Aim of the present study was first to identify genetic variants associated with egg number (EN) in female broilers, second to describe the mode of their gene action (additive and/or dominant) and third to provide a list with implicated candidate genes for the trait. A number of 2586 female broilers genotyped with the high density (~ 600 k) SNP array and with records on EN  were used. Data were analyzed with application of additive and dominant multi-locus mixed models.BHLHE40 and CRTC1) to be involved in the ‘entrainment of circadian clock by photoperiod’ biological process. Gene prioritization analysis of the positional candidate genes identified 10 top ranked genes . Seven prioritized genes  have documented functional relevance to reproduction, while two more prioritized genes (ITPR1 and ELL) are reported to be related to egg quality in chickens.A number of 7 additive, 4 dominant and 6 additive plus dominant marker-trait significant associations were detected. A total number of 57 positional candidate genes were detected within 50 kb downstream and upstream flanking regions of the 17 significant markers. Functional enrichment analysis pinpointed two genes (Present results have shown that detailed exploration of phenotype-marker associations can disclose the mode of action of genetic variants and help in identifying causative genes associated with reproductive traits in the species. The breeding objective used for selection in broilers is balanced between reproduction, welfare and production traits . Modern As a typical reproductive trait, EN presents low to medium additive heritability estimates. In broiler hens, pedigree-based additive heritability for the trait has been estimated as high as 0.32, while respective estimates are in the range from 0.13 to 0.36 when using genomic relationship matrices . The conHigh-density SNP (single nucleotide polymorphism) genotyping arrays have greatly facilitated the detection of candidate causal variants in genome-wide association studies (GWAS) for various traits related to egg production and egg quality. Most GWAS have, so far, focused on the detection of additive SNPs for egg production –7 and egDriven from the scarcity of published reports for broiler breeders, we elaborated the present study with the primary aim to detect genetic variants impacting on EN. Next, we sought to describe the mode of gene action of the significant genetic variants and finally attempted to provide a list with most likely candidate genes for the trait under investigation. Current findings are expected to contribute to a better understanding of the genetic mechanism(s) underlying the EN phenotype in the species.p-values along with estimates of the genomic inflation factors (λ = 0.95 and 0.97 for the respective additive and dominant genetic model) were indicative of no systematic bias due to population structure or analytical approach. Profiles of the SNP p-values (expressed as -log10) for the additive and dominant genetic model are presented in form of circular Manhattan plots in Fig. p < 2.09E-07) using the Bonferroni correction method. Nevertheless, using the same correction method, a total number of 17 SNPs reached chromosome-wide significance across four autosomes  (Table rs313298834) was detected on GGA12 (threshold p = 0.05/7475 = 6.68896E-06), one (rs314011910) on GGA22 (threshold p = 0.05/1870 = 2.6738E-05), one (rs313045367) on GGA26 (threshold p = 0.05/3013 = 1.65948E-05) and 14 on GGA28 (threshold p = 0.05/2268 = 2.20459E-05). As observed in Table rs313045367) resided on GGA26 while 6 were located on GGA28. One dominant SNP (rs314011910) was detected on GGA22 while 3 dominant SNPs  were located on GGA28. Of markers displaying both gene actions, one marker (rs313298834) resided on GGA12 and 5 were located on GGA28 (Table 2) levels. A detailed view of these SNPs along with LD (r2) levels between markers is depicted in Fig. 2 > 0.70) formed by marker pairs rs15250929-rs16212041 and rs314418757-rs318126353  to 0.85% (rs314418757) for the additive markers and from 0.69% (rs314011910) to 0.84%  for the dominant markers  for the trait. The Q-Q plots  and rs314011910 (GGA22) were detected as additive/dominant or dominant markers, respectively. Estimates of a, d and |d/a| for the four SNPs  are shown in Table rs16212040, rs313298834 and rs318126353) to 1.1 . Notably, no marker was associated with over-dominance. We furthermore sought to quantify the joint effect of the combined genotype of the two markers (rs16212040 and rs318126353) retained by LASSO on GGA28 by estimating LS means for the combined genotypes (Table n = 9) and ABAA  that could not be attributed to additive allelic effects of individual markers. Specifically, the highest EN estimate for AABB is suggestive of additive-by-additive (AABB) interaction (epistasis) while that of ABAA of additive-by-dominance (ABAA) epistasis. However, due to limited number of observations, especially for the AABB combined genotype (n = 9), current results should be treated with caution.Application of the LASSO method on the 14 co-localized SNPs on GGA28 resulted in selection of two markers i.e. n = 16) were detected around dominant rs318126353 (GGA28) while the minimum number of genes (n = 6) were identified around 5 SNPs . Four additive SNPs  were located within genes ARL8A (GGA26), UPF1 (GGA28), CRTC1 (GGA28) and TMEM59L (GGA28), while 2 more markers (rs313312915 and rs14307369) resided in gene ELL (GGA28). Three dominant SNPs  lied within genes DDX49, KXD1 PGPEP1 (GGA28). Of additive/dominant SNPs, 3 co-localized markers  were detected within COMP (GGA28) and one more (rs314418757) within CRTC1 (GGA28). As 14 significant markers resided in nearby locations on GGA28, 26 out of the 36 positional candidate genes were associated with more than one marker  were associated with gene CRTC1.A total number of 57 positional candidate genes (i.e. 43 annotated and 14 LOC genes) were identified within the searched genomic regions  enriched BP with two participating genes (CRTC1 and BHLHE40) (results not shown).A total number of 50 out of the 57 positional candidate genes were recognized by the DAVID tool and used for functional enrichment analysis. The latter analysis revealed the ‘entrainment of circadian clock by photoperiod’ (GO:0043153) as the only significantly  according to the semantic annotations imposed. The majority (n = 7) of the prioritized genes resided on GGA28, followed by two genes (BHLHE40 and ITPR1) on GGA12 and one (ELF3) on GGA26. On GGA28, the first ranked gene was GDF15, followed by JUND, GDF3, COMP, ELL, CRLF1 and IFI30. Notably, two highly ranked genes i.e. GDF15 (1st) and GDF3 (4th) belong to the transforming growth factor beta (TGF-β) superfamily. The two genes (BHLHE40 and CRTC1) that participated in GO:0043153 ‘entrainment of circadian clock by photoperiod’ were also prioritized and ranked 2nd and 13th, respectively.Results of PA are displayed on Table d| = |a| meaning equal genotypic values for the minor homozygous (μΑΑ) and the minor heterozygous genοtypes (μΑΒ) that both differ from the major homozygous genotypic mean (μΒΒ). This was exactly the case for marker rs314011910 that was detected only as dominant variant. But what happens in the case of partial dominance (0 < |d| < |a|)? In such cases  meaning that apart from the dominant model, a linear relationship between the genotypic mean values and the number of copies of the minor allele i.e. the additive genetic model may also be applicable. For an excellent interpretation of how least squares regression performs in GWAS in additive and dominant models we refer to Huang and Mackay [d| > |a|). In the latter case, μΑB > μΑA and μΑB > μBB implying the need of using a different model parameterization by coding the heterozygous genotypes as 1 and the two homozygous genotypes as 0. Due to model parameterization difficulties we could not explore the validity of an over-dominant genetic model here and this may be the reason why no marker has been associated with over-dominance in the current study.This is the first GWAS enlisting a significant number of animals n ~ 2600) and reporting on genetic variants implicated in the genetic control of EN in broiler breeders. Present results have demonstrated the need to thoroughly exploring the applicability of all possible genetic models when conducting a GWAS. This is particularly important when analyzing quantitative traits such as EN where not only additive but also non-additive e.g. dominant gene action of the causative loci may be fairly anticipated . In line600 and rWhile estimates of genetic effects (additive and/or dominant) are expected unbiased for a few, independent variants, this may not be the case for multiple, highly correlated variants residing on the same haplotype block(s) since the effect(s) may be ‘shared’ by many SNPs. For this reason, it is important to have a parsimonious model involving limited number of regressors (SNPs). To this end, application of the LASSO technique has proved particularly helpful as it has selected only two markers, each one residing in the two LD blocks on GGA28. Then, the next step was to explore whether the two variants interact and, if yes, to portray the exact type of interaction. This exploration has delivered interesting results since non-additive genetic interaction(s) between the two variants could also be detected. Although these findings are based on limited number of observations, they are indicative of potential importance of epistasis in the inheritance of the trait.GDF15, BHLHE40, JUND, GDF3, COMP, ITPR1, ELF3, ELL, CRLF1 and IFI30) with interesting biological properties were highlighted. Genes GDF15  and GDF3  serve as good examples here since they both belong to the TGF-β superfamily genes. In rodents and humans, many factors belonging to the TGF-β superfamily are expressed by ovarian somatic cells and oocytes in a developmental manner and function as intraovarian regulators of folliculogenesis [GDF15 is involved in placentation [GDF3 might affect folliculogenesis by inhibiting the bone morphogenetic protein cytokines [GDF3  is expressed at the early blastoderm stages of embryonic development [GDF9 is expressed in the ovary and functions on hen granulosa cell proliferation as in mammals [BHLHE40 (basic helix-loop-helix family member e40) in the mouse ovary leads to a circadian gating of cellular processes in the ovary as well as in the hypothalamus during ovulation [JUND  is important for maturation of human ovarian cells [COMP (cartilage oligomeric matrix protein) is involved in ovarian follicle development in mice [COMP gene affect chondrogenesis in chickens [ITPR1  is involved in the Ca2+ transport for supplying eggshell mineral precursors in chicken uterus [ELF3 (E74 like ETS transcription factor 3) has been related to the development of chicken oviducts [ELL (elongation factor for RNA polymerase II) has been associated with yolk weight [CRLF1 (cytokine receptor like factor 1) and IFI30  had no documented involvement in reproduction. Such a finding underscores the limitations of in silico PA. In almost every guilt-by-association (GBA)-based prioritization tool, functional annotations of genes refer mainly to human and mouse PPINs (protein-protein interaction networks) [Another intriguing problem that needed to be addressed in the present study was as how to narrow down the list with the 43 positional candidate genes. This post-GWAS step presents an important problem, because the experimental validation of the true causal genetic variants requires considerable costs, effort and time. To address this issue, we performed in silico prioritization analysis (PA) using explicitly two semantic annotations: GO: BP and co-expression. This approach was based on the assumption that co-expressed genes tend to be involved in the same biological process and that expression of functionally related genes should vary concordantly across the various tissues. Typically, gene co-expression networks do not provide information about causality, but they can serve as first proof of their involvement in a particular biological process  and can entation , while Gytokines . In chicelopment  while an mammals . Expressvulation . JUND   neglectietworks)  such as etworks) .BHLHE40 and CRTC1 (CREB regulated transcription coactivator 1). Both genes were enriched in the BP of ‘entrainment of circadian clock by photoperiod’ raising the intriguing question as what might be the exact mechanism of their implication in egg production. To answer the question, first we have to provide a short description of the molecular mechanism underlying circadian rhythms (CR). CR are controlled by a pacemaker located within the suprachiasmatic nucleus of the hypothalamus that is entrained to the external light–dark cycle via light input from the retina conveyed via the retinohypothalamic tract [Clock and Bmal1 act as positive components, whereas those of the Per and Cry genes act as negative ones [BHLHE40  acts as a suppressor of Clock and Bmal1 genes [CRTC1 to induce expression of Per1 and Sik1 [Of particular interest in this study were genes ic tract . In hensic tract . At the ive ones . With rel1 genes  while anand Sik1 . As the and Sik1 .GDF15, BHLHE40, JUND, GDF3, COMP, ELF3 and CRTC1) of the prioritized genes were associated with reproductive traits while 2 (ITPR1 and ELL) were related to egg quality traits. From the above, only 3 genes i.e. COMP, ELL and CRTC1 included significant SNPs. We finally, compared our candidate genes list  while the second ARL8A (ADP ribosylation factor like GTPase 8A) has been associated with eggshell thickness and eggshell formation [In total, 7 . Finally, a total of 2586 samples and 239,267 SNPs across 28 autosomes (GGA1–28) were retained for further analyses. QC was performed using the SNP & Variation Suite software (version 8.8.3).Genotypic and phenotypic records for 2992 female broiler breeders from a purebred line were provided by Aviagen Ltd. EN records (28 to 50 weeks of age) ranged from 26 to 196 eggs per hen with an average of 132.4 (SD = 29.8). The 600 k Affymetrix® Axiom® high density genotyping array  was emplStepwise regression with forward inclusion and backward elimination of multiple markers (SNPs)  was apply is the n × 1 vector of phenotypic values of EN for n female broilers, X is the n × 53 matrix of fixed effects: hatch (36 classes) and mating group (17 classes), β is the 53 × 1 vector of corresponding coefficients of fixed effects, a is the fixed effect for the minor allele of the candidate SNP to be tested for association, w is the incidence vector relating observations to SNP effects with elements coded as 0 for the major homozygous genotype, 1 for the heterozygote genotype and 2 for the minor homozygous genotype (additive genetic model) and 0 for the major homozygous genotype and 1 for the heterozygous and minor homozygous genotypes (dominant genetic model). Z is the incidence matrix relating observations to the polygenic random effects, u is the vector of polygenic random effects and e is the vector of random residuals.Specifically, the following mixed model was used for EN data:ij and xik represent the number  of the minor allele of the ith SNP of the jth and kth individuals, and pi is the frequency of the minor allele [The random effects were assumed to be normally distributed with zero means and the following (co)variance structure:rix (GRM ) with elr allele .p-values were subsequently corrected for multiple comparisons using the Bonferroni correction method. A SNP was considered as significant at the genome-wide level when its p-value was lower than the threshold value 2.09E-07  while a chromosome-wide significant SNP had a p-value lower than 0.05/N, where N is the number of SNPs on a given chromosome. All analyses were performed using the SNP & Variation Suite software (version 8.8.3). SNP positions were based on GRCg6a assembly . SNP p-07 0.05/2,267 whillus/104/ ).λ) were used to assess potential systematic bias arising from population structure or the analytical approach [λ was performed using the SNP & Variation Suite (version 8.8.3).To characterize the extent to which the observed distribution of the test statistic followed the expected (null) distribution, quantile-quantile (Q-Q) plots were used. These plots in combination with estimates of the genomic inflation factor  was also calculated as follows:mrssh0 is the Mahalonobis root sum of squares (mrss) for the null hypothesis and mrssk is the same for marker k. All above estimations were performed using the SNP & Variation Suite software (version 8.8.3).The proportion of variance explained by a SNP When multiple markers were present in a specific genomic region, a variable selection method i.e. the Least Absolute Shrinkage and Selection Operator (LASSO)  as implep-values because of multiple means comparisons. Results of the mixed model analysis are presented as LS means (μ) with standard errors (SE). Additive allelic effect (a) was defined as half the difference between LS means of the two homozygous genotypes, using the minor homozygous genotypes as reference. Dominance deviation (d) was the heterozygous genotype LS mean minus the average of the two homozygous genotype LS means. Finally, degree of dominance was determined as |d/a|, where additive = 0–0.20, partial dominance = 0.21–0.80, complete dominance = 0.81–1.20 and over-dominance> 1.20 [Significant SNPs associated with dominant or dominant and additive gene action(s) were further analysed toward estimation of additive allelic effects, dominance deviation and degree of dominance. This analysis was based on estimates of genotype least squares (LS) means by application of a mixed model to the EN data fitting hatch, mating group and the marker as fixed effects and the animal as a random effect. Degrees of freedom were estimated using the Satterthwaite method while the Tukey-Kramer method was used to adjust the ce> 1.20 , 44. Thihttps://www.ncbi.nlm.nih.gov/gene/ [GRCg6a assembly  and the002315.6 , https:/lus/104/ ). Subseqhttps://david.ncifcrf.gov/, version 6.8) [Gallus gallus species for the input gene list and as genome background. During enrichment analysis, the following settings were used: EASE score   and the  p-value ) cutoff https://toppgene.cchmc.org/prioritization.jsp [https://www.ncbi.nlm.nih.gov/gene/ [Gallus gallus. Candidate gene prioritization is based on fuzzy functional similarity measures between any two genes and specific semantic annotations imposed. In our study two semantic annotations: ‘GO: Biological Process’ and ‘Coexpression’ were used. A p-value for each annotation of a test gene was derived by random sampling of 5000 genes from the whole genome. The partial p-values were finally combined into an overall p-value using the probability density function. For gene prioritization, there were 30 training genes (ZNF764L was omitted) and 43 test genes . Not all of the 57 positional candidate genes were included in the analysis because the human homologs could not be found for all of them, especially for LOC genes (n = 14). Genes with an overall p-value lower than 0.05 were considered as prioritized.Gene prioritization analysis (PA) of the positional candidate genes followed, using the ToppGene portal (tion.jsp ). PA wasov/gene/ ) using tAdditional file 1: Supplementary Fig. 1. Quantile- quantile (Q-Q) plots of the additive (top) and dominant (bottom) SNP effects for EN. Blue dots denote the −log10 obtained from the additive (λ = 0.95) and dominant (λ = 0.97) genetic models and the red lines represent the expected values for the null hypothesis under no association. Q-Q plots were constructed with the qqman package [http://www.r-project.org/). package  in R (htAdditional file 2: Supplementary Table 1. Positional candidate genes for EN.Additional file 3: Supplementary Table 2. Genes obtained by previous GWASs for egg and reproductive traits in Gallus gallus.Additional file 4: Supplementary Table 3. List of training genes retrieved by the NCBI database for the ‘reproduction’ and ‘egg production’ queried terms in Gallus gallus.


A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089–0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations.The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12 Genomic prediction is a new generation breeding technology, and it has been widely implemented in animal and plant breeding . GenomicThe choice of marker genotyping platform is a key factor affecting the accuracy of genomic estimated breeding values (GEBV) . Single RRGS can produce a large number of genomic markers at a low price and thus be used as an alternative genotyping platform, but the genotype quality of the markers is lower than that obtained with SNP chips . In animThere are many local broiler poultry breeds with unique characteristics around the world, such as Chinese yellow feather chickens and French Label Rouge chickens. Compared to SNP chips, a sequencing approach can obtain genetic variations specific to local breeds and, thus, may achieve higher accuracy than SNP chips.The accuracy of genomic prediction does not increase dramatically as the number of markers increases exponentially . HeidariThe aim of this study was to: (1) propose a novel method to screen markers for genomic prediction, and (2) compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers.The broiler population used in the current study was established by crossing the “High Quality chicken Line A” (HQLA) with the Huiyang Beard chicken (HB). The HQLth week (BW) and the feed conversion ratio (FCR). Body weight and feed intake were measured during the period from the beginning of the 7th to the end of the 12th week (42 d). FCR was calculated as the ratio of average daily feed intake to average daily gain, as described in c), instead of original observations (y), were used as response variables to calculate the difference between phenotypes of the two homozygous genotypes and to predict breeding values using SNP markers. The reason for using yc as response variables was to reduce noise by removing fixed effects which could be estimated much more accurately using a larger dataset, rather than using only genotyped animals with the two genotyping platforms. The fixed effects were estimated using linear least squares regression including sex (two levels) and batch (six levels), and yc = y – sex effect – batch effect.As shown in In this study, we provide a new method, the pre-marker-selection (PMS) method, to screen informative markers for genomic prediction based on the difference between phenotypes of the two homozygous genotypes at the marker with the data of the reference population, and the marks which have no homozygote or only have one homozygote will be deleted. The model is1A1 and A2A2; 1A1 and A2A2, respectively.where d is absolute value of the difference between phenotypes of the two homozygous genotypes Ad′ = d/max (d). After transformation, d ′ was in the interval . To explore the suitable cutoff value with PMS method, four different d ′ value cutoffs  were set to prune markers of genome data, the peak value of genomic prediction accuracy were on the cutoff value of 0.05, the summary of this preselection is presented in d′ greater than 0.05 were retained in leave-one-out cross-validation method.To generalize the PMS method, a transformation was applied, p values from GWAS results with the data of the reference population. The GWAS model isIn this study, genome-wide association studies (GWAS) using single marker regression was also used as a control method to preselect marker. The markers were selected based on the y is the vector of corrected phenotypic values of body weight at the 12th week and FCR, μ is the intercept, q is the effect of the marker in the model, which is treated as a fixed regression of observation on genotype, x is a vector containing genotypes of the marker with 0 for A1A1, 1 for A1A2 and 2 for A2A2, and ε is a vector of random deviates, which is assumed that where p values  were set to prune markers of genome data, the peak value of genomic prediction were on the cutoff value of 0.01, the summary of this preselection is presented in p value less than 0.01 were retained in leave-one-out cross-validation method.To explore the suitable cutoff value with GWAS method, three different cutoff In the current study, breeding values were estimated using a genomic best linear unbiased prediction model (GBLUP). The GBLUP model isy and μ are the same as above, g is the vector of genomic breeding values to be estimated, Z is the incidence matrix of g, and e is the vector of random residuals. It is assumed that G is the additive genomic relationship matrix based on SNP markers , the coefficients of the ith column in the M matrix are (0–2pi) for genotype A1A1, (1–2pi) for A1A2, and (2–2pi) for A2A2, where pi is the allele frequency of A2 at locus i, and where the definitions of  markers , G=MM′/Σc), the differences between the correlations were analysed by a bootstrapped paired t-test . The variance and covariance components were estimated using restricted maximum likelihood (REML) based on the mixed function of “mixedFunction.R”, and leave-one-out cross-validations were performed with “mixedCV.R”. The R codes “mixedFunction.R” and “mixedCV.R” can be downloaded from the website in (https://www.r-project.org/).The bsite in . The booAs shown in All 395 individuals were used for assessing cutoff values in preselection of markers. As shown in Using SLAF-seq markers preselected by PMS method with best cutoff values, the number of SNPs was reduced from 121,132 to 13,423 for BW and 10,976 for FCR, respectively. As shown in Using the SNP chip markers preselected by with PMS method with the best cutoff values, the number of markers was reduced from 46,690 to 15,748 and 11,506 for BW and FCR at the peak value of genomic prediction, respectively. As shown in Without preselection of markers, the estimates of heritability for BW were 0.703 ± 0.087 using SLAF-seq and 0.634 ± 0.076 using the SNP chip. The estimates of heritability for FCR were 0.265 ± 0.099 and 0.266 ± 0.093, respectively.As shown in With the PMS method, the accuracy of genomic prediction for the two traits was improved, regardless of whether SLAF-seq or the SNP chip was used. When using SLAF-seq, the accuracy of genomic prediction was increased by 0.162 for BW and 0.250 for FCR, and the gains were significant (P < 0.05). Using the SNP chip, the accuracy of genomic prediction was increased by 0.089 for BW and 0.190 for FCR and the gains were also significant (P < 0.05). In addition, the accuracy of genomic prediction using SLAF-seq was higher than that using the SNP-chip by 0.066 for BW and 0.077 for FCR. As shown in As shown in In this study, the genomic prediction of growth and feed efficiency traits in a small broiler chicken population was compared between high-throughput sequencing and SNP chip platforms. The results showed that when markers were not screened, the use of high-throughput sequencing data did not result in a higher accuracy than the use of chip data. Our method for marker screening, improved the accuracy of genomic prediction for both genotyping platforms, and high-throughput sequencing achieved higher accuracy for both traits.With the rapid decline in the price of high-throughput sequencing, its application in genomic selection has received a high level of attention . MeuwissTo improve the accuracy of genomic prediction, some previous studies tried to add causative mutations to chip data . Many stp value of GWAS as a control method, and the results showed that using preselected variants resulted in almost no increase for most traits and even increased the bias of the predictions , which is less than 1/2 the size of the genomes of cattle (2716 Mb) and pigs (2548 Mb), which means that chickens can achieve the same density of genome coverage at a lower cost. In addition, sequencing technology can flexibly select the depth of sequencing for a single sample, which can further reduce the cost of genotyping.The cost of genotyping is an important factor limiting its application in poultry breeding. With the continuous development of sequencing technology, the reduced price of sequencing may have advantages in regard to the cost of genotyping. De Donato et al. showed that the cost of sequencing data for the same number of markers is approximately 1/3 that of the SNP chip . Among tLocal breeds usually have characteristic traits that are preferred by local people, allow the birds to adapt well to the local environment, and usually exist in the form of small populations with a small effective population size. Druet et al. showed that the accuracy of genomic prediction depends largely on the coverage of key genes affecting target traits by genotyping platforms . HoweverThe results from this study indicate that with our PMS marker screening method, the accuracy of genomic prediction obtained using high-throughput sequencing, such as SLAF-seq, is higher than the accuracy obtained using SNP chips in local broiler populations. With accurate prediction and a low cost, the PMS method is a promising method for the use of high-throughput sequencing data for genomic prediction in breeding programmes of local broiler populations.Genotype data and trait data for the chickens used in this study are not publicly available, but are available from the corresponding author upon reasonable request.This study was approved by the Animal Care Committee of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences (No. GAAS-IAS2009-73).TL, CL, and HQ conceived and designed the experiments. TL, CL, JM, YW, DS, GS, and HQ discussed and interpreted the results. TL drafted the manuscript. CL, JM, YW, DS, GS, and HQ revised the manuscript. All authors read and approved the final manuscript.This work was supported by the Earmarked Fund for Modern Agro-Industry Technology Research System (CARS-41), the Science and Technology Program of Guangdong  and Key-Area Research and Development Program of Guangdong Province (2018B020203001). The funders had no role in study design, data collection and analysis.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Triticum aestivum) cultivars. We used stringent criteria to identify identical-by-state haplotypes and distinguish these from near-identical sequences (~99.95% identity). We showed that each cultivar shares ~59 % of its genome with other sequenced cultivars and we detected the presence of extended haplotype blocks containing hundreds to thousands of genes across all wheat chromosomes. We found that genic sequence alone was insufficient to fully differentiate between haplotypes, as were commonly used array-based genotyping chips due to their gene centric design. We successfully used this approach for focused discovery of novel haplotypes from a landrace collection and documented their potential for trait improvement in modern bread wheat. This study provides a framework for defining and exploiting haplotypes to increase the efficiency and precision of wheat breeding towards optimising the agronomic performance of this crucial crop.Crop productivity must increase at unprecedented rates to meet the needs of the growing worldwide population. Exploiting natural variation for the genetic improvement of crops plays a central role in increasing productivity. Although current genomic technologies can be used for high-throughput identification of genetic variation, methods for efficiently exploiting this genetic potential in a targeted, systematic manner are lacking. Here, we developed a haplotype-based approach to identify genetic diversity for crop improvement using genome assemblies from 15 bread wheat ( Brinton, Uauy and colleagues utilize genomic data from the 10+ Wheat Genome Project to develop a useful tool for studying and generating new wheat cultivars. This framework uses advanced exploitation of wheat haplotypes to bring newfound precision and efficiency to wheat breeding. Most of these traits are governed by genomic regions defined by underlying genetic variation . These genomic regions are often co-inherited as blocks of variation, or haplotypes, which are the effective units of selection by breeders. Most breeders currently use one or a few SNP markers to tag these haplotypes and introduce novel traits from diverse germplasm into modern cultivars3. Although valuable, these markers are often not causal but are simply associated with the trait of interest, providing limited information about the surrounding sequences. This can result in the selection of false-positive and false-negative individuals, which limits the efficient use of genetic diversity within breeding programmes4. Genomics provides a powerful means to define these haplotypes more precisely and implement targeted approaches to identify and exploit novel genetic variation efficiently to enhance crop performance. This is especially relevant given that the genetic diversity of many crops, including self-pollinating hexaploid bread wheat, has been severely constrained during the course of domestication and subsequent pure-line breeding6.Crop breeding involves assembling desired combinations of traits to generate improved cultivars7. We used chromosome-scale genome assemblies corresponding to 9 wheat lines 7 and the Chinese Spring RefSev1.0 assembly8, alongside 5 scaffold-level assemblies corresponding to cultivars Cadenza, Claire, Paragon, Robigus and Weebill7. Thirteen of these 15 lines are considered cultivars (cultivated varieties of wheat), whereas ArinaLrFor is a line derived from cultivar Arina and Chinese Spring is a landrace collected in the early 1900s from China. To avoid multiple designations, for the purposes of this study we will refer to all 15 lines as cultivars.Here, we defined haplotype blocks in hexaploid bread wheat using genome assemblies of cultivars representing modern-day diversity across wheat breeding programmes9. For each alignment, we calculated the percentage sequence identity *100) and plotted these values across the chromosome  with ≥99.99% median sequence identity, stitching together adjacent bins exceeding the ≥99.99% threshold  to account for genotyping errors, while still grouping highly similar lines into the same haplotype group. Our results provide evidence that this level of divergence is not sufficiently stringent to accurately differentiate haplotypes in wheat and possibly other crops. However, the differentiation of haplotypes (median ≥ 99.99%) and ‘near-identical’ sequences (median < 99.99%) may not be relevant for breeding. To investigate this potential issue, we examined the 300 Kbp interval surrounding the two semi-dwarfing Reduced Height genes (RHT-B1 and RHT-D1), which were the genetic basis for the Green Revolution in wheat14. We identified five RHT-B1 and four RHT-D1 haplotypes in the 15 sequenced cultivars. The Green Revolution RHT mutant haplotypes had ~99.96% sequence identity with all wild-type haplotypes, with alignments extending across >99% of the 300 Kbp region  and are based on linkage disequilibrium or a fixed window size of adjacent markers7 (see “Methods” for details). To select parameters for defining haplotype blocks using gene-based BLAST alignments we performed a precision-recall analysis using the NUCmer-defined haplotypes as ground truth, testing a range of sliding window sizes  and flanking sequence surrounding genes   than distal blocks . These values illustrate that, regardless of chromosomal position, haplotype blocks fix sequence across regions containing hundreds if not thousands of genes, which is particularly relevant considering that these blocks are the effective units that are shuffled and assembled during breeding.Using the haplotypes called with 5-Mbp bins, we found that the median haplotype block length in wheat was 9.34 Mbp, containing a median of 196 genes. Blocks in centromeric regions (C) tended to be larger than those in distal regions We compared the extent to which the 15 cultivars shared common haplotypes and observed that on average, 59.3 ± 4.6% of the genome was shared with at least one other cultivar Fig. . This vaRHT-B1b haplotype on chromosome 4B . Similarly, the identification of these regions will allow the focused discovery of alternative haplotypes and the implementation of breeding strategies for the targeted introduction of genetic diversity with improved or novel functions.We analysed the physical positions of the haplotype blocks across all cultivars to identify conserved regions that constitute targets for introgression of genetic variation. We identified regions that we defined as ‘highly conserved’ based on the sharing of a common haplotype with five or more other cultivars   originating from different platforms: 15K iSelect23 and 35K Axiom breeders’ array24 (SNPs derived from RNA-Seq), and exome-capture data11 , even though other haplotypes are present in the germplasm pool . Likewise, the delayed final maturity associated with H3 (ref. 22) is likely to be selected favourably in environments with extended growing seasons (e.g. UK) compared to those with late-season heat .Despite the limited resolution of breeders’ markers, we used the available array and exome capture data to conclude that H3 dominates in modern European germplasm and has been selected over time  to 40 (31 Watkins-specific) Fig. . All happ < 0.05)  x Watkins bi-parental populations. Of the nine Watkins-specific haplotypes that were represented in bi-parental populations, we identified three associated with significantly increased grain weight  Fig. . As thes28, to re-examine historic breeding trial data in a retrospective manner1, and to intentionally assemble optimised haplotype combinations that have directly predictable performance. This approach will be bolstered by gene editing to generate mutations within specific haplotypes18 and possibly targeted recombination to specified chromosomal regions29. To further facilitate the use of haplotypes in breeding and research, we developed an interactive visualisation platform (http://www.crop-haplotypes.com/) to investigate haplotype blocks in wheat. Our haplotype analyses provide a framework for exploiting natural diversity in a targeted, systematic manner with the aim to increase the precision of wheat breeding and optimise the agronomic performance of a key global crop.The adoption of haplotype-led breeding will enable breeders to discover novel haplotypes and follow their introduction and phenotypic performance, as demonstrated for chromosome 6A. Defining haplotypes will promote more precise parental selection to maximise genetic gains within breeding programmes and enhance the ability to detect recombination events that partition and re-arrange haplotypes. This will allow breeders to evaluate local additive and epistatic effects30.To call haplotype blocks we used a combination of whole chromosome level NUCmer alignments and gene-based pairwise alignments using BLASTn31. We performed pairwise alignments for each chromosome using the NUCmer program from MUMmer-3.2332 including the–mum option to use anchor matches that are unique in both the reference and query (this helps reduce the number of repeat induced matches). We filtered the raw delta files using the delta-filter command including the options -l 20,000 , -r and -q . We used an -l of 20,000 to exclude shorter alignments between non-syntenic retrotransposons which have a median size of 9584 bp in wheat9. All reciprocal pairwise alignments between chromosome-level assemblies were performed and analysed. For alignments between chromosome-level and scaffold-level assemblies, we first filtered scaffolds to include only those that contained at least one RefSeqv1.1 gene model projection from the corresponding chromosome7. After scaffold filtering, we performed pairwise alignments and subsequent filtering as described above; in all cases the chromosome-level assembly was used as the reference and the scaffolds as the query. Pairwise alignments between scaffold-level assemblies were not conducted using NUCmer . To call haplotype blocks from the NUCmer alignments, we first calculated the percentage identity for each alignment and then binned alignments by position across the chromosome in 5-Mbp bins. It is important to note that the filtering for alignment size of ≥ 20,000 bp meant that the full 5-Mbp of sequence is not always contained within alignments. However, upon manual inspection of several regions we found that gaps in alignment sets were due to exclusion of smaller alignments between sequences disrupted by Ns and not shorter alignments of lower identity than the surrounding sequence. The median amount of sequence aligned in the 5-Mbp bins of chromosome-scale assemblies was 3,848,154 bp (i.e. 76.9% of bin breadth). Bins with a median sequence identity ≥99.99% were considered to be identical-by-state and therefore a shared haplotype. Whilst we would expect regions of identical-by-state to share 100% sequence similarity, the ≥99.99% allows for technical issues including the presence of Ns and sequencing errors, such as those that occur in areas of lower sequence complexity , we assigned each scaffold to a chromosome based on the origin of the projected genes. Then, we kept genes consistent with the expected chromosome. Finally, we filtered out genes with more than one projection in the expected chromosome. The script was written as a Jupyter notebook33 using BioRuby 2.0.1 (ref. 34), Daru 0.2.2 (ref. 35).BLASTn alignments: to complement the NUCmer alignments and allow for direct pairwise comparisons between scaffold-level assemblies, we performed pairwise BLASTn alignments of projected genes (GFF annotation RefSeqv1.1)36 script that takes the filtered genes and performed pairwise BLASTn alignments between all cultivars for each gene. From the XML output (-outfmt ‘5’), we extracted the largest HSP in the alignment, including the alignment length, percentage identity and number of Ns. We filtered out alignments containing Ns in the aligned sequence. For each pairwise comparison, the gene-based alignments were ordered based on the Chinese Spring RefSeqv1.0 physical position. We then called haplotype blocks using a sliding window of 25 consecutive genes. For each window of 25 genes, we ordered the gene-based alignments by percentage identity and removed the 10% of alignments with the lowest percentage identity (i.e. 3 lowest). We then calculated the mean of the percentage identity of the remaining alignments. If the mean was equal to 100%, we considered the window to be identical-by-state i.e. a shared haplotype. After calling blocks using windows based on the RefSeqv1.1 gene order, we converted the coordinates of each block to the corresponding assembly using the method described below (section “coordinate conversion between assemblies”). In addition to ± 2000 bp, we also performed pairwise alignments for the coding sequence (CDS), the gene including the introns (± 0 bp), ± 1000 bp and ± 5000kbp. We selected the parameters of ± 2000 bp flanking sequence and a 25 gene window based on a precision/recall analysis  and a range of window sizes . We then called blocks using all combinations of these parameters for a subset of chromosomes . For each chromosome from each pairwise comparison of cultivars, we looked for overlaps between the previously called NUCmer blocks and the BLAST blocks called using the different combinations of parameters and calculated precision/recall scores .To visualise the haplotypes a Ruby on RailsIn addition to the visualisation code, the database was used for the following analysis:Coordinate conversion between assemblies: Coordinate conversion was performed using the projected RefSeqv1.1 genes as a common factor across all assemblies. The algorithm works as follows: first, we extract the projected genes within the physical interval to convert (i.e. in the haplotype block), along with the corresponding gene IDs and coordinates from the RefSeq v1.1 annotation. We then identify the projections of these genes in the target assembly with their mapped coordinates and order the genes by their position in the target assembly. We then join all adjacent genes in blocks, allowing gaps of up to 20 genes. If none of these blocks contains at least 10 genes, we keep the longest block, otherwise, we keep all the blocks with more than 10 genes. Finally, we extract the coordinates of the converted block in the target assembly based on the first and last coordinates within each block .41) with a kmerSize of 2. In all cases we used the reference sequence from each pairwise comparison. For each pairwise comparison, we assigned SNPs as either within a haplotype block in the specific pairwise comparison, or not, according to the 5-Mbp haplotype blocks called using NUCmer alignments. We performed pairwise Wilcox tests with Benjamini-Hochberg adjustment42 for multiple testing in R to test differences in sequence complexity between SNPs inside and outside haplotype blocks. We performed tests for the total set of SNPs in each pairwise comparison, in addition to subsampling 10,000 SNPs from each category where possible  from each pairwise comparison. We also calculated significance for the combined sets of subsampled SNPs across all pairwise comparisons for each chromosome.To calculate sequence complexity surrounding SNPs we used the show-snps from MUMmer to extract all SNPs plus 10 bp flanking sequence from all pairwise NUCmer alignments of chromosomes 2B and 6A (using -C option to exclude SNPs with ambiguous mapping). We excluded sequences with Ns. We calculated the Shannon richness of k-mers of SNP surrounding sequence using seqComplexity from the R library dada2 (ref. RHT-B1 (Traes4B02G043100) and RHT-D1 (TraesCS4D02G040400) alleles43. Using ~300 Kbp of sequence surrounding each gene, we performed a BLASTn alignment for a representative cultivar with the GA-insensitive RHT-B1b allele (Jagger) against the GA-sensitive alleles RHT-B1a_1 (CS), RHT-B1a_2 (Arina), RHT-B1a_4 (Julius) and RHT-B1a_8 (Mace). A similar comparison was done for the D genome GA-insensitive RHT-D1b allele (Mace) against the GA-sensitive alleles RHT-D1a_1 (CS), RHT-D1a_2 (Arina) and RHT-D1a_3 (Jagger). For each pairwise comparison, the total sequence used and the number of Ns per sequence was determined to calculate the ‘maximum alignable’ sequence . SNPs and small indels, alongside matched sequences were tallied for each BLAST alignment, and the ‘total aligned sequence’ was calculated (SNPs/indels plus matched sequence). The percentage of sequence identity was calculated by dividing the total matched sequence by the total aligned sequence. The sequence identity values calculated from the BLASTn alignment of the ~300 Kbp were consistent with the values obtained in the tabulated NUCmer output files which only include alignments ≥20 Kbp. The breadth of the total alignment was calculated by dividing the ‘total aligned sequence’ by the ‘maximum alignable’ sequence. The average breadth of the total alignment in the 7 pairwise comparisons  was 99.6 ± 0.2%, i.e. an average of 297,244 bp aligned out of the possible 298,315 bp queried. Indels which disrupted the BLAST alignment were included in the comments section of Supplementary Fig. Rht-B1 comparisons) and 77.4 ± 5.4% (3 Rht-D1 comparison) of the ~300 Kbp sequence. This was due to alignments shorter than 20 Kbp which were considered in the analysis above but that were filtered in the NUCmer output (-l 20000).For the 15 wheat cultivars, we identified RHT allelic status were based on http://wheatpedigree.net/ and published work44, and were visualised using Helium45 .Cultivar pedigrees and 15. For the whole genome block length and gene content we calculated median values including all pairwise haplotype blocks. To calculate block length and gene content in relation to chromosome position . This enabled us to calculate gene content using the RefSeqv1.1 high-confidence (HC) and low-confidence (LC) genes, in addition to placing the haplotypes into chromosomal compartments as calculated based on recombination for Chinese SpringTo calculate the percentage of the genome/individual chromosomes shared between pairs of cultivars , yield25, spike number50, plant height53, harvest index58, chlorophyll50, ear emergence59 and maturity53. To assign physical positions we used the probe sequence (where available) for the peak (GWAS) or flanking markers (QTL) as queries in BLASTn alignments against the IWGSC RefSeqv1.0 assembly. In cases where only primer sequences were available for markers, we required that both markers had a BLAST hit in the same region on chromosome 6A (exact distance tolerated depending on the type of marker). All alignments were manually inspected. Data are presented in Supplementary Data We performed a literature search for studies which had previously identified associations with productivity-related traits in the ‘highly conserved’ region on chromosome 6A. We identified associations with grain sizeeight TGW7, yield260. 35K Axiom genotyping for the 111 Recommended List cultivars  Germplasm Resources Unit (GRU). Seed for the UK Recommended List cultivars was also obtained from the JIC GRU; if not available it was obtained directly from the companies which bred the cultivars . DNA extraction of young leaf samples and KASP genotyping were conducted as previously describedars Fig.  was perf4F2 plants for recombination between markers gwm334 and gwm570 (ref. 22). 67 recombinants were identified and self-pollinated to generate homozygous BC4F3 RILs. We defined the recombination events using 40 additional KASP markers  were developed by screening 212 BCThese 11 RILs were evaluated at the JIC Experimental Field Station in Norwich  in five trials across four years: large-scale yield plots (1.1 x 6 m) in 2013–2016 and an additional trial of 1.1 x 1 m plots in 2015. Exact lines grown in each trial are detailed in Supplementary Data 4F2 plants) with the aim of further documenting recombination events across the extended chromosome 6A haplotype block. We identified 428 independent heterozygous RILs between BS00066522 and BS00066623 and confirmed 178 independent homozygous BC4F3 RILs after self-pollination. Note that not all heterozygous BC4F2 RILs were taken forward to the BC4F3 generation. We further defined the location of recombination events in the homozygous RILs using 5 additional markers , BS00185740 , BS00041481 , Hap-P2  and BS00009988 ; Supplementary Fig. We subsequently conducted a second, larger screen (2674 BC23) and extracted all markers located on chromosome 6A according to the physical positions. To assign genotyped lines to 6 A haplotype groups H1–7, we extracted markers within the minimum haplotype block (MHB) based on the physical positions provided . We ass: We downloaded the Breeders’ 35K Axiom genotyping data from cerealsdb.uk.net62 along with physical positions according to RefSeqv1.0 and genetic map information. We extracted all markers located on chromosome 6A based on having both a RefSeqv1.0 position on 6A and a consensus genetic map position on 6A. To assign genotyped lines to 6A haplotype groups H1–7, we filtered to include only markers that had calls for both alleles in the whole dataset  and then extracted markers in the MHB based on physical positions (187–445 Mbp). We assigned haplotype groups based on marker profiles across the MHB. For haplotype assignment, we excluded AX-95201760 as it had inconsistent allele calls amongst repeated lines, and also between Cadenza samples and the Cadenza assembly. We also excluded AX-94532884, AX-94560723 and AX-94531101 as the probe sequences did not have unambiguous BLAST hits in at least four of the sequenced cultivars, and therefore alleles could not be extracted (see below for details). In this study, we present the genotypes from Australia, CIMMYT Core Germplasm (CIMCOG), UK Recommended List and the Watkins landrace collection61. We also generated 35K Axiom data for an additional 111 lines from the UK Recommended List and used the same set of markers across the MHB as described above to assign lines to 6A haplotype groups . We included this quality control given that the SNPs originated from an exome capture (i.e. SNPS expected in exons and adjacent intron sequence) and that the VCF files do not include information about quality/depth of SNPs. Lines were assigned to 6A haplotype groups based on the marker profile across the MHB (187–445 Mbp). Here, we present the haplotype allocations for the cultivars from the USA.Exome capture: We downloaded the processed VCF (after imputation and filtering) of SNPs called for 811 accessions: To identify alleles for all markers in the MHB for each platform, we performed BLASTn alignments against all 15 genome assemblies using the SNP context sequence as the query (-perc_identity 98 -max_target_seqs 1). For the 15K iSelect and 35K Axiom arrays we used the probe sequence and for the exome capture we extracted 100 bp up and downstream of the SNP position from the IWGSC RefSeqv1.0 assembly. We filtered the BLAST output to retain only those results with full length hits to chromosome 6A (in the case of chromosome level assemblies) and extracted the allele from the corresponding location in the BLAST query sequence. For marker-assembly combinations for which a full length hit to chromosome 6A was not found, BLAST alignments were manually inspected to identify the correct allele call. In the case of scaffold level assemblies, manual inspection of BLAST hits was used to ensure scaffolds originated from chromosome 6A based on homoeologous SNPs observed in chromosome level assemblies. Only sequences which could unambiguously be assigned to chromosome 6A based on the BLAST alignment were retained and given an allele call.Extracting alleles of markers and exome capture SNPs from sequence cultivars: We used show-snps from MUMmer to extract SNPs from pairwise alignments of chromosome 6A between Chinese Spring and all other cultivars (using -C option to exclude SNPs with ambiguous mapping). We removed SNPs where SUB1 or SUB2  were either “N” or “.”. We then collated SNPs from all comparisons and removed duplicate reference SNP positions to get the final set of chromosome 6A SNPs summarised across all 15 sequenced cultivars with respect to the Chinese Spring assembly.Identification of SNPs on chromosome 6A across 15 sequenced cultivars63 for 23 SNPs across the MHB, which we tested on DNA samples of the 15 sequenced cultivars. Of these, 17 markers performed well (e.g. distinct clusters) and were selected as the final set of haplotype-specific markers ), we considered a combination of markers from the 15K, 35K and exome capture in addition to novel SNPs identified amongst the 15 sequenced cultivars. We aimed to include 15K and 35K arrays if informative as these markers are already used by breeders and therefore may reduce the number of additional markers required for breeders to assign material to haplotype groups. We developed KASP assays using Polymarker4 using single seed descent. Seed source for plots were F4:5 seeds derived from a single F4 plant used for DNA extraction and genetic map development26. Populations were drilled in autumn (September/October) in all years as 1 m x 1.5 m plots (three rows spaced at 0.5 m). Heights were recorded by hand from the centres of the plots at maturity. All grain characteristic  were determined on the MARVIN grain analyser  with ~400 grains from harvested plots.The Paragon x Watkins bi-parental mapping populations were evaluated at the John Innes Centre Experimental Field Station  in 2011, 2013, 2014, 2015, 2016 and 2017  using package “qtl” (v1.41)64. The analysis took cross type and generation number of the populations into account by using “read.cross” options “BC.gen = 0” and “F.gen = 4” or “F.gen = 5”. The QTL model used a significant threshold calculated from the data distribution. A first QTL scan, using Haley-Knott regression, determined co-factors for the second scan. The second scan by composite interval mapping (CIM) identified QTL at a significance level of 0.05 taking the co-factors into account. All data can be downloaded from http://wisplandracepillar.jic.ac.uk/results_resources.htm.Genetic maps were constructed as described2 test of independence (‘MASS’ library65) to compare the frequency of haplotype H3 to all other haplotypes in the European germplasm based on year of release (pre-2000 vs. 2000 onwards). Pairwise Wilcox tests with Benjamini–Hochberg adjustment for multiple testing were conducted for sequence complexity analyses and for comparisons of block length and gene numbers across chromosomal compartments.Data are presented as mean ± s.e.m. For all boxplots, the box represents the middle 50% of data with the borders of the box representing the 25th and 75th percentile. The horizonal line in the middle of the box represents the median. Whiskers represent the minimum and maximum values, unless a point exceeds 1.5 times the inter-quartile range in which case the whisker represents this value and values beyond this are plotted as single points (outliers). In Fig. Supplementary informationDescription of Additional Supplementary FilesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5Supplementary data 6Supplementary data 7Supplementary data 8Supplementary data 9Supplementary data 10


P < 0.05). Perch width and shape were found to affect both usage and perch balance, measured as the probability of successful or problematic landings. The wide rope was generally used more during the first week (P < 0.05) and was used more for sleeping or resting (P < 0.05) than the other wide perch types. Furthermore, birds were more likely to land on the wide rope or on flat perches successfully than they were to have a problematic landing (P < 0.05). That birds were more likely to be observed preening on flat perches than on the potentially shaky rope perches could further reflect a sense of security. Our results suggest that presenting several litter types could better fulfill chicks' behavioral needs and that flat perches or a wide rope (4.5-cm diameter) could be appropriate perch types for laying hen chicks and thereby promote early perch use and training.Early chick environment, such as provision of litter and perches, can be a predictor of laying hen welfare. Inadequate or nonpreferred litter and perch types could have similar negative effects as those seen when not providing these resources at an early stage, such as increased feather pecking and cannibalism in adult flocks. However, suitable litter and perch types for chicks are not well explored. In the present project, 6 different types of litter  and 6 different types of perches  were presented in a controlled way (3 at a time) to chicks in 6 pens. Usage was compared in 93 chicks of Lohmann Selected Leghorn Classic divided across the pens, during their first 3 wk after hatch. Different litter types were seen to be preferred for different behaviors. The majority of dustbathing bouts occurred in sand and peat. Chicks foraged more in wood shavings, hemp shavings, and sand than in peat and pellets ( An appropriate rearing environment is essential for laying hen welfare . WelfareRearing chicks without continuous litter provision could have long-term negative welfare effects . Adult bForaging behavior, such as ground scratching and pecking, starts to develop and increase during the first week after hatch . It has Dustbathing is an essential behavior for feather maintenance by removing excessive fat lipids . The behRegarding perch use, chicks start using perches when they are 1 wk of age . Early pMore research has been requested with regard to identifying appropriate facilities for foraging and dustbathing  along wiThe aim of this study was to investigate chicks' preference and usage of litter and perch types when presented with a choice of different types in their home environment. Identifying and using appropriate litter and perch types for chicks in the rearing environment could lead to greater success in the adult environment.The study was approved by the Animal Research Ethics Committee in Uppsala .For the present project, 93 female chicks of the hybrid Lohmann Selected Leghorn Classic, from the same commercial hatchery, were transported 383 km on their first day after hatch to the Swedish Livestock Research Center in Uppsala, Sweden. On arrival, chicks were divided across 6 pens in the same stall: 3 pens with 15 chicks and 3 pens with 16 chicks. The pens (1.15 × 1.5 m) contained feed and water ad libitum . The staThe 6 tested litter and perch types are presented in Because only 3 litter and 3 perch types could be presented at a time in a pen, the locations of the types were changed within and between pens over the study period so that chicks in all pens came in contact with the different types in a standardized way. This was carried out as per a prepared schedule  at middaLive behavioral observations were divided into 2 parts: “time budget observations,” to determine how chicks were distributing themselves between the different types of each resource and what behavior they were performing in each type, and “perch balance observations,” wherein we observed chicks' abilities to jump up onto and maintain their balance on the different perch types . An equaFor “time budget” observations, the location and behavior of each chick was observed by instantaneous scan sampling each pen. See For “perch balance” observations, we conducted one 5-min continuous observation per pen on 8 occasions  at 5 ages: day 6, 9, 12, 16, and 21 (between 10 am and 3 pm). Observation days were chosen so that the observations were balanced across different perch heights. See A total of 1,764 scans per litter type and per perch type, as well as a total of 720 min of balance observations per perch type, were performed.All statistical analyses were performed using R software  for each perch type.All proportions were treated as real values, and linear mixed models were fitted using the restricted maximum likelihood approach using Laplace Approximation and R package lme4  as fixed effects and pen as a random effect. The effect of age (as week) was included in the model for litter or perch type preference if an interaction between age and perch or litter type was found to be significant. An effect of age (as week) was only found for general perch use and sleeping/resting on a perch and was therefore only included in these models. There was no effect of time of observation (hour of the day) on preference, so this factor was left out in the statistical models. For perch balance, the mixed model included perch width, perch shape, and landing type (problematic or successful) as fixed effects (including a possible interaction between them) and pen as a random effect.P < 0.05) were further investigated using pairwise comparisons of the least square means with Satterthwaite's approximation. Pairwise comparisons were adjusted for using Tukey's method.Significant fixed effects were investigated using type III ANOVA with Satterthwaite's approximation of degrees of freedom and the lmerTest package  in the Real Statistics Resource Pack Software .In the following section, the general time budgets of the chicks are presented, followed by results on how the different litter and perch types were used. Finally, we present how the different perch types affected a chick's ability to land in a stable manner.On average, chicks spent the greatest proportion of their time on the litter (0.5 ± 0.04), followed by being on a perch (0.18 ± 0.04), on the concrete floor (0.17 ± 0.01), at the feeder (0.12 ± 0.01), and at the drinker (0.02 ± 0). Regarding the proportions of observed behaviors, chicks were most frequently foraging (0.43 ± 0.04), followed by sleeping or resting (0.25 ± 0.03), moving or standing (0.22 ± 0.01), preening (0.08 ± 0), and dustbathing (0.01 ± 0.01). Moving/standing was not considered a motivated behavior category, and it is not highly dependent on litter or perch type, so it was not investigated further.5,30 = 9.86, P < 0.001), sleeping or resting in litter , preening in litter , and the average number of performed dustbathing bouts in litter . In pairwise comparisons, there was more foraging in wood shavings than in pellets (P < 0.001), peat (P < 0.001), and straw  and peat , sand (P = 0.006), and pellets , straw (P = 0.02), and wood shavings . Pairwise comparisons stratified on weeks showed that the wide rope was used more during the first week than any of the other perch types (P < 0.05), whereas no differences between perch types were found in the other weeks , whereas sleeping and resting in litter decreased correspondingly . However, there was no interaction between week and perch preference for sleeping or resting on a perch (P > 0.05). Instead, a significant interaction between perch width and shape was found for the average proportion of chicks sleeping on a perch  and wide flat perches . On average, birds tried to land more often on rope perches than on round (P ≤ 0.001) and flat perches , the wide flat (P ≤ 0.001), and the wide rope perch . Attempting to land on the 2 round perch types was equally likely to result in a problematic as in a successful landing  would be appropriate to promote early use of these essential resources and thus help reduce some of the welfare problems commonly seen in adult laying hen flocks.Chicks were observed to forage in around 50% of our observations, which corresponds to time budgets observed in semiwild relatives to the domestic fowl and red jungle fowl that forage around 60% of their active period . This su10.13039/100014337Furthermore, low dustbathing activity when young can lead to severe feather pecking activity in adult age  or a braided rope shape  could be adequate perch types for laying hen chicks to promote early perch use, increase general usage, and improve perch balance. All these could be beneficial for bird welfare in commercial aviary systems.


ST and varLD tests. The principal component analysis revealed a sub-structure with a close connection between the two subpopulations. The averages of genomic relationships, inbreeding coefficients, and linkage disequilibrium at varying genomic distances were rather similar across them, suggesting non-accentuated differences in overall genomic diversity. Autozygosity islands revealed selection signatures common to both subpopulations at chromosomes 13 (63.77–65.25 Mb) and 14 (22.81–23.57 Mb), which are notably known regions affecting growth traits. Nevertheless, further autozygosity islands along with FST and varLD tests unravel particular sites with accentuated population subdivision at BTAs 7 and 18 overlapping with known QTL and candidate genes of reproductive performance, thermoregulation, and resistance to infectious diseases. Our findings indicate overall genomic similarity between Angus subpopulations, with noticeable signals of divergent selection in genomic regions associated with the adaptation in different environments.Highlighting genomic profiles for geographically distinct subpopulations of the same breed may provide insights into adaptation mechanisms to different environments, reveal genomic regions divergently selected, and offer initial guidance to joint genomic analysis. Here, we characterized similarities and differences between the genomic patterns of Angus subpopulations, born and raised in Canada (N = 382) and Brazil (N = 566). Furthermore, we systematically scanned for selection signatures based on the detection of autozygosity islands common between the two subpopulations, and signals of divergent selection, via F Bos primigenius taurus) of moderate frame size, originated in the Highlands of Scotland. It has become adopted worldwide for beef production, due primarily to its distinguished carcass and meat quality highly attractive in currently demanding market1. The introduction of Angus in Canada and Brazil occurred during the 19th and early 20th centuries, respectively. In both countries, black and red coat color variants have been registered in a single herd book3. In Canada, purebred Angus is found throughout all provinces, being the most popular beef breed, in terms of number of new registered animals per year 5. In Brazil, nearly 51% of 9.62 million beef cattle semen doses commercialized in 2018 were from Angus breed, having more than half of the semen straws imported from Canada and the U.S.6. Despite widely importing foreign genetic material, Brazil also performs national genetic evaluation including Angus purebred local records, which allows the identification of superior Brazilian sires. The progeny originated from locally selected animals tend to be more adaptable to tropical conditions and more robust to environmental changes7.Angus is a taurine breed  between the genotyped markers and quantitative trait loci (QTL), which may not persist between subpopulations, or may even be in reversed phase. Although it is expected that LD phases extend across large distances for subpopulations of the same breed in different countries13, this information is yet to be adequately assessed in Angus cattle.Combining international data of the same breed with the employment of proper adjustment factors has long been referred to as a promising strategy improving accuracies for cattle genetic analysis14. Furthermore, selection, either natural or human-oriented, intensifies the differentiation between subpopulations by driving particular genomic regions towards special patterns in each specific geographical location15.The geographic division of Angus subpopulations may increase the diversity and sub-structure within the breed, albeit they originated from a unique domestication center. Since, differentiation in allelic frequencies, haplotypes diversity and LD tend to increase over time due to reduced common ancestry between subpopulations, different environmental pressures, and potential differences in selection schemesHere, we characterize differences and similarities in the genomic profile of Angus from Canada and Brazil, by assessing the genomic level of relationship, inbreeding, linkage disequilibrium and persistence of linkage phase in both subpopulations. In addition, we scan for selection signatures that may shed light on the molecular basis of selected traits and divergent adaptation mechanisms on the two subpopulations. Although the current study has been focused on the population genomic aspects, our results are relevant to genomic predictions, especially to further collaborative analyses incorporating the studied herds.Here, we evaluated a sample of 948 Angus cattle (382 from Canada and 566 from Brazil) with genotypic information of 31,483 autosomal markers, spanning up to 2.5 GB of the bovine genome in an average spacing of 78.6Kb . The overall averages of observed heterozygosity  and minor allele frequency  did not differ between Brazilian and Canadian subpopulations  explained by the first and second principal components was 21.72% and 6.07%, respectively Fig. . The levGRM estimates being overall higher than FROH  were similar in the two subpopulations Table , being sOH Table , Fig. 2.2 statistic. The number of SNPs, averages of SNP distances and r2 of adjacent SNPs per chromosome are presented in Supplementary Table 2 of all pairwise adjacent markers averaged 0.20 and 0.21 in the Brazilian and Canadian subpopulations, respectively , through rly Table . The r2 ld Table . There wWith all combinations of syntenic SNPs, the overall average of r² kept higher than 0.2 for markers distanced up to 50Kb in both subpopulations Fig. . At distST and varLD tests (between-population comparative methods) identified 5 regions each, with at least two consecutive windows exceeding the empirical threshold  in the Canadian and Brazilian subpopulations, respectively Fig. . The FSTold Fig. . The lenMYH7B (myosin heavy chain 7B), GDF5 (growth differentiation factor 5), PLAG1 (PLAG1 zinc finger) and XKR4 (XK related 4)20. A further ROH island exclusively detected in the Canadian subpopulation, BTA7:37.84–38.64, overlapped with a signal of divergent selection highlighted via FST (Table ARL10 (ADP-ribosylation factor-like 10) gene is suggested to play important role in bovine pre-implantation embryo development21 and the UNC5A (unc-5 netrin receptor A) gene previously showed association with reproductive traits in taurine breeds, such as the number of insemination per conception, fertility index and the interval between first and last insemination22.Two ROH islands common to both subpopulations were detected at BTA13:63.77–65.25 and BTA14:22.81–23.57 Fig. , surrounST Table , indicatST, and varLD), at BTA7:21.31–21.89 and BTA18:11.75–11.94, are putatively related to adaptation into adverse conditions across the different environments (Table IRF1 (interferon regulatory factor 1) and IL4 (Interleukin-4) genes, which control the differentiation of naive T helper (Th) cells and activation of cell-mediated or antibody-mediated immune responses24. In addition, the KIF3A (Kinesin family type 2 member 3 A) gene, mapped at this region, is a candidate to innate tick-resistance typical of some breeds25. Beyond immunity-related genes, the selection signature BTA18:11.75–11.94 comprised the COX4I1 (cytochrome c oxidase subunit 4I1) that is related to thermoregulatory efficiency. This gene represents a candidate gene to body temperature regulation, which was previously associated with cold tolerance26, and was up-regulated in the liver of cows experimentally submitted to cold-stress27.Selection signatures revealed by the overlaps of the between-population differentiation methods  that is involved in cellular responses to heat stress30, and some key players of immune responses, such as CYBA  and CDK10 (cyclin dependent kinase 10)33.A further selection signature identified via FST, and varLD signals) comprised 74 genes. Significant enrichment of particular terms or biological processes was not detected in any of these gene lists.The selection signatures common between Brazilian and Canadian subpopulation (BTA13:63.83–64.87 and BTA14:24.43–26.20) comprised 41 genes, whereas the regions representing signals of divergent selection  recent gene flow or adaptive genetic variation in the subpopulation of higher diversity, or (2) genetic drift, reduced effective population size or increased inbreeding in the population of lower diversity36, but higher than the average of 0.28 previously reported to 42 Angus samples37. Presumably, this difference is due to the greater number of genotyped individuals studied here. Similar to previous results, the correlation between FGRM  and FROH was high, with greater estimates obtained to FGRM due to the non-distinction between IBS and IBD alleles as done by FROH39. Long ROHs  were observed in both subpopulations, indicating relatively recent relatedness within both of them. Autozygous segments longer than 8,000 Kb and 16,000 Kb indicate nearly 6 and 3 generations, respectively, since the common ancestor that gives origin to both haplotype copies40.The observed heterozygosity within each Angus subpopulation was in agreement with averages varying from 0.357 to 0.399, reported in taurine breeds genotyped with high-density SNP panelshttp://www.asbia.org.br/wp-content/uploads/2018/10/INDEX-ASBIA-2017_completo.pdf).The population structure depicted in PCA showed a great overlap between the clusters of the Brazilian and Canadian samples. One reason for this continued genomic connection resides in the recent common ancestry, also evidenced by the relative high LD phase persistence across the subpopulations. This recent common ancestry is most likely due to the importation of semen from Canada into Brazil, as well as the extensive importation of U.S. Angus sires into both countries 43. The LD is a decisive parameter to define the required markers density for successful genomic prediction and genome-wide association studies44, being suggested that r² values of 0.2 are required to achieve high prediction accuracies (>0.8)45. Considering the bovine genome size of 2,875 Mb, and the averages of r² higher than 0.2 for adjacent SNPs spaced up to 100Kb in both Angus subpopulations, the minimum genotyping density required to these subpopulations would be 28,750 SNPs. The distribution of 31,483 SNPs common between two different commercial arrays maintained some gaps longer than 100Kb along chromosomes. Thus, the imputation of genotypes to the non-common SNPs would be a feasible strategy to enhance the marker density in both subpopulations, fulfilling long gaps and assuring great prediction accuracies in further studies. Furthermore, a single reference population genotyped with a higher density of markers could supply reference haplotypes to both subpopulations, due to the strong preserved phase between these two subpopulations.The averages of r² considering all SNP pairs spaced up to 50Kb showed agreement with estimates previously reported for taurine breeds (r² ~ 0.25)et al.46 reported considerable gains in genomic prediction accuracies in five cattle traits using only SNPs of consistent linkage phase between two distinct populations. The linkage phase correlation between the Brazilian and Canadian subpopulations stayed high for genomic distances extended to 5 Mb, corresponding well with estimates of approximately 0.7 reported for subpopulations of a single breed42. This persistence was significantly higher than estimates involving different breeds (approximately 0.45)41. Therefore, combining the two subpopulations studied here to perform genomic prediction would result in gains in reliability, as it would enable increased reference population without the effect of SNPs in reversed phase masking the marker-QTL association.The persistence of phase across populations may indicate allele-QTL preserved phases, being another very important issue to joint-genomic analysis. Porto-Neto 48, thus putatively influential on growth traits undergoing selection in both subpopulations. These two signals were previously reported as selection signatures of Angus49 and were significantly associated with the body weight of Brahman50, in a study based on the reference UMD3.1. Reasonable candidate genes of BTA13 includes the GDF5 gene, which belongs to a family of bone morphogenetic proteins that stimulate bone formation and regulate growth51, and MHY7B that is expressed in the skeletal muscle and downregulated in double-muscled breeds20. The region of BTA14 comprising PLAG1 and XKR4 genes is well-known for its pleiotropic effect on economically important traits of beef cattle, such as backfat thickness, ribeye area, body and carcass weight5255, as well as the serum level of the growth-related hormones, IGF153.The suitability of ROH islands for the selection signature scanning was reinforced here, due to several overlaps with QTL of traits undergoing selection and previously reported selection signatures of beef cattle. The two ROH islands found common between the Brazilian and Canadian samples suggested common selection events on BTA13 and BTA14, at genomic regions harboring genes known to be associated with stature in mammalsST results. The rs110428791 SNP, which is an intronic variant of gene UNC5A mapped at this selection signature, was previously associated with reproductive performance in taurine breeds, in a study based on the reference genome UMD3.122. In addition, homologous genes of RNF44 (ring finger protein 44), and UIMC1 (ubiquitin interaction motif containing 1), have been associated with reproductive risks and duration of reproductive life in women57. These findings suggest selection pressures in Canadian subpopulation with a balance between growth and reproductive traits.An impressive signal of genomic differentiation between the subpopulations was identified at BTA7, corresponding to the ROH island exclusive of the Canadian subpopulation that partially coincided with F58, and it comprises genes that showed differential expression between susceptible and resistant hosts to cattle tick (KIF3A)25, mange infested and uninfested animals 59, healthy and with mastitis cows (IL4)60. In addition, this selection signature was associated with the adaptation of taurine breeds to harsh environments61 and of indicine breeds to not so harsh, but still challenging environmental conditions62.The signal of divergent selection detected at BTA7:21.31–21.89 is most likely related to differences in the ability of quicker and strong response towards pathogen invasions across the subpopulations raised in different environments. This genomic region was previously identified as a QTL associated with resistance to viral load in Holstein cattle61 and harbored candidate genes to immune and thermoregulatory efficiency. It is noteworthy that the longest selection signature (BTA18:13.76–15.40), encompassed the major gene for coat color variation in Angus, MC1R28. We were able to refuse the likelihood of this signal being related to the presence of red Angus samples (recessive homozygous) in the genotyped Brazilian subpopulation. However, we didn’t estimate frequencies of recessive allele among the samples of black coat color, since our genotyping information did not include the causal mutation for pigmentation in Angus, rs10968801329. Nevertheless, none of the two countries perform selection based on coat colors. Thus, it is not discarded the hypothesis of this signal being caused by other genes in this region, related to better performances on selected traits under challenging environmental conditions. Furthermore, this region has been indicated as QTL of resistance to infection in dairy cattle63, and revealed as a selection signature of cattle breeds with coat color phenotypes not determined by MC1R, such as Brown Swiss, Hanwoo and Nguni65. Further investigation adopting denser panels or even sequencing data could narrow down this region and empower the identification of the causal mutation(s) behind this signal.Two highlighted regions of BTA18 overlapped with selection signatures previously reported in Russian taurine breeds adapted to harsh weather13, the Angus subpopulation from Canada and Brazil still present substantial genomic connection likely due to common ancestry (mainly influenced by the use of semen from common sires) and similarity between the selection schemes, primarily focused on growth-related traits in both countries66. Nevertheless, signatures of divergent selection pinpointed particular loci, related to reproductive performance, thermoregulation, and resistance to infectious diseases. Therefore, specific population subdivision might have been caused by both adaptation to distinct environments and particularities of selection criteria applied in each country, such as an additional emphasis on breeding indexes that combines fertility and growth traits in Canada69.Despite the potential of parallel selection in promoting differentiation of geographically isolated subpopulations of the same breedCare and Use Committee approval was not required for the current study since it was performed with previously genotyped samples.et al.70, consisted of steers born at the Onefour Research Substation of the Agriculture and Agri-Food Canada Research Centre (AAFC) at Lethbridge - AB, from 2004 to 2008. These samples were genotyped for approximately 54 K SNPs included in the BovineSNP50  panel. The Brazilian subpopulation comprised 352 heifers born in 2013 at Santa Helena farm at Uruguaiana - RS, and 214 influential Angus sires of Brazilian herds. Brazilian samples were genotyped for approximately 139 K SNPs included on GGP-150K - NEOGEN  panel. The genotyped Canadian subpopulation was exclusively composed of black coat color samples, whereas 206 out of 566 Brazilian samples were red Angus.The genotypic data consisted of 382 and 566 Angus born and raised in Canada and Brazil, respectively. The Canadian subpopulation, previously described in Chen 71, using publicly available coordinates provided at https://www.animalgenome.org/repository/cattle/UMC_bovine_coordinates. SNPs presenting different positions among the different coordinate files aligned into the ARS-UCD 1.2 (N = 12) were filtered out from both arrays during the remapping process. Then, non-autosomal markers, markers presenting call-rate lower than 0.90 or showing a significant deviation from Hardy-Weinberg equilibrium (p < 10−5) were removed in both subpopulations datasets. Imputation of missing genotypes and haplotype phasing was performed per subpopulation using the Fimpute v372. Finally, only non-monomorphic markers, common between the two panels were kept to further analyses .All the genotyped samples presented call-rate higher than 0.90. The SNPs from both arrays were remapped according to the new bovine assembly, ARS-UCD 1.273, with base allele frequency fixed to 0.574. Genomic inbreeding coefficients based on GRM (FGRM) were estimated by subtracting one from its diagonal elements. Runs of homozygosity based inbreeding coefficients (FROH) were estimated as the sum of lengths of ROHs of each individual divided by the length of the genome covered by the SNPs75. The ROH discovery was conducted using PLINK v1.976, by sliding a 30 SNPs window in one SNP interval throughout each autosomal chromosome. One heterozygous genotype was permitted per window to account for occasional genotyping errors. The minimum length of an ROH segment was set to 1,000Kb, where a density of at least 1 SNP every 120Kb and no marker-intervals longer than 1,000Kb were required, following the recommended criteria of smaller density genotypes77.Relatedness between all pairs of individuals was estimated using the GRM computed according to method 1 in VanRadenth percentile were assigned as ROH islands. The positions of the first and the last SNP of these regions were assumed as the start and end point of ROH islands.SNP autozygosity scores were calculated per subpopulation and expressed as a proportion of animals in which each SNP appeared in an ROH. Genomic regions harboring at least two adjacent SNPs falling into the 99.976) in our results, the ROH detection was repeated with an approach based on run detection in consecutive SNPs, implemented in the package detectRUNS78 of R software v3.4.479. This procedure revealed FROH values highly correlated (0.98) with those estimated in PLINK76, and essentially the same ROH islands , as proposed by Hill and Robertson82. The decay of LD was then analyzed in each subpopulation using r² averages within bins according to the physical distances of SNP pairs, including 10 Kb intervals starting from 10 to 100 Kb, and the intervals 100–200 Kb, 200–500 Kb and 500–1000 Kb. To assess the effect of MAF on LD, the r2 was calculated with SNPs of minimum MAF higher than 0.01  and 0.05  in both subpopulations.Phased haplotypes were used to estimate the LD extent and persistence of phase, applying publicly available scripts at https:// www.msu.edu/~steibelj/JP_files/LD_estimate.htm. A measurement of LD was estimated for all syntenic SNP pairs by the genotype squared correlation . We estimated rij between all pairs of SNPs spaced for shorter intervals than 10 Mb in both subpopulations. Then, the persistence of phase was estimated within intervals of 100Kb, starting from 0.1 to 5 Mb, as Pearson’s correlation of rij for each subpopulation, as follows:m,n is the correlation of phase between rij in the subpopulation m (Brazilian) and n (Canadian); ij within interval p in the subpopulation m and n, respectively; and s(m) and s(n) are the standard deviation of rij in the subpopulation m and n, respectively83.The consistency of the linkage phase for pairs of 84, implemented in varLD software85. The varLD method consists of building symmetric LD matrices in each subpopulation, with r2 of all pairs of SNPs within windows defined based on a fixed number of markers, sliding in one SNP interval. Next, eigenvalues of each symmetric matrix are computed per subpopulation, and raw varLD scores assigned as the sum of absolute differences between ranked eigenvalues of the homologous matrices. Finally, each raw varLD score is compared against the distribution with all windows values. Since one of the premises of varLD statistic is the establishment of windows encapsulating the same number of SNPs across the genome84, we choose to define windows of 15 SNPs (mean length of 1095.9 ± 389.9 Kb), instead of windows with fixed length size. Genomic regions with at least two consecutive windows falling in the 99.9th percentile of varLD were considered indicative of divergent selection. Preliminary analyses carried out using a range of SNP numbers  yielded consistent results related to the majority of signals, with the shortest boundaries of the detected signals associated with the windows of 15 SNPs , estimated according to Cockerham and Weir86 using the “fsthet” package87 in R software, v3.4.479. FST and heterozygosity were averaged in sliding windows set equal to those of varLD procedure . To control false positive discoveries in FST test the empirical thresholds were defined based on the FST-heterozygosity outliers approach87. Windows were grouped into 20 bins according to heterozygosity and genomic regions were considered as putative signals of divergent selection if at least two sequential windows fell into the 99.9th percentile scores in its heterozygosity’s bin. Preliminary analyses carried out using windows defined based on physical size  and SNP number (15 and 30) yielded consistent results  were considered selection signatures, as they are more likely of being true positive signals88. Known QTL overlapping the selection signatures were assessed in the CattleQTL database - release 4089, with QTL positions lifted from the former reference genome UMD3.1.1 to the new reference ARS-UCD 1.2. The Ensembl cow gene - set 9990, which is based on the new ARS-UCD1.2 reference, was used for gene prospection.Genomic regions identified into the top 99.9 percentile of more than one of the tests .Two lists of candidate genes were evaluated as over-representation of biological processes and molecular pathways, using the DAVID v.6.7Supplementary information.