIn the current study, growth (ADG) and carcass traits (related to fatness: BFT and to anatomy: MFR and CRCL) were investigated. The previous reported heritabilities range from 0.03–0.49 for ADG, 0.12–0.74 for BFT and 0.55–0.60 for CRCL . There aA total number of 20 genomic clusters were identified . They weThe traits in this study are influenced by several genes expressed during the prenatal and postnatal development. Carcass length is a trait mostly determined prenatally and proportional to the length of the spine, as well as the individual length of the vertebrae . AverageIGF2 was not assembled in the Sscrofa 10.2 genome version, but now has been positioned on the Sscrofa 11.1 reference genome. IGF2 has been described to have an effect on muscle mass and fat deposition [IGF2 resides is included on six of the clusters identified for ADG, BFT and MFR  which are partially overlapping  was identified for MFR via meta-analysis: ASGA0085597  = 24.04 and q value = 1.63e-20). Moreover, the partially overlapping clusters located on SSC2 , FGF4 , FGF19  and VEGFB  as well as genes responsible for the maintenance of gastrointestinal epithelium (GO: 0030277): MUC6 , MUC2 , MUC5AC  and MUC5B . Other genes underlined for ADG, BFT and MFR are HRAS  and DHCR7 . Cluster 10 and 12 found via the joint and meta-analysis narrowed down a region specific for MFR which contains associations for several olfactory receptors which reside in the genomic region SSC2: 12–14 Mb, gene family which is known to have significant expansion throughout time within the pig genome [Several known genes that have their functions previously reported were associated to the traits. One of the most prominent genes, the position . The regrlapping . The hig on SSC2  harbor gPPARD  was found as a good gene candidate for fat deposition traits [p value) = 6.11 and q value = 3.5e-4) is located in one intron of the gene. Furthermore, the critical region on SSC7 harboring the two clusters for BFT overlaps with the clusters 16 and 17 which were assigned to CRCL in D2 and in the joint analysis, respectively. The common significant variants associated within these clusters for BFT and CRCL also demonstrate discordant direction of effects. This suggests the great interplay between BFT and CRCL associated variants leading to pleiotropic consequences on the phenotypes. It is then reasonable to believe that in prenatal developmental stages the horizontal growth of the animal is already mainly determined by the number of vertebrae and their length while the same or other genes contribute conversely in postnatal existence of the individual ensuring the vertical growth of the backfat thickness. For that reason, the following genes which are located in a highly associated region for both BFT and CRCL (SSC7: 24–26 Mb) were suggested: BMP5  part of the transforming growth factor-beta (TGF-beta) signaling pathway, HMGCLL1 , GFRAL , a receptor required for GDF15 (Growth differentiation factor 15) mediated reductions in food intake and body weight in mice with obesity [HCRTR2 .On SSC7 from 19 to 38 Mb, clusters 5 and 7 showed associations with BFT for the D2 population and in the joint design. Several other studies pinpointed QTLs related to fat traits in the same region , 41. Then traits . One of  obesity  and HCRTVRTN  [LTBP2  [TGFB3  [PLCB1 , FLRT3  and FERMT1 . Nevertheless, the most interesting finding was situated close to the highest significant SNP for CRCL in D1  = 28.68 and q value = 9.25e-25) and is represented by BMP2 . The bone morphogenetic protein 2 (BMP2) belongs to the same family as BMP5 and is involved in the transforming growth factor-beta (TGF-beta) signaling pathway, playing a role in bone and cartilage development.Cluster 13, 18 and 20, which mostly overlapped, contained significant associations for carcass length for D1 design, for the joint analysis and the meta-analysis. This genomic region contains genes which have been already associated with carcass length: 624,273) , LTBP2   and TGFBIGF2, VRTN and TGFB3). Finally, among the additional candidate genes being suggested, BMP2 is being proposed as a strong candidate gene for carcass length. The findings of this study provide novel insights into approaches of dissecting the genetic basis of growth and carcass traits and indicate directions of further research which will lead to the identification of causal mutations affecting traits relevant in pig breeding programs.A genome-wide association study was conducted for growth and carcass traits using SNP-chip information from three populations sharing a common founder breed (Piétrain). An individual population GWAS was conducted and two strategies for combining the datasets were employed: a joint population GWAS and a meta-analysis of the individual population GWAS summary statistics. While the joint population GWAS and the meta-analysis did not identify new associated regions besides the ones identified in the individual populations, both approaches had a positive impact on the mapping resolution which implies that causative mutations can be identified with higher accuracy. Depending on the access to the complete original datasets, the strategies can complement or substitute each other. A total of 20 genomic clusters were pinpointed and they contained genes previously associated with the traits Click here for additional data file.S1 Table10 (pgenome-wide) was 5.95 for D1, 5.91 for D2, 5.87 for D3, 5.81 for D1D2D3 and for MA. The nominal significance threshold was—log10  = 4.30. Genome-wide significant SNPs (pgenome-wide < 0.05) are written in boldface.The genome-wide significance threshold—log(DOCX)Click here for additional data file.S2 Tablep < 0.05, except for the ones marked as ns = not significant.All significant at (DOCX)Click here for additional data file.


Exploiting genetically diverse lines to identify genes for improving crop performance is needed to ensure global food security. A genome-wide association study (GWAS) was conducted using 46,268 SNP markers on a diverse panel of 143 hexaploid bread and synthetic wheat to identify potential genes/genomic regions controlling agronomic performance (yield and 26 yield-related traits), disease resistance, and grain quality traits. From phenotypic evaluation, we found large genetic variation among the 35 traits and recommended five lines having a high yield, better quality, and multiple disease resistance for direct use in a breeding program. From a GWAS, we identified a total of 243 significant marker-trait associations (MTAs) for 35 traits that explained up to 25% of the phenotypic variance. Of these, 120 MTAs have not been reported in the literature and are potentially novel MTAs. In silico gene annotation analysis identified 116 MTAs within genes and of which, 21 MTAs were annotated as a missense variant. Furthermore, we were able to identify 23 co-located multi-trait MTAs that were also phenotypically correlated to each other, showing the possibility of simultaneous improvement of these traits. Additionally, most of the co-located MTAs were within genes. We have provided genomic fingerprinting for significant markers with favorable and unfavorable alleles in the diverse set of lines for developing elite breeding lines from useful trait-integration. The results from this study provided a further understanding of genetically complex traits and would facilitate the use of diverse wheat accessions for improving multiple traits in an elite wheat breeding program. Triticum aestivum L.) production region of Northern Kazakhstan and Western Siberia is approximately 15 million ha, in a belt 600-1000 km wide between the desert of Central Kazakhstan and boreal forests of Siberia (latitude 45-47° north). The crop is grown under continental climatic conditions with long cold winters, short hot summers, and annual rainfall that varies from 280-300 mm in the south to 400-450 mm in the north. Spring wheat is normally planted in mid-May and harvested in mid-September. A detailed description of the production environments, biotic and abiotic stresses, varieties, and breeding challenges was reported previously , and increasingly ambitious in occupying the export markets. Unlike, modern cultivars grown in similar environments of North America, the spring wheat varieties grown in this region are tall and sensitive to day length (Rht (reduced height) and Ppd (photo period sensitivity) genes, or the 1B.1R translocation, Siberian varieties have achieved annual genetic gains of 0.7% since 1900 (Puccinia graminis f.sp. tritici) and leaf rust (incited by P. triticina) resistance while maintaining local adaptation, drought tolerance, and grain quality. However, to achieve these goals require inclusion of diverse novel source of genetic resources, characterization of their genetic diversity and traits, and identification of beneficial genes for their utilization in a wheat breeding program.Both Kazakhstan and Russia are major wheat producers, self-sufficient in wheat grain  represent a particularly important group of novel genetic resources because of their wide genetic diversity, resistance/tolerance to multiple biotic and abiotic stresses, easy crossing compatibility with wheat, and large number of novel favorable alleles compared to wild relatives . Several studies have used GWAS to unravel novel loci associated with agronomic performance, disease resistance, and quality traits in bread wheat , 2019a. T. turgidum L.) and 13 diverse accessions of Ae. tauschii. The second group comprised of 39 winter synthetics developed by CIMMYT from crosses between five winter durums  and 10 different Ae. tauschii accessions. The third group comprised of 14 bread wheat lines originated from USA breeding programs. The fourth group comprised of 77 bread wheat lines developed by KASIB (Kazakhstan and Siberian) breeding program, where14 lines were from Kazakhstan and 63 from Siberia, Russia (Table S1). Genome-wide diversity analysis conducted in these lines showed considerable genetic diversity among lines from different breeding programs, and the population structure analysis identified that these lines can be divided into three subgroups based on the type of wheat and their geographical origin  from crosses between Langdon durum (Two years of field experiments (2017 and 2018) were conducted at the research farm located at Omsk State Agriculture University  under rainfed conditions. The experimental design used in this study was a randomized complete block design with four replications.Blumeria graminis f. sp. tritici), and Septoria tritici blotch ]; area under disease progress curve (AUDPC) for leaf rust and stem rust; 27 agronomic (grain yield and yield related traits including root traits) and two grain quality (grain protein and gluten content) traits under rainfed conditions. Grain yield related traits were emergence, days to heading, grain length, grain area, grain circularity, grain perimeter, grain weight per plant, grain weight per spike, grains per spike, number of spikes, spike length, spike weight, spike density, and spikelet numbers at maturity, spike harvest index, peduncle length, plant height at maturity, leaf number, number of productive tillers and plants at maturity, harvest index, thousand kernel weight, dry plant weight with roots, root-traits including root diameter, root volume, and total root length. These traits were measured using standard protocol described previously  was calculated using following model.2g, σ2yr, σ2gxyr, and σ2e are the variance components for genotype, year, genotype × year, and error, respectively, and y and r are the numbers of years and replications, respectively.An individual analysis of variance for each year was performed due to the significant genotype x year interactions for most of the traits under study . Best liPstI and MsPI), and pooled libraries were sequenced using Illumina HiSeq . SNP discovery was performed using TASSEL v.5.2.40 GBS v2 Pipeline  using Bayesian clustering algorithm  was used as a random effect (P > 1.08 × 10−6 (-log10P = 5.97). The percent of phenotypic variance explained by each significant SNP was estimated using multiple-linear mixed model as described previously (The multi-locus mixed linear model using FarmCPU (Fixed and random model Circulating Probability Unification) algorithm implemented in R package (r2). Pairwise LD 2r values were plotted against the corresponding physical distance, and a non-linear regression model was fitted to estimate the genome-wide LD decay  among markers was estimated using the full matrix and sliding window size of 50 in Tassel V5.2.52 (http://plants.ensembl.org/Triticum_aestivum/Tools/VEP/).The putative genes underlying the significant SNPs and their annotations were obtained from the IWGSC RefSeq V1.0 annotations provided for the Chinese spring wheat . The prehttps://gsajournals.figshare.com/s/6434bc736b63a242e546. Figure S1 contains Manhattan and quantile-quantile (Q-Q) plots for 35 traits in 143 diverse wheat accessions obtained from a genome-wide association study. Figure S2 contains principal component biplot analysis among agronomic, disease resistance, and grain quality traits in 143 diverse wheat accessions. Figure S3 contains the physical distribution of 46,268 genotyping-by-sequencing derived SNPs within a 1-Mb window size on 21 chromosomes (Chr) of 143 diverse accession of wheat. Table S1 contains details of the 143 diverse wheat accessions used in this study. Table S2 contains genotype-by-sequencing derived SNPs identified in 143 diverse wheat lines. Table S3 contains details of significant markers associated with 35 traits in 143 diverse wheat accessions grown in two seasons (2017 and 2018) in Siberia. Table S4 contains details of potential candidate gene functions harboring SNPs affecting agronomic, disease resistance, and grain quality traits from two years (2017 and 2018) of experiments conducted in Siberia using 143 diverse wheat accessions. Table S5. Genomic fingerprinting of 143 diverse accessions of wheat showing the distribution of favorable (1), unfavorable (0) and heterozygote (0.5) alleles of significant marker-trait associations identified in this study. The synthetic and bread wheat seeds are available upon request. Supplemental material available at figshare: https://doi.org/10.25387/g3.9943682.Supplementary files including genotyping-by-sequencing derived SNP marker data are available in figshare at -2 with an average of 322 g m-2 in 2017 and it ranged from 77 to 657 g m-2 with an average of 391 g m-2 in 2018. Similarly, variation for grain protein content ranged from 14 to 22% with an average of 18% in 2017 and it ranged from 14 to 21% with an average of 17% in 2018  revealed significant cross-over genotype x year interaction for most of the traits under study . Therefo in 2018 .2, protein content >15.5%, thousand kernel weight > 41.5 g, spike harvest index >0.74, and low to moderate level of disease severity scores against multiple foliar fungal diseases (Table S1).This study identified several lines with low disease severity scores.. For instance, seven, 10, and three lines had very low disease severity scores against leaf rust , stem rust , and powdery mildew , respectively, in 2017 (Table S1). One line (2017ENTRY # 69) had the lowest severity scores against all these diseases, and five lines  had lower disease severity scores against both leaf and stem rusts. In 2018, we had 14 lines  with the lowest disease severity scores against leaf rust and one line (2017ENTRY #24: ‘AISBERG/AE.SQUARROSA_511’) had the lowest severity score against stem rust. Based on two years of data, we have recommended top five ranking lines  that had grain yield > 453 g/m2H = 0.89) and spike density (2H = 0.89), followed by leaf rust AUDPC (2H = 0.88), gluten content (2H = 0.87), leaf rust severity score (2H = 0.85), and heading date (2H = 0.83) whereas the lowest heritability was observed for root diameter (2H = 0.28). The heritability observed for grain yield (2H = 0.75) and thousand kernel weight (2H = 0.72) in this study were also in the high heritability range (2H > 0.70).Broad sense heritability of the traits ranged from low to high heritability . The higA principal component analysis (PCA) based on correlation matrix was performed to investigate the relationship among all 35 traits . Approximately, 68.4% of the total variation in 143 lines were explained by the first four PCs. The first two PCs were predominantly associated with agronomic and grain quality traits, and it contributed ∼54% of the total variation in the data. Wheat rust diseases such as leaf and stem rusts represented the third PC, and it contributed 8.9% of the total variation. Similarly, root traits such as total root length and root volume were associated with the fourth PC, and it contributed 5.5% of the total variation.A total of 192,876 GBS derived SNPs were obtained based on the Chinese Spring RefSeq v1.0. After removing SNPs with MAF <5% and missing data >20%, 46,268 SNPs were retained for subsequent analysis (Table S2). The marker density for the A, B, and D genome were 20.2, 20.8, and 20.2 Mb per SNPs, respectively .2r against physical distance showed that the LD decay with increasing physical distance. The average LD decayed below 2r = 0.2 for the entire genome was 1.06 Mb where, the greatest extent of LD was observed on the D genome (3.06 Mb), followed by the B (1.71 Mb) and A (1.06 Mb) genomes  that were associated with 35 traits including agronomic, disease resistance, and grain quality traits, . A total of 116 MTAs were found within genes and of these, 21 MTAs were annotated as a missense variant and had a moderate impact (Table S4). Moderate impact genes were found for several traits such as grain length, grain perimeter, heading date, leaf rust AUDPC, number of productive tillers, number of spikes, peduncle length, plant weight with roots, protein content, Septoria, spike length, spike weight, spikelet number, and thousand kernel weight. The detailed annotations of these gene-IDs are provided in The functional annotation of genes underlying significant SNPs was identified through the IWGSC RefSeq v1.1 annotation and the impact of the genes was identified through variant effect predictor from EnsemblPlants (Multi-trait MTAs are the common genomic regions controlling multiple traits. The present study identified 23 multi-trait (group affects 2-4 traits) MTAs located on chromosomes 1A, 1B, 2A, 2D, 3B, 4A, 4B, 5A, 5B, 5D, 6B, 7A, and 7D  and of tGenomic fingerprinting for 243 MTAs with the distribution of favorable, unfavorable, and heterozygote alleles in 143 diverse wheat lines is shown in A wide range of distribution of favorable alleles between synthetic and bread wheat accessions was observed . Previous studies have also identified these traits as complex traits with similar heritability  mapping studies found QTL/MTAs for grain yield on chromosomes 3A  identified for these traits have not been previously reported and they are potentially novel MTAs controlling these traits. For other phenological traits such as plant height and heading date, most of the QTL identified in this study have been reported previously on chromosomes 1B, 3A, and 5A  for leaf rust and stem rust was measured based on the repetitive disease severity assessments and is used to assess quantitative disease resistance in wheat accessions . Two MTASeveral MTAs identified for grain quality traits such as grain protein and gluten content have been previously reported in the literature. For instance, the previous QTL similar to our study for grain protein concentration were on chromosomes 1A , 4A Pus. SimilarThe variant effect predictor available on Ensemble was used to determine the functional consequences of the variant within 5 kb of the SNP location. In this study, 116 significant SNPs were identified within genes based on in silico candidate gene analysis. Of which, 21 SNPs were annotated as a missense variant and had moderate impact on genes. These missense variants cause an amino acid change and such changes may alter the function of genes, which makes these annotated gene as a strong candidate gene for future functional characterization studies in wheat. Some of the genes such as NBS-LRR disease resistance protein , 2018d, Several multi-trait MTAs identified in this study were also positively associated with each other. This result suggested that multiple traits may have similar genetic basis and simultaneous improvement of multiple traits could be possible. Additionally, most of the co-located MTAs were present in genes. We have identified several favorable alleles that increases the agronomic performance, disease resistance, and quality traits in wheat accessions. Although the number of favorable alleles distribution in different breeding program did not significantly vary from each other, alleles present in each breeding program were different for different traits, which indicates the use of diverse accessions of wheat for different breeding objectives. Genomic fingerprinting for MTAs with favorable and unfavorable allele distribution in 143 diverse accessions could assist in building strategies while integrating the useful traits in an elite breeding lines. This study have recommended top performing five lines with higher number of favorable alleles for multiple traits, indicating the potential use of these lines in a breeding program. The favorable alleles identified for multiple traits in this study could be useful for pyramiding superior alleles in elite wheat germplasm upon validation in an independent population.The present study found considerable useful genetic variation for improving multiple traits and recommended five superior lines for directly use in a breeding program. A total of 243 genomic regions for improving agronomic performance, disease resistance, and grain quality related traits were identified. Of these, 120 favorable genomic regions were found to be novel MTAs, where large numbers were distributed on the D genome, indicating the usefulness of diverse lines in improving the D-genome diversity in elite wheat germplasm. Furthermore, this study identified co-located multi-trait markers within important gene families. The present study fingerprinted 143 diverse wheat lines for trait-associated markers and provided a detailed understanding of genetic architecture of agronomic, disease resistance and quality-related quantitative traits using diverse accessions of synthetic and bread wheat lines. However, further investigation on identified genomic regions could assist multi-trait marker-assisted breeding program.


The knowledge of genetic trends and trade-offs between growth and reproductive traits might be useful to understand the evolution of these traits in livestock and natural populations of animals. We estimated the genetic trends and trade-offs between pre-weaning growth and calving intervals of Nellore animals from a commercial farm. Two-trait animal models were used to estimate covariance components and breeding values (EBV) for direct and maternal genetic effects of pre-weaning growth and direct genetic effects of calving intervals. Regression analyses were performed to examine the relationship between direct and maternal EBV of pre-weaning growth and direct EBV of calving intervals (dependent variables) and the coefficient of generation of each animal (independent variable). We also performed regression analyses to examine the relationship between direct EBV of calving intervals (dependent variables) and direct and maternal EBV of pre-weaning growth (independent variables). The genetic trends for direct and maternal genetic effect for pre-weaning growth were significant and presented genetic evolution in the studied Nellore herd. The genetic trends for the reproductive traits were also significant but indicated genetic changes in an unfavorable way. The genetic correlations between direct effects of pre-weaning growth and calving intervals traits and the genetic correlations between maternal effects of pre-weaning growth traits and direct effects of calving interval traits were not different from zero. The presence of trade-offs between the direct effects of growth and reproductive traits were confirmed through regression from direct EBV of calving intervals over EBV of pre-weaning growth traits. In addition, regression analyses showed that selection to increase pre-weaning growth also increased calving intervals. Our results showed that pre-weaning growth and calving intervals are increasing over generations and that trade-offs occurred between those traits in the studied Nellore herd. Animal growth is important in livestock production and in natural populations because it is related with economic profit and adaptation –3. In adIndirect selection for improving maternal effects occurs through selection for pre-weaning growth of the offspring because there is an association between the maternal genotype and the offspring’s phenotype ,3,8. HowTrade-off between traits is the result of an unfavorable correlation between them . The traBos taurus indicus). Genetic parameters and trends of pre-weaning growth and calving intervals were estimated. The trade-offs between direct and maternal genetic of pre-weaning growth and direct effects of calving intervals were inferred through genetic correlation and regressions analysis.Trade-offs can occur at genetic or phenotypic levels and generally happen between different traits measured in the same individual ,14 or inBos taurus indicus) developed in tropical regions and it accounts for approximately 40% of Brazilian cattle herd. The database used in this study contained records of Nellore animals born between 1994 and 2015 in a commercial herd. This herd underwent to selection for over 38 years and has animal records up to nine generations. The main selection criteria were related to growth traits. Data structure of this herd was suitable for this study because contained data regarding pre-weaning growth and reproductive traits of cows and regarding their calves for estimation of maternal genetic effects . During the cow-calf phase, calves were kept with their dams in 30-hectare pastures and weaned at approximately 205 days of age.Nellore is a zebu breed (When editing the database for body weight (BW), to calculate it in standard ages (in the case of BW120), information of animals with measurements performed between 75 and 165 days of age were utilized. From this body weight, birth weight was subtracted and the average daily gain was calculated and then the value obtained was multiplied by 120 days. Summing the value with birth weight, the body weight at 120 days of age (BW120) was obtained. Regarding BW205, the same procedure was realized, but with phenotypic data of animals between 160 and 250 days of age. These both measurements were considered as pre-weaning growth traits. Calving interval (CI),calculated as the difference between calving dates from two consecutive calvings, was used as reproductive trait considering the first, second, third, and fourth calving intervals  when editing the data. We considered birth and calving date observations from 1994 to 2014. Calving intervals outside the range between 280 and 1448 days were discarded. Descriptive statistics regarding the data set used in the analyses are presented in The genetic analysis of maternal ability and reproductive traits was performed through two-trait animal model ,17. Thesyijklm represents the BW120 or BW205 for animal i; μ is the general constant present in all observations; ai is the additive genetic effect of the animal i; mj is the maternal genetic effect of the dam j, which is the animal’s mother; pmj represents the permanent maternal environmental effect of the dam j; FEk is the fixed effect of the k management group of the animal on the weighting day; b1 is the regression coefficient associated with the linear effect of the calf age to the evaluation (lAGE); c1 is the regression coefficient associated to the linear effect of the age of the dam at calving (ADCm); c2 is the regression coefficient associated to the quadratic effect of the age of the dam at calving  were regressed as a function of maternal and direct EBV for pre-weaning growth traits (BW120 or BW205). These regressions were performed to base the inferences about the trade-offs. The regressions of direct EBV for reproductive traits in terms of direct EBV of BW120 or BW205 have considered information of cows having reproductive trait data and their own BW information (BW120 or BW205). In the regressions of the direct EBV of calving interval over maternal EBV of BW120 or BW205, we considered information of dams that had reproductive trait data and offspring with BW record (BW120 or BW205). In this way, all regressions have been considered the genetic values from animals that had phenotypic information for both traits.Posterior means of heritabilities and correlations are presented together with their lower and upper limits of the highest posterior density intervals (HPD) with 90% of the samples (between parentheses). The direct heritabilities of BW120 and BW205 were 0.23  and 0.23 , respectively. The maternal heritabilities of BW120 and BW205 were 0.10  and 0.08 , respectively. The genetic correlations between direct and maternal effects of BW120 and BW205 were not different of zero and the posteriori means were 0.18  and 0.02 , respectively. The ratio between the maternal permanent environmental effect and the phenotypic variances of BW120 was 0.14  and of BW205 was 0.14 .The direct heritabilities of first, second, third and fourth calving intervals were 0.09 , 0.09 , 0.10  and 0.10 , respectively. Complementary information about covariances of pre-weaning growth and calving intervals can be found in Table B in P< 0.0001), with average changes of 1.58 kg/generation for BW120 and 2.65 kg/generation for BW205 (P< 0.0001), with average changes of 0.10 kg/generation for BW120 and 0.16 kg/generation for BW205 (Direct genetic trends of BW120 and BW205 were positive and significant (or BW205 . Maternaor BW205 .P< 0.0001), with average changes of 4.27, 3.20, 6.09 and 3.48 days/generation, for CI1, CI2, CI3 and CI4, respectively  and the direct effects of calving intervals did not differ from zero, except the genetic correlation between BW205 and CI3 . HoweverThe results showed that for every additional change (1 kg) in direct EBV of pre-weaning growth traits we can expect the calving intervals to increase by an average of 1.6 days .Which caP < 0.0001, In general, the genetic correlations between maternal effects of pre-weaning growth traits and the direct effect of calving intervals were not different from zero . HoweverAt this point, it is also important to highlight that selection to increase genetic maternal effects of pre-weaning growth can be more impairing to dam reproduction compared with selection to increase genetic direct effects. To confirm this result we computed the confidence intervals for the regression coefficients for direct and maternal EBV and they were not overlapped , the statistical model needs to contemplate additional terms that control the factors which affect these traits.Furthermore, the accuracy and the precision of covariance parameters estimates for direct and maternal effects are affected by the availability of phenotypic measurements of the dams and of their progenies . And theThe phenotypic records used in this study are from a commercial farm and selection has been performed to increase BW at weaning (≈ 205 days) and post-weaning (≈ 550 days). At weaning, the lighter calves  are culled and the rest are kept for post-weaning evaluation. At the end of the post-weaning evaluation, only the young bulls with the highest pre-weaning growth rate are kept to replace old bulls and only the heifers that conceive before 30 months of age are kept to replace open cows.e.g., selection for improving BW measured in further ages).Body weight has moderate heritability ,21, and The direct genetic effects are larger than maternal genetic differences  between direct and maternal effects ,26. HoweCalving interval heritability is low ,30 and uThe genetic correlations estimates of the present study  and otheBody weights in different ages are moderately to highly correlated ,32. In tGenetic correlations between maternal effects of pre-weaning growth and direct effects of calving interval were not significant to indicate the presence of trade-offs . Weak geCalves that are heavier at weaning, for instance, are more desirable in a livestock framework because they can reach the slaughter weight earlier than lighter calves. Notwithstanding, the cow needs to invest energy resources to wean a heavy calf. Increasing milk production is an expensive investment in this process, with an increase in energy requirement  and a neThe residual correlations between pre-weaning growth and calving intervals were not significant  and indiThe phenotypic correlations between pre-weaning growth and calving intervals were low but significant . These vWe observed that regression coefficients of maternal effects were greater than regression coefficients of direct effects of pre-weaning growth traits Figs . This reMaternal effect of pre-weaning growth is associated with milk yield  Click here for additional data file.S1 FileSupplementary material and methods, and tables A, B, and C.(DOCX)Click here for additional data file.S1 FigVariation of estimated breeding values (EBV) for direct effects for body weight at 120 (left) and 205 (right) days of age (BW120 and BW205) along generations. The EBV of each animal is represented by gray dots, the means of EBV for each generation, by green dots), genetic trends of EBV, by red line, and the confidence interval by blue line. Slope (standard error) and significant level are showed for each analysis of pre-weaning growth and calving intervals.(TIF)Click here for additional data file.S2 FigVariation of estimated breeding values (EBV) for maternal effects for body weight at 120 (left) and 205 (right) days of age (BW120 and BW205) along generation. The EBV of each animal is represented by gray dots, means of EBV for each generation, by green dots, genetic trends of EBV, by the red line, and the confidence interval, by blue line. Slope (standard error) and significant level are showed for each analysis of pre-weaning growth and calving intervals.(TIF)Click here for additional data file.S3 FigThe EBV of each animal is represented by gray dots, means of EBV for each generation, by green dots, genetic trends of EBV, by red line and the confidence interval, by blue line. Slope (standard error) and significant level are showed for each analysis of pre-weaning growth and calving intervals.(TIF)Click here for additional data file.


Aphelocoma californica) and Woodhouse’s Scrub-Jays (A. woodhouseii) come into secondary contact and hybridize in western Nevada. Although previous work investigated divergence and gene flow between these species using a handful of microsatellite markers, the hybrid zone has not been studied using genome-scale markers, providing an opportunity to assess genome-wide introgression, test for a genetic basis for ecomorphological traits, and compare these estimates to those derived from microsatellites.Hybrid zones are geographic regions where genetically distinct taxa interbreed, resulting in offspring of mixed ancestry. California Scrub-Jays (Using variant sites flanking ultraconserved elements (UCEs), we performed population assignment and quantified hybrid ancestry for 16 individuals across the zone of secondary contact. Our study included 2468 SNPs distributed throughout the genome, allowing discrimination of genetic affinities of hybrid individuals that were similar to estimates from microsatellites. We show a relationship between bill and wing length and the genetic composition of individuals that was not found in prior work using microsatellites, suggesting a genetic basis for these traits. Our analyses demonstrate the utility of UCEs for the analysis of hybrid zones and provide a basis for future studies to identify the genomic architecture of speciation and phenotypic differences between these incipient species.The online version of this article (10.1186/s13104-019-4481-z) contains supplementary material, which is available to authorized users. Hybrid zones are windows into the process of species formation , 2. The Molecular marker development is changing the way hybrid zones are studied, allowing genome scale inferences. Whole genomes are becoming the gold standard for hybrid zone studies , but whoAphelocoma californica) and Woodhouse’s Scrub-Jay (A. woodhouseii) using 13 microsatellite loci. These two recently-split species [A. californica has a more heavy, hooked bill compared to the narrower and longer bill of A. woodhouseii; these differences are thought to be adaptations to local resources [Here, we compare two molecular marker datasets across an avian hybrid zone to test if SNPs mined from UCEs give similar results to prior microsatellite work, and we use the genome-scale UCE data to assess whether some ecomorphological traits have a genetic basis. Previously, Gowen et al.  examined species  show difesources , 18. Howesources  examinedTo determine if UCE data could shed light on this question, we first assessed whether sequence capture of UCEs would collect enough informative SNPs to determine genetic affinities of individuals. Then, we compared individual-level population assignment based on UCE data to that estimated from a previous dataset of 13 microsatellites for the same individuals . FinallyAphelocoma californica, A. woodhouseii, and hybrids along a 722 km transect running northwest to southeast from the Sierra Nevada in northeastern California into west-central Nevada , we analyzed the SNP dataset in STRUCTURE v2.3.4 [K = 2. The Q score reflects the estimated proportion of nuclear variation inherited from A. californica or A. woodhouseii. We ran three independent analyses for each value of K = 2, using the admixture model and correlated allele frequencies. Each run had a burn-in of 100,000 generations and 1,000,000 Markov chain Monte Carlo generations post burn-in. We calculated per-locus FST on SNPs within the core UCE region, regions flanking UCEs, and microsatellites from Gowen et al. [To assess population assignment and obtain estimates of individual-level hybrid ancestry  to identify axes defining the most variation. We assessed relationships between Q scores and univariate and multivariate morphological traits at two spatial scales: across the oak woodland and pinyon-juniper woodland  and within the immediate area of contact .We conducted linear regression analyses of morphological traits with estimates of s length , 21. Mor8 raw reads from each Illumina library. We retained 99% of the read data after trimming of low-quality bases and removal of adapter contamination .Estimates of Q score . Narrowing the focus to the area of contact, where environmental differences are minimized, Q score explained ~ 67% of the variation in bill length .Although sample sizes were small, there were significant correlations between estimates of hybrid ancestry and two ecomorphological traits: wing length and bill length  having nearly 50/50 ancestry estimated using our SNP data.These results add to previous findings demonstrating that UCEs are informative genomic markers for addressing questions at shallow evolutionary timescales , 23–26 sQ scores than individuals at the 4 end sites , although sample sizes were low and this effect was not significant (p = 0.11). While it is difficult to know which Q score is the more accurate measure because we do not know the true ancestry of individuals, hybrid values from SNP data were more similar for the two individuals within sites , suggesting SNP data might be, if not more accurate, at least more precise than the microsatellite data.We also show that hybrid ancestry estimated from ~ 2400 SNPs drawn from UCEs are correlated with estimates from the 13 microsatellites, especially for individuals predicted to be of pure or nearly pure ancestry of one species (i.e. those at the ends of the cline). Individuals in the 4 middle sites (i.e. sites 3–6) showed less correlation between SNP-based and microsatellite-based A. californica DNA would also be more likely to have a short and stout bill, provided bill shape is under genetic control. However, there is considerable room for noise in this kind of analysis, because bill size and shape are likely polygenic [A. californica DNA might nevertheless have inherited A. woodhouseii genes that affect bill dimensions. Despite these caveats, and our low sample size, we found an association between hybrid score and both bill length and wing length. This finding sets the stage for follow-up studies with larger sample sizes where pure individuals and natural backcrosses could be analyzed using whole-genome analyses to study the genomic underpinnings of these traits.More precise estimates of hybrid ancestry might help determine whether morphological traits have a genetic basis. Building off prior work on these species , 17, 18,olygenic  and the Hybrids zones are natural laboratories for investigating speciation and the genomic architecture of phenotypic traits. Here, we show that UCEs, and SNPs contained within them, are promising markers for the study of hybrid zones. Although UCEs do not typically occur within protein-coding genes when collected from vertebrate taxa, SNPs within UCE loci are amenable for studying so-called “neutral” processes in a similar manner as they are used currently in phylogeography and phylogenetics. Moreover, UCEs are universal across major taxonomic groups, abundant throughout the genome, efficient to collect, and useful with available tissue types, including degraded DNA extracted from museum specimens . For theWhole genome sequences and more individuals would be needed to identify the genomic underpinnings of speciation and phenotypic differences between these species.Additional file 1. Methods for DNA extraction, library preparation, sequence capture, and variant calling.Additional file 2: Table S1. Statistics on sequenced DNA data for individual museum specimens.Additional file 3: Figure S1. Linear regression of hybrid ancestry (Q scores) estimates.Additional file 4: Figure S2. Bootstrapped average per-locus FST values.Additional file 5: Table S2.R2 values between hybrid index and phenotypic traits.Additional file 6. VCF file of SNPs from UCE loci.


MC1R variants are the principal genetic cause of red hair colour, but with variable penetrance. Here, we have extensively mapped the genes responsible for hair colour in the white, British ancestry, participants in UK Biobank. MC1R only explains 73% of the SNP heritability for red hair in UK Biobank, and in fact most individuals with two MC1R variants have blonde or light brown hair. We identify other genes contributing to red hair, the combined effect of which accounts for ~90% of the SNP heritability. Blonde hair is associated with over 200 genetic variants and we find a continuum from black through dark and light brown to blonde and account for 73% of the SNP heritability of blonde hair. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour.Natural hair colour within European populations is a complex genetic trait. Previous work has established that  Natural hair colour in Europeans is a complex genetic trait. Here, the authors carry out a genome-wide association study using UK BioBank data, suggesting that in combination with pigmentation genes, variants with roles in hair texture and growth can affect hair colouration or our perception of it. Furthermore, hair colour is largely determined by only a few well-characterised cell types: the melanocytes where the melanin pigment is made, the keratinocytes of the hair to which the pigment is transferred and fibroblasts of the dermal papilla, which signal to and regulate the melanocytes. It is thus an excellent model system to explore genetic and cellular interactions in development and homoeostasis. Hair colour variation is partially correlated with skin and eye colour variation, reflecting differences in cellular interaction in different tissues3.Natural hair colour within European populations is strikingly variable and is a complex genetic trait that is impacted relatively little by known, non-genetic, factorsMC1R gene5. Less well known is the observation that most of these variants are only partially penetrant, and some of them have very low penetrance indeed6. Other genetic factors must be interacting with MC1R to modify the penetrance of these variants. MC1R is a G-protein-coupled receptor, expressed on the surface of skin and hair melanocytes. Binding of the MC1R cognate ligand, α-melanocyte stimulating hormone (α-MSH), induces a melanogenic cascade resulting in the production of dark eumelanin. This is packaged into vesicles, termed melanosomes, for transport to epidermal keratinocytes where it provides protection against ultraviolet radiation. The cellular trafficking of melanosomes to keratinocytes in the hair follicle additionally gives colour to the growing hair. Loss of MC1R signalling in many vertebrate species results in the inability of the melanocytes to produce eumelanin that instead default to synthesising phaeomelanin, a red or yellow pigment. MC1R has a second ligand, an inverse agonist, agouti signalling protein (ASIP)7. Overexpression of ASIP, in mice for example, leads to synthesis of only yellow phaeomelanin, even in the presence of a functional MC1R and α-MSH8. Previous studies have defined a role for ASIP in red hair in humans, but its molecular basis is largely unknown10.Several studies have examined the genetic basis of hair colour variation. Red hair is well established as being associated with coding variation in the 14. Each of these studies identified between 4 and 8 genes, with a total of 11 genes associated with hair colour differences. Most of these genes have been previously described as causing coat colour variation in mice , zebrafish (SLC24A5) and humans . However, during preparation of this manuscript a GWAS was reported identifying over 100 loci contributing to hair colour15. Hysi et al.15 analysed a subset of participants in a very large population health cohort of British individuals, UK Biobank, in addition to a similar number of individuals from 23andMe, totalling 290,891. This report, however, mostly considered hair colour as a single ordered variable including red hair. This has the potential to mask associations due to differences in genetic architecture for separate categories of hair colour.Until recently, genome-wide association studies (GWAS) identified only a small number of loci associated with blonde hair, compared to black and brownMC1R that contributes to red hair. Furthermore, we identify eight additional variants that explain most of the SNP heritability of red hair, including variants at ASIP, where an eQTL shows epistatic interactions with the poorly penetrant MC1R variants. Additional epistatic interactions are seen between MC1R and the HERC2/OCA2 locus and with PKHD1. Furthermore, we identify more than 200 genetic variants independently associated with multiple hair colours on the spectrum of blond to black. Notably, we find that many of the associated genes seem not to be involved in melanocyte biology per se, but are rather involved in hair growth or texture. This highlights the importance of the melanocyte–keratinocyte interactions in the determination of hair pigmentation and the impact of hair shape on colour perception.We report here the analysis of the majority of UK Biobank, a total of almost 350,000 subjects. By performing genome-wide analyses across hair colours, we have discovered novel variation in and around 16. In addition, of the individuals who were third-degree relatives (first cousins) or closer, identified by genotyping16, only one of any related group was analysed. This left 343,234 participants with hair colours shown in Table Participants in UK Biobank responded to the question “what is your natural hair colour” with one of six possible answers. We used only self-reported, white British individuals, confirmed by genotype15, but as expected the prevalence of red hair is higher and black hair is lower than in Southern European cohorts. Within UK Biobank a higher proportion of females report red or blonde hair than males and a much lower proportion of females report black hair. Whilst there may be some self-reporting bias, we and others have previously shown using colorimetry that females on average have hair that is both more red and lighter17.Generally the frequencies of different hair colours are comparable to other populations with considerable Northern European ancestry, such as the QIMR cohort from Australia16.Genotypes for more than 800,000 SNPs and indels were directly assayed by UK Biobank using custom Affymetrix arrays, and an additional ~40 M variants imputed using the Haplotype Reference Consortium panelλGC 1.024). The strongest association with red hair is located around the MC1R gene on human chromosome 16  does not originate from any observed amino acid changes, but is an SNP located some 97 kb from the 5′ end of MC1R and remains significant even after adjusting for all coding variants in MC1R. As we know that multiple MC1R alleles affect red hair colour, we performed stepwise conditional association testing and identified 31 additional association signals in this region at genome-wide levels of statistical significance (p ≤ 5×10−8), altering the odds of having red hair compared to brown and black hair (Supplementary Table MC1R coding region. Included in these are two missense variants rs368507952 (R306H) and rs200000734 (R213W) not previously associated with red hair colour.We performed a GWAS comparing individuals with red hair to a combined group of black- and brown-haired individuals. Accounting for genetic structure within the UK Biobank by inclusion of the first 15 genetic principal components adequately controlled the genomic inflation in our analysis  between the associated SNPs and multiple coding variants.In addition to these 10 coding variants, we find 21 associations beyond the MC1R, as a candidate transcriptional regulatory variant. Including this variant, the two newly associated missense variants described above and 13 previously described coding variants we find that the proportion of red-haired individuals with two MC1R alleles is 92%, whilst only 6.3% carry a single allele. The cases of red hair with only one or no variants  may be explained by, for instance, (a) rare coding variant alleles not genotyped or imputed in this study, (b) additional extragenic variation affecting MC1R expression, (c) dominant action of specific alleles, (d) variation in other genes in the same or a parallel pathway or (e) misreporting of hair colour.We asked how many cases of red hair can be accounted for by MC1R coding variation. We included rs3212379, located only 120 bp 5′ of the transcription start site of MC1R coding variants have different penetrance with respect to red hair (termed “R” and “r” for high and low penetrance)6. With this very large cohort we are able to more precisely quantify the degree of penetrance of each allele, whether as homozygotes or in combination with any other allele.  of POMC, which encodes α-MSH, the agonist of MC1R. Increased expression of POMC is likely to promote melanogenic signalling and thus dampen the effect of those MC1R variants which have some, albeit reduced, signalling activity. A single variant in an intron of RALY, located 5′ of ASIP, the gene encoding the inverse agonist of MC1R, was associated with red hair. This variant, rs6059655, is also an expression QTL (eQTL) for ASIP expression in skin, with the red-hair-associated allele showing higher mean expression levels20   Fig. . We suggHERC2 associated with a decreased probability of red hair. It is well established that variants in HERC2 alter transcription of the neighbouring pigmentation gene OCA2 which is additionally associated with blue eyes and blonde hair colour24. Recessive mutations in OCA2 result in albinism. It is possible that varied expression of OCA2 modifies the effect of reduced signalling though variant MC1R, such that red hair colour is not expressed.We find a variant in TSPAN10 gene, also known as oculospanin, which is highly expressed in melanocytes and retinal pigment epithelium. The lead SNP lies in strong LD (r2 = 0.995) with a non-synonymous variant  affecting a conserved amino acid. This association signal is also in moderate LD  with a previously reported association with increased hue-saturation of eye colour, which corresponds to darker eyes, in a Dutch cohort25. Previous targeted knockdown of the murine Tspan10 mRNA resulted in reduced melanocyte migration in a trans-well migration assay26, indicating this gene may be a good functional candidate as a novel hair colour gene.An association is also seen in the MC1R coding variants, including the known “R” and “r” MC1R red hair colour alleles, by constructing a logistic regression model whilst correcting for relevant covariates (see Methods). At a P value of 3.9×10−7 , we found consistent epistasis signals between MC1R variation and a ±1.5 MB region surrounding rs6059655, which is the hair colour-associated ASIP eQTL SNP . Alternatively some or all signals may each be correlated with the same variant that has been neither genotyped nor imputed. Many signals of association are close to, or within, previously known pigmentation genes from both human and model organism studies These allelic effects span a spectrum of OR and minor allele frequencies consistent with many other phenotypes with an underlying polygenic architecture28 . Several variants notably stand out, which have been previously associated with a variety of pigmentation related traits in humans , some of which have been specifically linked to alterations in transcriptional regulation (IRF4 and KITLG)30.Whilst red hair is essentially a Mendelian trait modified by additional loci, the genetic architecture of blonde hair colour is concordant with a polygenic trait. We performed a genome-wide association analysis comparing blonde hair to combined brown and black hair-coloured individuals. Following conditional association testing to uncover additional signals of association, we discover 213 lead variants associated with blonde hair colour Fig. . In manye28 Fig. . Using pMC1R, are also identified in our blonde hair analysis. Although 93% of individuals with red hair carry two MC1R variants, these make up only 15% of people who carry two MC1R variants. The majority of people with two variants have blonde (15%) or light brown hair (41%). The proportion of individuals with blonde hair decreases with one or no variants whilst the proportion with dark brown and black increases , as a linear combination of the allele-weighted regularised logistic regression coefficients. We found that self-reported black, dark brown, light brown and blond hair lie on an approximately linear spectrum . Among the variants with the highest probability are at RALY, upstream of ASIP as noted above and in the first intron of TSPAN10 for red hair. Most of the eQTLs are associated with gene expression at considerable distance and often with several genes. Among the most significant eQTLs, across all three datasets, are several missense variants in MC1R, which are independently linked to expression changes in multiple genes located up to several hundred kb from MC1R. These may well be synthetic associations reflecting weak LD and the unusual behaviour of this segment of the genome. Whist the colocalisation of hair colour association signals with skin tissue cis-eQTLs may appear promising, they are at best a strong indication of biological effect and will require extensive further hypothesis testing to establish any role in determining pigmentation.In order to aid the interpretation of our GWAS and identify functional hypotheses, we tested associated variants for statistical colocalisation with eQTL signals from skin biopsies in the GTexTo understand the transcriptional regulatory mechanisms that might underpin the observed genetic associations with hair colour, we examined the potential for these variants to affect the chromatin landscape in cell types relevant to pigmentation. Specifically, we tested histone tail modifications associated with gene activation or repression and with chromatin accessibility (DNase I hypersensitive sites) in melanocytes, keratinocytes, fibroblasts and other cells. Additionally, the proximity of several association signals to core promoter regions raises the possibility of alterations to TSS and pigmentation cell-specific regulatory factors, i.e. the melanogenesis master regulator MITF.32, we tested each annotation in each cell type where data were available (Table Using a permutation-based approach (GoShifter)33. We identified ~200 orthologous mouse genes in the database, which we analysed for site of expression and mutational phenotypes. Of the 172 genes with expression data, 89 were expressed in the skin (P = 1.3×10−9) (Supplementary Table P = 5.2×10−7). Not surprisingly, 18 of these affected pigmentation, but we unexpectedly found that 70% affect primarily skin, hair or other skin appendages rather than pigmentation . In addition, the refractive and reflective properties of individual hairs may affect perceived colour35 and there is evidence that different coloured hairs have different morphology. Vaughn et al. have demonstrated a strong inverse correlation between the lightness of hair colour and the diameter of the shaft; blonde hair is thinner than dark36.Follicular melanocytes, keratinocytes and dermal papilla cells have mutual interactions; the dermal papilla signals to melanocytes with ASIP, the melanocytes transfer melanin granules into the keratinocytes. Perturbations of these interactions could affect the amount and type of melanin delivered to the hair. Furthermore, variation in growth rate could impact the effectiveness of melanin transfer. Recent GWAS have identified 14 loci associated with hair shape variationIn summary, the very large dataset provided by UK Biobank has enabled us to dissect the complex genetic nature of hair colour. This forms the foundation for functional analysis linking genetic variation to phenotype and exploring the cellular interactions between melanocytes and other cells in the hair follicle.16.Study individuals were derived from the UK Biobank cohort that consists of 502,655 individuals aged between 40 and 69 years at recruitment, ascertained from 22 centres across the UK between 2006 and 2010. The study was approved by the National Research Ethics Committee, reference 11/NW/0382, and informed consent was obtained from all participants as part of the recruitment and assessment process. From these, we analysed 343,234 unrelated individuals, who reported their background as “British” and with similar ancestral backgrounds based on PCAχ2P value for the Hardy−Weinberg equilibrium >10−10 , a call rate >0.95 in unrelated white British individuals, a UKBB imputation score > 0.9 and an MAF >10−4. The number of SNPs analysed after quality control is 9,154,080.Variants included in the analysis were autosomal SNPs present in HRC imputation file, with a Self-reported hair colour (before greying occurred) for UK Biobank participants was selected from one of eight possible categories: “Blonde”, “Red”, “Light brown”, “Dark brown”, “Black”, “Other”, “Prefer not to answer”. The number of individuals in each category are: Red: 15,731, blonde: 39,397, light brown: 141,414, dark brown: 127,980, black: 14,526, other: 4186, Do not know/Prefer not to answer: 650. Individuals with missing data  were excluded from all the analysis. For the red hair colour against brown and black hair colour, the blonde individuals were removed  and self-reported red hair individuals were removed from the blonde against brown and black analysis . For the comparison of brown vs. black, light and dark brown individuals were combined and compared to black hair individuals .βAglead) from each signal of association [2].p ≤ 5×10−8), subsequent rounds of conditional analysis were performed, adding each new lead SNP as an extra conditioning SNP  additive model, adjusting for the first 15 axes of variation from the PCA and genotyping batch  with that allele combination. The calculation and plot were made using the .ped and .map plink files, and plotting was performed using ggplot2 in R.19 which takes into account the strength of association of the lead genetic variant and the LD structure in the fine-mapping interval to calculate the posterior probability that a particular genetic variant is the causal variant given that another variant is the lead variant. PICS calculates the conditional posterior probability P, that is the probability that SNP B is the causal variant given SNP A is the lead SNP. Under the assumption that effect sizes for non-causal SNPs are drawn from a Normal distribution, N, Farh et al. derive from empirical data the sample standard deviation, σs, and the expected mean association signal, μs, which scales linearly with the LD to the true causal SNP.P is the −log10P of association of the causal SNP for that locus, which is taken to be the lead SNP for that fine-mapping interval. The calculated posterior probabilities were used to construct 95% credible intervals, defining the resolution of fine-mapping over a given genomic interval.We implemented the probabilistic inference of causal SNPs (PICS)MC1R non-synonymous variants and red hair colour-associated loci was performed by comparing the following full  using the --lr command. Due to complex LD structure around the MC1R region it was necessary to remove variants in LD (r2 ≥ 0.1) with the target variant in each case. Additionally, SNP alleles that co-occur on the same haplotypes, but are in imperfect LD, may generate the false impression of interactions, Wood et al.38. suggest including the main effects as covariates to remove these false interactions. Therefore, in the SNPs where there was apparent epistasis in chromosome 16, we corrected for rs34357723 and the MC1R SNPs (Table Variants within 3.0 Mb of the index SNP (±1.5 Mb either direction) for each signal of association were selected for epistasis analysis. Explicit testing of interactions between ing full  logisticentclass1pt{minimaλ was selected using the most regularized model from a ten-fold cross validation, within one standard error of the minimum using the glmnet package in R39. The regularized effect estimates were used to create genetic scores for blonde hair colour, assuming an additive genetic model. Non-British  and related  individuals were taken as independent test for the blonde hair genetic scoring. The summed genetic score for each individual was calculated using Plink v1.9.In order to calculate the hair colour genetic score we performed least absolute shrinkage and selection operator (LASSO). A penalised logistic regression model was created for blonde colour, including all lead variants for blonde vs. black and brown hair colour. The parameter Bed format files were downloaded for epigenetic marks associated with activated or accessible chromatin from the Roadmap Epigenome and ENCODE project websites (October 2017). Files were downloaded for annotations in primary melanocytes, keratinocytes, and fibroblasts included in the Roadmap Epigenome Project. Annotations were H3K4me1, H3K4me3, H3K27ac and DNase I hypersensitive sites . Primary melanocyte, keratinocyte and other pigmentation cell type transcriptional start site (TSS ± 100 bp) data were downloaded from the FANTOM5 website (October 2017).42. Short sequencing reads were extracted using fastq-dump and assessed for quality using fastQC (see URLs). Reads were processed in order to remove adaptor contamination using trimmomatic and then aligned to GRCh37/hg19 using BWA-aln43. As there are no replicates in the ChIP-seq data, peaks were called with MACS244 with an FDR 1%. For each cell type and annotation where more than one sample existed, intervals were merged using Bedtools45 intersect intersect for two files and multiIntersectBed for >2 files per cell type. To test for genomic enrichment we used a method that tests for the local enrichment of lead variants and those in LD with genomic features by randomly shifting annotations to generate a null distribution; implemented in GoShifter32. LD values calculated from the 1000 Genomes phase 3 EUR reference panel were used in conjunction with the lead variant from each independent signal of association . GoShifter was run for 10,000 permutations and enrichment P values were adjusted for multiple testing using the Benjamini and Hochberg procedure implemented in the R function p.adjust with a 5% FDR.Micropthalmia-associated transcription factor is generally considered the master regulator of melanogenesis in melanocytes. Short read data were downloaded from SRA corresponding to MITF ChIP-seq data derived from primary melanocytes and the melanocytic cell line Hermes3A46 and MuTHER47 studies in tissues relevant to pigmentation . Summary statistics for genes that fell within 3 Mb intervals of the lead SNP of each association signal were extracted. We wished to test the explicit hypothesis that the same causal variants underlie each eQTL and our association signal for each hair colour (HA). Subsequently the null hypothesis is a compound null of no association for either trait (H01) and the combinations of overlapping but independent eQTL and trait associations (H02) and an association in one trait/eQTL but not the other (H03 and H04). A Bayesian testing framework that tests the set of hypotheses relating to those defined above (H01−4 and HA) was developed by Giambartolomei et al.48 and is implemented in the R package coloc. Subsequently, we report the posterior probability for each association signal-gene eQTL signal and the alternative hypothesis HA (PP4).To test for the potential of non-coding association signals to impact on gene regulation, we utilised eQTL summary statistics from the GTex15 report 141 significant SNPs in 123 different loci in a meta analysis of UKBiobank phase 1 and 23andme data. As the Hysi et al. analysis takes hair colour as a quantitative variable , we could not compare the results directly. To compare the results, we took their significant SNPs and then we took the lowest P value in our GWAS analyses  and calculated the overlap between their significant SNPs and ours. We also ran a new GWAS taking hair colour as a quantitative variable and compared our results to Hysi et al. Some of the SNPs reported in their analysis do not pass our QC, so they have been reported as missing SNPs (NA). Finally, we calculated the overlap with significant SNPs in our data for different distances  around SNPs reported significant in the Hysi et al. paper.Hysi et al.50 we calculated the genetic correlation between males and females for blonde and red hair coloursUK Biobank unrelated white British data consist in 184,929 females and 158,957 males. In females 12.7% have blonde hair and 5.2% have red hair, while in males 9.9% have blonde hair and 3.7% have red hair. We performed a GWAS for red vs. non-red  and blonde vs. non-blonde  for males and females separately. With SNPs significant in at least one of the GWAS results  we did a linear regression of the OR for each hair colour, to see the different effects of the SNPs in different sexes. Using LD ScoreLD score was also used to calculate SNP heritability for hair colour. To calculate the amount of heritability explained by the significant SNPs we removed these SNPs from the analysis and we calculated the heritability again, the change in heritability is the heritability explained by the SNPs we removed. In red hair individuals, we were also interested in the heritability explained by chromosome 16, so we removed the significant SNPs in chromosome 16 and calculated again the heritability of red hairhttps://www.gtexportal.org/Gtex: http://www.staff.ncl.ac.uk/richard.howey/cassi/Cassi: http://www.muther.ac.uk/Twins UK: https://github.com/ncbi/sra-toolsSRAtoolkit: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqQC: www.roadmapepigenomics.org/data/Roadmap Epigenomics Project: fantom.gsc.riken.jp/5/FANTOM5: Supplementary InformationPeer Review FileReporting Summary


Identifying maize inbred lines that are more efficient in nitrogen (N) use is an important strategy and a necessity in the context of environmental and economic impacts attributed to the excessive N fertilization. N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) are components of N-use efficiency (NUE). Despite the most maize breeding data have a multi-trait structure, they are often analyzed under a single-trait framework. We aimed to estimate the genetic parameters for NUpE and NUtE in contrasting N levels, in order to identify superior maize inbred lines, and to propose a Bayesian multi-trait multi-environment (MTME) model. Sixty-four tropical maize inbred lines were evaluated in two experiments: at high (HN) and low N (LN) levels. The MTME model was compared to single-trait multi-environment (STME) models. Based on deviance information criteria (DIC), both multi- and single-trait models revealed genotypes x environments (G x E) interaction. In the MTME model, NUpE was found to be weakly heritable with posterior modes of heritability of 0.016 and 0.023 under HN and LN, respectively. NUtE at HN was found to be highly heritable (0.490), whereas under LN condition it was moderately heritable (0.215). We adopted the MTME model, since combined analysis often presents more accurate breeding values than single models. Superior inbred lines for NUpE and NUtE were identified and this information can be used to plan crosses to obtain maize hybrids that have superior nitrogen use efficiency. Maize is the leading cereal crop in terms of production and, together with rice and wheat, it is one of the most important sources of the global population’s daily caloric requirement . Most ofNitrogen-use efficiency (NUE) is defined as grain yield per unit of N available in the soil and is composed of N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) . NUpE reThe selection of maize inbred lines that are superior for NUE and its components is complex, since they are controlled by many genes and are strongly affected by the environment. Several studies have shown that there is genetic variability for NUE in maize , 8, 9 atIn plant breeding, the genetic evaluation of multiple traits is relevant because superior varieties combine optimal attributes for several traits simultaneously. Despite the fact that data collected in plant breeding studies often present a multi-trait multi-environment structure, these data are rarely analyzed in a combined analysis . AlthougToward this orientation, we aimed to propose a multi-trait multi-environment Bayesian model to estimate genetic parameters for N-uptake efficiency and N-utilization efficiency under contrasting N levels in the soil as well as to perform comparisons with single trait models. We aimed also to identify superior maize inbred lines for N-uptake and N-utilization efficiencies.th article, of the third paragraph of the No. 9456 Brazilian Law of April 25th, 1997). The genetic sources used for our program to develop new inbred lines are conventional varieties. The 64 maize inbred lines were developed using a modified pedigree method in the same region where they were evaluated. Most of tropical hybrids used as source to develop the inbred lines studied in this work are recommend to tropical areas; consequently, they are adapted to the region where the inbred lines were studied. The CMS28 is a tropical maize population introduced in Brazil from International Maize and Wheat Improvement Center (CIMMYT) by the Brazilian Agricultural Research Corporation (EMBRAPA), Maize and Sorghum unit, in the 70’s [The genetic material consisted of 64 tropical maize inbred lines. These maize inbred lines represent a set of diverse germplasm from the maize breeding program of Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais state, Brazil. They were obtained from different sources of tropical maize germplasm: commercial tropical maize hybrids, maize populations and maize open-pollinated varieties OPV; . It is ithe 70’s . The Nitthe 70’s . The CMSthe 70’s . In addiThe 64 tropical maize inbred lines were evaluated in 2014 at UFV Experimental Station in Coimbra , located in southwest Minas Gerais state, Brazil. Maize inbred lines were evaluated in two independent experiments under contrasting levels of N: low nitrogen (LN) and high nitrogen (HN). In this context, there is a totally different environmental condition for plants, thus characterizing different environments, mainly because they are very contrasting nitrogen levels.2 (4 m long with 0.8 m row spacing and 0.2 m plant spacing). At the LN level, 30 kg ha-1 of N was applied. At the HN level, 180 kg ha-1 was applied. Trait management was the same for all N level experiments, employing standard agricultural practices.The design of each experiment was an 8 x 8 lattice square with two replications and two-row plots. The plot size was 6.4 m-1) was calculated at 145 g kg-1 moisture. At physiological maturity, five representative plants were harvested from each plot by cutting them close to the soil surface. All plant stover together with cobs (with kernels removed) at maturity were chopped and oven-dried to a constant weight at 70°C for 72 hours. The harvest ears were also oven-dried at 70°C for 72 hours. Grain and stover samples were milled using an analytical mill and analyzed for N according to the Kjeldahl method [Grain yield was recorded from all ears on the plot at physiological maturity. Ears were shelled, the grain weight and grain moisture percentage were recorded, and grain yield  in the aboveground biomass to total N in the soil (kg ha-1) and N-utilization efficiency was calculated as the ratio of grain yield (kg ha-1) to total N (kg ha-1) in the aboveground biomass.The components of NUE were calculated according to : N-uptakN-uptake efficiency and N-utilization efficiency were analyzed using single and multi-trait models via the Markov Chain Monte Carlo (MCMC) Bayesian approach. The idea was to compare: (i) the full model (considering the interaction between the genotypes and N levels) with the null model (not considering the interaction); (ii) the estimates of the genetic parameters from single- and multi-trait models at LN and HN levels.The multi-trait multi-environment model was given by:h represents high N; 1 represents NUpE and 2 represents NUtE; y is the vector of the phenotypic values of the traits (NUpE and NUtE); β is the vector of systematic effects of environment (N levels), β ~ N; r is the vector of the replication inside the environment effect, r ~ N; b is the vector of random effects of block inside replication inside environment, b ~ N; u is the vector of random effects of genotypes, u ~ N; ɛ is the vector of random residual effects, ε ~ N. X is the incidence matrix of environment (N level) effects, Z1 is the incidence matrix of replication inside the environment effect, Z2 is the incidence matrix of block inside replication inside environment effects and Z3 is the incidence matrix of genotype effects.Which can be rewritten as:h represents HN; 1 represents NUpE and 2 represents NUtE. We assumed that the variance-covariance matrices follow an inverted Wishart distribution, which was used as a priori to model the variance-covariance matrix [The (co)variance matrix estimates are given by:e matrix .The model was implemented in the “MCMCglmm” R package , 29 of RpD is the effective number of parameters in the model. Models with smaller DIC should be preferred to models with higher DIC.The full (considering the inbred lines x N levels interaction) models were compared with the null (not considering the interaction) models by the deviance information criterion (DIC) proposed by : DIC=D ranging from: h2 = 0.240–0.674 and h2 = 0.084–0.396, posterior modes: h2 = 0.490 and h2 = 0.215, respectively. The difference between mean, mode and median heritability estimates  under both N levels  . Althougquantitative trait loci (QTL) studies tend to confirm that variation in NUtE is greater than variation in NUpE at LN environments and the opposite occurs at HN environments. In the present study, the genetic variance was greater for NUpE in LN and greater for NUtE in HN . Relative variation indices that are greater than the unit suggest that genetic variation is more influential than residual variation. This was observed in this study for both traits but only in HN input, suggesting that there was a greater influence of the residual variation under low N level than high N level. Highest CVe and CVg were observed for NUpE in HN input, whereas lowest CVg was observed for NUtE in LN input .We have chosen to adopt the MTME model for the inbred lines selection since a combined analysis takes more data into consideration and consequently is often more accurate to predict breeding values than single models , 38. AccSuperior inbred lines in a given environment (N level) for a given trait were identified through the breeding values obtained in the MTME analysis. The superior inbred lines presented greater breeding values for the given trait. The inbred lines that stood out from the others for NUpE were, at the HN level, VML010, VML017, VML077, VML014, VML037, VML009, VML016, VML032, VML005, VML040, whereas at the LN level, VML017, VML051, VML016, VML002, VML033, VML019, VML022, VML188, VML048 and VML034 presented good performances compared to the other inbred lines. For NUtE, the most efficient maize inbred lines were VML081, VML022, L038, VML032, VML051, VML016, VML020, VML027, VML028 and VML013 for both N levels. Another observation that must be reported is that the VML016 inbred line was the only one that appeared in the top 10 list for both traits in the two considered N levels, possibly constituting an important source of genes for NUE traits.We demonstrated the feasibility of the proposed multi-trait multi-environment Bayesian model for plant breeding involving a set of genotypes that are evaluated for multiple traits across a range of environments. The accurate estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in maize breeding.NUpE was not genetically correlated to NUtE in HN and LN environments. The panel of inbred lines evaluated presents genetic variability, which is fundamental for the selection and improvement of traits of interest. This variability is particularly important in low nitrogen environments, which is the condition that most small farmers that grow maize in the tropics experience. Superior inbred lines were identified for each environment (N level), thus allowing to design selection strategies specifically for each evaluated environment.S1 Table(TXT)Click here for additional data file.