Small ruminant production is an important agricultural activity and has a substantial contribution to smallholder farmers in generating income and securing food in developing countries . TropicaAwassi crossbred rams have been distributed in the highlands of Ethiopia to increase the body size of the indigenous fat-tailed sheep breeds through crossbreeding . HoweverGenome sequencing and the subsequent development of single nucleotide polymorphism (SNP) chip data have been extensively used to learn about population stratification and admixture in human populations , 15. Genhttp://www.sheephapmap.org/) database. The three villages were Negasi-Amba, Chiro and Serity. Negasi-Amba and Serity are located in North Shewa whereas Chiro is in the South Wollo administrative zone of the Amhara Regional State in Ethiopia. The two government farms were Debre Berhan Agricultural Research Center (DBARC) and Amed Guya Sheep Breeding and Multiplication Center (AGSBMC). Menz and Wollo sheep breeds are indigenous to Ethiopia, classified as short fat-tailed breeds and they are reared for meat in sub-alpine and cold highland agro ecological zones of Ethiopia [A total of 754 genotypes were obtained from three parental sheep breeds  and two crossbred populations (Menz x Awassi and Wollo x Awassi) Table Ethiopia . Both brEthiopia , 18. CroEthiopia , 19. In n = 75 sheep) were used to select AIMs. Ovine SNP50K data  for the two parental breeds, Menz (n = 34) and improved Awassi (n = 23) are available in ISGC database. Quality control for data obtained from Menz and Awassi sheep was indicated in [n = 19), genomic DNA was isolated from ear tissue samples at Holeta Agricultural Research Center, Holeta, Ethiopia. The samples were genotyped using Illumina OvineSNP50 BeadChips  containing 53,862 evenly spaced SNPs according to the manufacturer protocol. A total of 4494 SNPs and one individual were removed due to SNP call rate of <0.99 and poor genotyping rate (<95% SNP missing rate). After removing SNP with unknown position and merging with common SNPs, a total of 75 individuals from the three breeds  sharing 47,749 SNPs were available. Data editing and quality check was carried out using PLINK [Ovine SNP50K data from the three parental breeds  were used to select the AIMs. The two indigenous Ethiopian populations were merged as one breed as they are very close to each other having FST value of 0.004. A total of 150 SNPs that showed large differentiation between local breeds and exotic Awassi were selected based on their FST values. The National Center for Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/) was used to identify nucleotide sequences in the genomic region for each of selected SNP (100 base pair in both directions). Top FST ranked 105 SNPs markers were selected from a list of 150 SNPs considering their compatibility in designing primers.SNPs passing the quality control criteria and common to the three breeds (n = 298), Chiro (n = 293) and Serity (n = 20) were obtained from farmers village for DNA extraction. Furthermore, crossbreds with exotic inheritance of 50% Awassi (n = 16) and 75% Awassi × 25% Menz (n = 16) were collected from DBARC and AGSBMC, respectively. Pure Awassi (n = 18) and pure Menz (n = 18) samples were collected from AGSBMC and Dargegn village, respectively and included in the admixture analysis as reference. The samples were ear tissues collected with Allflex ear tissue sampler, except for 90 blood samples collected with FTA cards. Ear tissue samples were collected between May and June 2012, whereas the FTA samples were collected in June 2009. Genomic DNA of the ear tissue samples collected from admixed crossbred populations and purebreds was extracted at Holleta Agricultural Research Center. Genome DNA extraction from FTA card was done at the Austrian Technology Institute laboratory, Tulln, Austria.A total of 643 admixed and 36 pure sheep samples were included in the admixture analysis. Samples from Negasi-Amba  at Veterinary Medicine University laboratory, Tulln, Austria. In short, a section of DNA containing a SNP was amplified from each individual by polymerase chain reaction (PCR), followed by a high-fidelity single-base primer extension reaction over the SNP being assayed, using nucleotides of modified mass. The different alleles therefore produced oligonucleotides with mass differences that could be detected using highly accurate Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight mass spectrometry .Three multiplex assays, each screening for 36, 36 and 33 SNPs, were designed using Assay Design Suite v1.0 software  software. SNP genotyping was performed using the iPLEX® GOLD Complete Genotyping kit with SpectroCHIPs® II in the 384 format , according to the manufacturer’s protocol, with a single modification: To reduce unspecific primer extension, 5 ng sheared salmon sperm DNA  per reaction was added to the PCR mastermix. Results were analyzed with the Sequenom Typer 4.0 software .Phenotypic data on growth and reproductive performances of crossbreed sheep having different Awassi levels were collected from flocks in Negasi-Amba and Chiro crossbreeding villages. Data for lamb growth were collected between May and July, 2012. Most of the lambs were at about 8 months of age at sampling time and therefore this weight was considered for the study. This age is also close to the market age of lambs in those areas. Data on ewe lambing interval and number of lambs weaned per ewe per year were obtained from database available at DBARC. Ewes with at least three lambing were considered for this study.Sheep body condition score is visual assessment of body fat and muscle on the live sheep. It is independent of body size which can provide an acceptable and useful estimate of the proportion of fat and muscle in live sheep. Data on body condition score were collected from each individual sheep by feeling the muscle and fat cover around the end of short ribs and over the backbone using the balls of the fingers and the thumb. Level of fatness and muscle was scored from 1 (emaciated) to 5 (obese or extremely fat). Full description of sheep body condition scoring is indicated in Additional file Ewes and lambs with best and poor performance were chosen within each of the two locations to test if there is association between performance and Awassi levels. Ewes were graded based on lambing interval and number of lambs having reached weaning age, whereas lambs were graded based on their eight months weight.ST value. Admixture level of each animal in the crossbred population was estimated using the software ADMIXTURE v1.2.3 [K = 2) parental populations. This software applies a maximum likelihood-based clustering algorithm that places individuals into two predefined clusters. Both unsupervised (prior population information was ignored) and supervised (with prior population information) computation methods were used. Individual admixture estimates from supervised and unsupervised admixture analyses were compared with Spearman’s correlation coefficients.The ancestry contribution was estimated for the admixed population using 74 top ranked AIMs selected by their FE v1.2.3  assumingST ranked SNPs in order to assess the possibility of reducing AIMs. Spearman’s rank correlation coefficient was used between each of the subsets and top ranked 74 SNPs. The accuracy and validity of ancestral estimates were also assessed using root mean square error (RMSE) which was used as a summary measure of precision in the estimate of individual ancestry proportion.Validity of the 74 SNPs in estimating the blood level of Awassi was assessed by comparing the ancestry estimate based on the 74 AIMs with pedigree information. Ancestry contribution was also estimated based on subsets of 65, 55, 45, 35, 25 and 15 SNPs selected based on both top and bottom Fyi is the observed admixture level for the ith observation and Where For the analysis of growth and reproductive performance, lambs and ewes were classified into six genetic groups based on the admixture level estimated from the genotype. The groups were 0% Awassi , 0 to <12.5%, 12.5 to <25%, 25 to <37.5%, 37.5 to <50% and equal or above 50% Awassi levels. Growth, lambing interval, number of lambs weaned per ewe per year were analyzed using PROC MIXED of SAS/STAT . Fitting location by genetic group analysis was not appropriate as most of the lambs in Menz location were below 25% Awassi level. Thus, within location analysis was implemented.The model to analyze lamb 8 month weight for each of Negasi-Amba and Chiro location was:yijkl is lamb eight months weight, μ is the overall common constant for all values of y, bi is the fixed effect of lamb Awassi level, sj is the fixed effect of sex, fk is the random effect of owner of lambs and ɛijkl is the residual effect.Where, The model to analyze lambing interval and number of lambs weaned per ewe per year for each of Negasi-Amba and Chiro location was:yijk is lambing interval or number of lambs weaned per ewe per year, μ is the overall common constant for all values of y, bi is the fixed effect of ewe Awassi level, fj is the random effect of owner of ewes and ɛijk is the residual effect. PROC GLM SAS/STAT [Where, SAS/STAT  was usedThe effect of lamb and ewe performance on Awassi level was further analyzed as:yij is either of the dependent variables in each location , μ is the overall common constant for all values of y, pi is the fixed effect of ewe or lamb performance level  and ɛij is the residual effect.Where, ST values between improved Awassi and Ethiopian fat-tailed breeds. The AIMs were distributed across the genome  and supervised (B) ancestry analysis from the ADMIXTURE software are presented in Fig. r = 0.990). Fitted linear regression line of the supervised on unsupervised (blue line) for crossbred population perfectly matched the diagonal line (red color)  than Menz sheep breed collected from two different locations (Negasi-Amba and Dargegn) with a range of 1.2–1.3%. The Spearman’s rank correlation between the individual ancestry fractions estimated by supervised and unsupervised analysis was very high , 75% Awassi and pure bred (100% Awassi). Especially the estimate was very close for the pure Awassi with mean value of 99.31% with 95% confidence 98.8 to 99.8% Table  and corrST ranked AIMs and subsets 65, 55, 45, 35, 25 and 15 AIMs selected both from top and bottom FST ranges are presented in Figs. ST was higher as the number of SNPs in the subset decreased. Among the SNP subsets the lowest root mean square error (RMSE) was found in the top 65 SNP set (0.013), with an increase to 0.020, 0.028, 0.037, 0.050 and 0.067 with top ranked 55, 45, 35, 25 and 15 AIMs, respectively. Bottom 65, 55, 45, 35, 25 and 15 SNPs resulted in RMSE values of 0.017, 0.028, 0.037, 0.045, 0.058 and 0.089, respectively. A t-test comparison between each subset and the top 74 SNPs  revealed that all SNP subsets had produced similar (P > 0.05) estimates with the mean value estimated based on the 74 SNPs, except for the bottom 15 SNPs (P < 0.05) (Table Spearman’s rank correlations (r) between the Awassi levels estimated using top 74 F5) Table  and 3a-cP < 0.05) affected lamb weight at eight months of age  at eight months compared to 0 to <12.5% and 12.5 to <25% Awassi level whereas similar (P > 0.05) to highest level of Awassi (25 to <37.5%) in that location. However in Chiro site, lamb eight months weight increased as the Awassi level increased up to 50%. Lambs with Awassi level above 50% were not different (P > 0.05) from the previous group (37.5 to <50% Awassi level). Male lambs were heavier (P < 0.05) than females in Chiro site.Awassi level significantly affected the eight months weight in both locations whereas the sex effect was significant only in Chiro village  and the number of lambs weaned per ewe per year and BC score did not vary. Lambing interval was longer as the Awassi level increased in both locations. Number of lambs weaned per ewe per year also showed a non-significant decreasing trend with increasing Awassi level.Within location analysis for lambing interval, number of lambs weaned per ewe per year and body condition score are presented in Additional file P > 0.05) in Negasi-Amba (6.8 to 10.1% Awassi level), but highly significant (P < 0.001) in Chiro village. Here the best performing lambs had an average Awassi level of 37.1%, while the medium and worst performing lambs had 25.2% and 17.7% Awassi level, respectively.Higher within population variations in lamb growth and ewe reproductive performance were observed due to crossbreeding in both locations. The weight of best performing 13% cohort lambs at eight months of age was 5.9 and 10.4 kg higher than the population mean in Negasi-Amba and Chiro, respectively  with Awassi level in any location  was higher compared to the correlation value of 0.96 obtained from ~500 AIMs suggested to predict breed composition in cattle [We identified a total of 74 SNPs from the Ovine50K SNP data as AIMs. The SNPs were selected based on their Fn cattle . These rn cattle . Sölknern cattle  proposedn cattle . Thus itn cattle . Use of n cattle , 31, 32.ST values. The RMSEs observed in this study were lower than the reported values of 0.090 and 0.182 when the ancestry estimate based on top 20 and 50 AIMs compared with the true ancestry [Ancestry prediction with AIMs also provided reproducible results when the number of SNPs was reduced. This was proved by very strong correlation (0.969 to 0.996) and low RMSE (0.013 to 0.037) observed between the 74 and subsets of 65, 55 and 45 SNPs selected either from top or bottom after ascending based on their Fancestry , and comancestry .ST ranked SNPs required to estimate the admixture level of Swiss Fleckvieh breed [ST = 0.76–0.95 for 105 SNPs) compared to the divergence of the ancestral populations of Swiss Fleckvieh  [ST value was 0.956.The number of markers required for population assignment depends on the populations under consideration. Our results showed about 45 SNPs selected in any way from the list of 74 SNPs were good enough for accurate estimation of the level of ancestry in the admixed Awassi and Ethiopian fat-tailed populations. The number of markers required to estimate the level of admixture in our study was fewer when compared with ~500 top Feh breed . This wa98 SNPs) . Ding et98 SNPs)  suggesteIn many developing countries, livestock crossbreeding has been implemented with poor or no pedigree recording. Thus, AIMs would provide a great opportunity to estimate the level of admixture in a cost effective way. Currently, the price or cost per SNP is in the range of about €0.04 to 0.15 for low density panels; highly dependent on the method and number of samples to be genotyped at a time. During data collection many farmers showed interest to pay for the breed composition information. Such information on accurate breed composition, particularly in Chiro site, has been important to sell breeding rams to surrounding farmers as well as other customers coming from other parts of the country. If a very low cost SNP chip would be available for this purpose, farmers would have a clear market advantage without the need to rely on incomplete or possibly inaccurate pedigree records.). Factors associated to variation in sheep performances and admixture proportions among location and farmers need to be investigated; however it might be associated with sheep flock size, environment, resource of farmers to keep bigger crossbred animals, level of farmers participation, interest of farmers, input access, variation in knowledge among farmers [Effects of location and farmers clearly influenced sheep performances and the level of Awassi admixture in two Ethiopian sheep populations  was superior to the 75% Awassi reported (23.5 kg) under on-station management at yearling age . It was .8 kg wasWe strongly suggest that choosing the level of exotic ancestry should consider the existing environment and its potential for improved management. Burrow  suggesteThe OvineSNP50 BeadChip array is a powerful tool to identify small subset of highly informative markers for inferring ancestry. We have established a list of 74 SNPs  with high levels of allele frequency differences between the Awassi and Ethiopian fat-tailed sheep. A subset of 45 SNPs selected from this list sufficed to accurately estimate admixture levels with a minimal error rate. This small set of SNPs might be cost effective for smallholder farmers to obtain the crossbreeding levels for their animals. Estimation of admixture levels could be easily integrated in the current Awassi crossbreeding schemes to increase efficiency of breeding programs. Moreover, the use of AIMs would inspire livestock breeding programs by availing breed composition, as opposed to pedigree based estimates of admixture levels, which persists as marked constraint in many developing countries. Genetic improvement applying efficient selection within the crossbred population and improvement in management should be considered together, leading to the development of a potentially robust and adapted composite population, preserving allelic combinations underlying productivity and particular adaptations. While this option should be explored in some areas of Ethiopia, we do not consider crossbreeding of local with exotic breeds the most important route for sheep improvement. Within breed selection of local breeds, applying conventional tools of performance recording and ranking of best animals needs to be widely established and supported.Additional file 1: Table S1.Descriptions of subjective sheep body condition scoring. (DOC 154 kb)Additional file 2: Table S2.ST values and non-missing allele counts for each population are shown. (DOC 169 kb)Selected ancestry informative markers. The marker name, chromosomal position, approximate location of the markers on the chromosome in kilo bases (Kb), two alleles, allele frequency of the first allele for each ancestral population, pairwise FAdditional file 3: Figure S1.Scatter plots of individual admixture levels estimated with supervised vs. unsupervised analyses. Blue line represents fitted linear regression line of supervised on unsupervised and red color represents the diagonal line when x = y. (PDF 86 kb)Additional file 4: Table S3.Least square means ± standard errors of lamb eight months weight for crossbred population by location, different Awassi level groups and sex in Negasi-Amba and Chiro villages. (DOC 48 kb)Additional file 5: Table S4.Least square means ± standard errors of lambing interval, number of lambs weaned per ewe per year and body condition score for the effect of Awassi level groups and sex in each site. (DOC 45 kb)Additional file 6: Table S5.Least square means ± standard errors of eight months weight, body condition score (BC) and Awassi level for top ranked and poor performing lambs. (DOC 37 kb)Additional file 7: Table S6.Least square means ± standand error of Awassi level and reproductive performance for top, medium and worst performing ewes in Negasi-Amba and Chiro sites. (DOC 37 kb)Additional file 8: Figure S2.Mean Awassi level and total number of crossbred sheep produced by farmers in Negasi-Amba (A) and Chiro (B) villages. Bar plots are indicated the total number of crossbred sheep by an owner and line plots showed the average level of Awassi (%). (PDF 234 kb)


Bos taurus chromosome).Bovine milk is widely regarded as a nutritious food source for humans, although the effects of individual fatty acids on human health is a subject of debate. Based on the assumption that genomic selection offers potential to improve milk fat composition, there is strong interest to understand more about the genetic factors that influence the biosynthesis of bovine milk and the molecular mechanisms that regulate milk fat synthesis and secretion. For this reason, the work reported here aimed at identifying genetic variants that affect milk fatty acid composition in Norwegian Red cattle. Milk fatty acid composition was predicted from the nation-wide recording scheme using Fourier transform infrared spectroscopy data and applied to estimate heritabilities for 36 individual and combined fatty acid traits. The recordings were used to generate daughter yield deviations that were first applied in a genome-wide association (GWAS) study with 17,343 markers to identify quantitative trait loci (QTL) affecting fatty acid composition, and next on high-density and sequence-level datasets to fine-map the most significant QTL on BTA13  from a high-density chip and variants detected by resequencing showed that the effect was more likely caused by a second nearby gene; nuclear receptor coactivator 6 (NCOA6). These findings were confirmed with results from haplotype studies. NCOA6 is a nuclear receptor that interacts with transcription factors such as PPARγ, which is a major regulator of bovine milk fat synthesis.The initial GWAS revealed 200 significant associations, with the strongest signals on BTA1, 13 and 15. The BTA13 QTL highlighted a strong functional candidate gene for de novo synthesis of short- and medium-chained saturated fatty acids; NCOA6. The most significant SNPs were either synonymous or situated in introns; more research is needed to uncover the underlying causal DNA variation(s).An initial GWAS revealed a highly significant QTL for de novo-synthesized fatty acids on BTA13 and was followed by fine-mapping of the QTL within The online version of this article (doi:10.1186/s12711-017-0294-5) contains supplementary material, which is available to authorized users. While bovine milk is generally regarded as being highly nutritious for humans and serving as an important source of proteins, fat, minerals, vitamins and bio-active lipid components, the net effect of dairy fat on human health is strongly debated. This is because saturated fatty acids (FA), which constitute roughly 60 to 70% of the FA in milk, have been associated with cardiovascular disease and obesity, while mono-and polyunsaturated FA have been associated with positive effects on both cardiovascular health and diabetes  that influences de novo synthesis of short- and medium-chained FA was fine-mapped and re-analyzed for novel single nucleotide polymorphisms (SNPs) that were detected by re-sequencing in order to attempt to identify the underlying causal DNA variation.Traditionally, detailed milk fat composition is determined by gas chromatography (GC) analysis. This is an accurate but expensive method and is not suitable for routine milk recording. Recent studies showed that Fourier transform infrared spectroscopy (FTIR) data, calibrated against gas chromatography with flame ionization detector (GC–FID) reference data from the same samples, has the potential to provide detailed prediction of milk fat composition –19. An a−1. The instrument was equipped with a DTGS detector. All spectra were transformed from transmittance to absorbance units. Absorbance spectra were preprocessed by taking the second derivative using Savitzky–Golay algorithm with a polynomial of degree 2 and a window size of 9 channels followed by extended multiplicative signal correction . The combined traits were CIS (% of FA with cis bonds), TRANS (% of FA with trans bonds), TRANS:CIS (trans:cis ratio), N3 , N6 , N3:N6 (omega-3:omega-6 ratio), DNS , SAT (% of saturated FA), MUFA (% monounsaturated FA), PUFA (% polyunsaturated FA), TOTAL , and iodine value. NEFA (free FA) and UREA were also included in the GWAS, but these traits have built-in prediction equations in the FT-IR instrument and are stored as a routine procedure in the Norwegian Dairy Herd recording system as parameters of milk quality and feeding, and were therefore not calibrated in this study.The traits that were calibrated in this study included 24 individual FA and 12 combined traits. Individual FA included seven short- and medium-chained, even-numbered saturated FA , two long-chained saturated FA , two odd-numbered saturated FA , seven monounsaturated FA . Predicted values of bimonthly test day samples were used for further statistical analyses. The ~1,650,000 FTIR-based FA profile predictions for individual cows (Y) were related to the pedigree structure of the NR population. To condense the information for genetic analyses, only a subset of the data was used. The cows had to be in 1st to 4th lactation and the test-days between 10 and 320 days after calving. The milk yield at the test-day had to be between 5 and 50 kg, and the fat percentage between 1.75 and 7.0. These criteria were designed to remove obvious outliers. Finally, the sire had to be an artificial insemination (AI) NR bull. Milk samples were recorded on a bimonthly basis. This left 950,170 profiles from 300,126 cows that were daughters from 1095 sires, with a total number of animals in the pedigree of 871,455 animals.The data were analyzed with the following mixed linear animal repeatability model:N for the GWAS were then derived from these results as the sire averages of daughters’ predicted FA compositions, which were each corrected for her fixed effects, non-genetic random effects and half of her dam’s genetic effect. The number of bulls with DYD and genotype information varied from step to step as described below, mainly because genotyping on the SNP chips (see below) was performed on animals with trait data for many of the traits in the breeding goal, and was not specific to animals with DYD for the milk FA. The average number of daughters per bull was ~300 in all steps.Initial genotyping for the GWAS was performed on 2552 NR AI bulls using the Affymetrix 25K bovine SNP chip  as described in . SNP filA dense SNP map for fine-mapping on BTA13 was constructed by combining genotypes from the Affymetrix 25K SNP chip with genotypes from Illumina’s BovineSNP50 (54K) and BovineHD (777K) BeadChips . A total of 1575 NR bulls were genotyped with the 54K chip, 536 of these bulls were also among the 2552 animals genotyped with the 25K chip. Next, 384 of the 1575 bulls were genotyped with the 777K chip. The three datasets were filtered to remove SNPs with a minor allele frequency lower than 0.05 and all remaining SNPs were positioned according to the UMD 3.1 assembly. The 25K dataset was imputed to 54K before the combined 54K dataset was imputed to 777K. All imputations and phasing were performed using BEAGLE v3.3.1  with defWhole-genome re-sequencing data were obtained for five NR elite bulls on an Illumina Genome Analyzer GAIIx instrument  with 2× 108 paired end reads. The five bulls were selected based on their large numbers of offspring and minimum relationships and therefore represented the genetic diversity of the population. Library preparation was performed using a TruSeq SBS V2-GA kit . Adaptor- and quality-trimming of raw reads in FASTQ-format was performed using the FASTX-toolkit v0.0.13 . The reanuclear receptor coactivator 6 (NCOA6) and acyl-CoA synthetase short-chain family member 2 (ACSS2) or within 2000 bp on either side of these genes. Among these 1260 SNPs, all SNPs in exons and UTR were selected for genotyping together with intronic SNPs that were present in the dbSNP database  as well as those of a model without this SNP effect [LogL(H0)], which were both calculated for each SNP using the ASREML package version 2.0 . A likelIn order to determine if more than one QTL was segregating in the candidate region, the effect of the most significant SNP from the single-marker analyses of the candidate gene region was corrected for by including it as a fixed effect in the single-marker model and repeating the analysis for all other SNPs in the candidate gene region.2) was estimated for all SNP pairs in the candidate gene region on BTA13 using Haploview 4.2 [NCOA6 block that contained the most significant SNPs (SNPs 98–102), block 2 was a wider NCOA6 block (SNPs 98–108), block 3 spanned ACSS2 (SNPs 114–122), while block 4 included SNPs that were present in both NCOA6 and ACSS2 (SNPs 98–125). For each of the defined blocks, haplotypes for each sire were determined from the phased genotypes. Since very few sires were homozygous for the least frequent haplotypes, sires with one or two copies of the haplotype were grouped and a two-sample t test was performed in R [Pair-wise LD measure (rview 4.2 . Haploptmed in R  to test 2CV) above 0.5; these FA were therefore considered predictable and included in the further analyses. As shown in Table cis-9, C18:0 and C14:0 . The best combined trait predictions were obtained for SAT, CIS and MUFA (R2CV = 0.96), while the best predictions for individual FA were found for C18:1cis-9 (R2CV = 0.94) and for C8:0 to C12:0 (R2CV = 0.91). The results showed that most major FA were predicted rather accurately, however with lower R2CV for C16:0, C14:0 and C18:0 . The ability to predict a FA with high confidence depended strongly on its concentration, and FA with concentrations less than 1% generally showed low R2CV and were considered unpredictable  was somewhat higher than that for the sum of monounsaturated (MUFA) and saturated (SAT) FA . These results can be explained by the fact that all three indices  reflect a combination of genetic and environmental factors, and that the prediction accuracy and concentration of individual FA are expected to affect the estimates for the indices. Estimated heritabilities for the sum of trans FA (TRANS) were lower than for the sum of cis FA (CIS), and this was also reflected in the individual FA.Relatively high heritabilities were estimated from the FTIR predictions Table . EstimatIn the literature, estimated heritabilities for bovine milk FA composition vary largely among studies depending on sample size, breed, and method. Our estimates were generally lower than those from other studies in which FA concentrations were predicted with mid-infrared spectroscopy , 8, 10, Phenotypic records for the 29 traits considered to be predictable, together with pre-existing records for urea and NEFA, were tested for their association with ~17,000 genome-wide distributed SNPs using a single-marker association model. We detected 200 significant marker-trait associations and the most significant associations were clustered on BTA1, 13 and 15. These QTL are further discussed below and compared with findings from other studies. All significant results are in Additional file acyl-CoA synthetase short-chain family member 2 (ACSS2), lies nearby this SNP and encodes an enzyme that catalyzes the activation of acetate for de novo synthesis of short-chained FA [ACSS2 was also suggested as a candidate gene that affects de novo synthesized FA  in Dutch Holstein–Friesian [In our study, the most relevant QTL were detected between 55.4 and 66.1 Mb on BTA13. These QTL affected the content in all short- and medium-chained, saturated de novo synthesized milk FA . Among these, the highest LRT was detected between SNP rs29018443 and C8:0 (LRT = 26.98), and this same SNP was also highly associated with C6:0, C10:0, C12:0, C14:0 and DNS. A strong candidate gene, ained FA . ACSS2 wFriesian  and DaniFriesian , and sevFriesian .cis-9,cis-12. This SNP was also significantly associated to N6, C18:1trans-11, C15:0 and PUFA. The QTL region spanned the ~126.3–144.4 Mb region and included also significant associations to C6:0–C12:0, DNS and DHA. SNP rs29019625 lies approximately 20 kb away from the SLC37A1 gene, which encodes a membrane bound protein that is involved in the translocation of glycerol-3-phosphate into the endoplasmic reticulum [ABCG1 and AGPAT3. The former is located at 144 Mb and is involved in macrophage cholesterol and phospholipid transport and may regulate cellular lipid homeostasis in other cell types  was between SNP rs29019625 located at 144.4 Mb on BTA1 and C18:2eticulum . Other ps , while  pathway .A connection between BTA1 and predominantly long-chained FA was reported in several studies. Schennink et al.  observedcis-9, CIS, trans:cis ratio, iodine value and total fat yield, with the highest LRT being for DNS (LRT = 25.8). This QTL is situated close to the genes encoding the following apolipoproteins APOA1, APOA3, APOA4 and APOA5 at 27.9 Mb. This QTL region is frequently cited in the literature. Bouwman et al. [cis-9 in Danish Jersey [The QTL region that was detected on BTA15 (between 22.6 and 29.0 Mb) affects C8:0–C14:0, DNS, C18:0, C18:1n et al.  detectedn et al.  and to Ch Jersey  were reph Jersey  reporteddiacylglycerol acyltransferase 1 (DGAT1) on BTA14 and stearoyl-coenzyme A desaturase 1 (SCD) on BTA26. DGAT1 encodes an enzyme that catalyzes the final stage of triacylglycerol synthesis . In contrast to the A variant, the K variant is associated with increased fat yield, fat percentage and protein percentage and decreased milk yield and protein yield. Selection may have favored the A variant in the NR population, because most selection pressure was put on milk and protein yield in the breeding goal. In contrast, both allele variants of an important SCD1 polymorphism (A293V) were found to be relatively common in the sequenced NR individuals with a MAF of 0.25 (data not shown); however a follow-up study that examined the SCD1 region by including additional SNPs did not detect any significant associations near SCD1 (unpublished results).GWAS studies frequently report strong associations between milk FA and the genes is (e.g. ), while rimarily . No signACSS2 as the most likely position of the QTL, but to a nearby gene i.e. nuclear receptor coactivator 6 (NCOA6) that encodes a transcriptional co-activator, which interacts with nuclear hormone receptors. The most significant result was found for SNP rs41700740 at 64,650,276 bp which is a synonymous variant located within this gene. The LRT for this SNP ranged from 62.6 for C8:0 to 24.5 for C14:0. Significant LRT were found for ~500 SNP/trait combinations in the QTL region. As an example, results for DNS are in Fig. Subsequent analyses were performed to fine-map the BTA13 QTL that affects de novo synthesized FA and to identify potential causal variations. We began by reanalyzing the associations between all the high-density SNPs on BTA13  and the traits that were significant in the initial GWAS (i.e. C4:0–C14:0 and DNS). Somewhat surprisingly, this analysis did not point towards the prime candidate gene NCOA6 and not ACSS2 as the most likely positional candidate gene underlying the QTL, both genes were investigated in more detail. A dataset consisting of 15 SNPs within NCOA6 and nine SNPs within ACSS2 as well as 180 SNPs in the regions surrounding these genes was constructed by combining sequence-level polymorphisms with SNPs from the Bovine HD BeadChip. Both C6–C14 as well as DNS were reanalyzed for these SNPs using the single-SNP model. The results showed that, for C6:0–C12:0 and DNS, the highest LRT was found for SNP 99, i.e. rs41700742 at 64,648,620 bp, which is a synonymous SNP located within NCOA6. High LRT were also detected for SNP 100 , SNP 102  and SNP 98 . All these SNPs are localized within NCOA6; the former and the latter are synonymous exonic SNPs whereas rs41700737 at 64,655,588 bp is an intronic SNP. For C14:0, SNP 161 (rs43711970) at 65,246,092 bp was slightly more significant (24.2 vs. 23.8) than SNP 99. SNP 161 is located within the gene UQCC, which is almost 400 kb away from NCOA6 on the telomeric side. Complete results for all traits and SNPs are in Additional file Since our analyses pointed towards In order to determine if more than one QTL segregated in the detected region, the DNS traits were re-analyzed by including the effect of SNP rs41700742 as a fixed term (not shown). The results showed that this SNP explained all the variation, which indicates that only one QTL is segregating for the DNS traits, and the signals detected for the remaining SNPs were merely due to LD between SNPs.2) for all SNP pairs in the candidate gene region are in Fig. NCOA6 block (block 1 that included SNPs 98 to 102), which displays eight haplotypes. A frequent haplotype (denoted 1.1) was associated with higher content of short-chained FA (p = 0.00037), while haplotypes 1.2 and 1.4 were associated with lower FA content . When the haplotype block was extended to include SNPs 98 to 108 in the broader NCOA6 block , the differences between haplotypes were less marked. Haplotype 2.1 within this block had an identical frequency and p value as in the narrow block. The two negative haplotypes from block 1 were split into several less frequent haplotypes, with the most frequent being haplotypes 2.4 (p = 0.038) and 2.6 (p = 0.09). Block 3 covered ACSS2 (SNPs 114 to 122) and produced even less significant results. A larger block that contained the SNPs located within both NCOA6 and ACSS2 , the differences between haplotypes became more marked again. The most frequent haplotype (4.1) showed a stronger effect than the remaining haplotypes with a p value of 0.00046. In summary, the strongest associations were found for haplotypes within a rather narrow region that contained NCOA6. Neither the haplotypes within a larger block that included both NCOA6 and ACSS2 nor the block that contained only ACSS2 were significant. Thus, the results of the haplotype analyses also suggest that NCOA6 is a stronger positional candidate for the observed variation in de novo FA synthesis than ACSS2.Finally, to better characterize the BTA13 QTL, all the SNPs within the QTL region were grouped into haplotype blocks in order to identify the haplotypes that displayed the strongest associations to C8:0, which is a proxy for DNS. Pair-wise LD measure r for all NCOA6, or nuclear receptor coactivator 6, encodes an essential, non-redundant multifunctional coactivator for nuclear hormone receptors and certain other transcription factors [NCOA6 is essential for embryonic development [NCOA6 isoforms are expressed in the mouse mammary gland at different developmental stages including adult virgin, pregnancy, lactation and involution [ factors . The gen factors  and also factors . NCOA6 ielopment , it is ielopment , and is elopment . Differevolution .NCOA6 in milk fat synthesis. However NCOA6 is known to be a ligand for transcription factors such as PPARα and PPARγ [LPL, CD36 and ACSL1 [NCOA6 (in that study denoted PRIP) was identified as one of the factors involved in PPARα/RXRα signaling [NCOA6 could be a functional as well as a positional candidate for the QTL on BTA13.To the best of our knowledge, no studies have specifically investigated the role of nd PPARγ , and thund ACSL1 , and is nd ACSL1 . In a stignaling . TherefoOur study did not identify any candidate causal polymorphisms underlying the QTL. The three SNPs with the highest LRT are either synonymous or intronic and therefore do not directly alter the protein sequence. However, introns can harbor important regulating elements such as binding sites for transcription factors and sites that affect alternative splicing. Synonymous SNPs are also suggested to have important biological roles, as they may have an impact on critical cis-regulating sequences, alter mRNA structure and influence translational speed . FurtherACSS2), but subsequent refined analyses highlighted that ACSS2 had little effect and that SNPs within the nearby NCOA6 gene were responsible for the observed QTL. To date, the functional role of NCOA6 in milk fatty acid synthesis is unclear, but one possible effect could be that it is a ligand for the transcription factor PPARγ, which is suggested to be a major regulator of milk fat synthesis.Using a combined dataset of high-resolution genotypes and FTIR phenotypes, our GWAS detected significant QTL for milk fatty acids on BTA1, 13 and 15. On BTA13, the QTL for de novo fatty acid synthesis mapped close to a known candidate gene (


The rapid adoption of genomic selection is due to two key factors: availability of both high-throughput dense genotyping and statistical methods to estimate and predict breeding values. The development of such methods is still ongoing and, so far, there is no consensus on the best approach. Currently, the linear and non-linear methods for genomic prediction (GP) are treated as distinct approaches. The aim of this study was to evaluate the implementation of an iterative method  that incorporates aspects of both linear [genomic-best linear unbiased prediction (G-BLUP)] and non-linear (Bayes-C) methods for GP. The iterative nature of GBC makes it less computationally demanding similar to other non-Markov chain Monte Carlo (MCMC) approaches. However, as a Bayesian method, GBC differs from both MCMC- and non-MCMC-based methods by combining some aspects of G-BLUP and Bayes-C methods for GP. Its relative performance was compared to those of G-BLUP and Bayes-C.We used an imputed 50 K single-nucleotide polymorphism (SNP) dataset based on the Illumina Bovine50K BeadChip, which included 48,249 SNPs and 3244 records. Daughter yield deviations for somatic cell count, fat yield, milk yield, and protein yield were used as response variables.GBC was frequently  superior to G-BLUP and Bayes-C in terms of prediction accuracy and was significantly better than G-BLUP only for fat yield. On average across the four traits, GBC yielded a 0.009 and 0.006 increase in prediction accuracy over G-BLUP and Bayes-C, respectively. Computationally, GBC was very much faster than Bayes-C and similar to G-BLUP.Our results show that incorporating some aspects of G-BLUP and Bayes-C in a single model can improve accuracy of GP over the commonly used method: G-BLUP. Generally, GBC did not statistically perform better than G-BLUP and Bayes-C, probably due to the close relationships between reference and validation individuals. Nevertheless, it is a flexible tool, in the sense, that it simultaneously incorporates some aspects of linear and non-linear models for GP, thereby exploiting family relationships while also accounting for linkage disequilibrium between SNPs and genes with large effects. The application of GBC in GP merits further exploration. The rapid adoption of genomic selection (GS) is due to two key factors: 1) availability of high-throughput dense genotyping, and 2) availability of statistical methods to estimate and predict breeding values [ availabi availabiG-BLUP is commonly used for routine genetic evaluations because of its simple and less computationally demanding nature. Since Bayesian methods are often implemented by using MCMC algorithms, they are time consuming and computationally demanding when they deal with large numbers of SNPs. Hence, they are rarely used in routine genetic evaluations although they can potentially pick up and use SNPs with large effects or the actual causative variants. The need to reduce computational demands, while maintaining the features of Bayesian methods, has led to the development of iterative methods (non-MCMC-based Bayesian methods) such as the VanRaden’s non-linear A/B , fastBayG) exploits the relationship in a given population more comprehensively than the pedigree-based relationship matrix (A), both by quantifying the variation in relationships between sibs and the historical relationships between individuals in the base generation of A [GP uses two sources of information: genetic relationships among individuals and LD between SNPs and QTL , 13. Theion of A , 14, 15.ion of A , 13. ThuOur aim was to develop an iterative method (referred to as GBC) that combines relationship information using the G-BLUP approach with information on the LD between QTL and neighboring SNPs using the Bayes-C  approache; i.e. weighted number of daughters for each bull) were obtained from GENO SA (http://www.geno.no). These were extracted from the routine genetic evaluations of 2013 for three production traits, fat yield (Fkg), milk yield (Mkg) and protein yield (Pkg), and a health indicator, somatic cell count (SCC). The DYD is an estimate of the average performance of each bull’s daughters, corrected for all fixed and non-genetic random effects of the daughters and genetic effects of the bulls’ mates [e was 108 and the average de was 177 with a standard deviation of ~31. The reliabilities of the DYD were calculated following Fikse and Banos [K = (4 − h2) and h2 is the heritability of the trait used in the evaluations. The parameters used for each trait and average reliabilities for each trait are in Table Daughter yield deviations  on 3244s’ mates . The minnd Banos  as \docuGenotyping data were also provided by Geno SA for these bulls. Bulls were previously genotyped with different SNP chips: 2450 bulls with the 25 K Affymetrix chip , 1650 were genotyped with the Illumina Bovine50K BeadChip , and 856 were genotyped with both.http://www.cigene.no) and is described in detail by Solberg et al. [Quality control was carried out by CIGENE  using Beagle v3.3.1 [We used genotype imputation to obtain ~50 K SNP genotypes. The genotypes of bulls obtained with the 25 K Affymetrix chip were imputed to the SNP density of the Illumina Bovine50K BeadChip. Genotype imputation was performed by CIGENE  of the SNPs has zero effects and that the distribution of the effects for the other fraction (π) is normal [M is the design matrix of scaled SNP genotypes as in the calculation of G above; Q is a diagonal matrix with indicators on the diagonal that are 1 if the SNP has an effect (with prior probability π) and 0 if it has no such effect (with prior probability (1 − π); q is a vector of SNP effects (qj) assumed to be normally distributed, i.e. π and 0 otherwise. All other model elements are defined as previously. The π values used were estimated from the dataset via a search between 1% and then 5 to 30% in increments of 5% to obtain the optimal π values. The GEBV for the validation animals was calculated as Mv describes the scaled genotypes for each bull in the validation set, and Bayes-C, a sub-model of GBC (i.e. where the variance explained by the GBLUP term in GBC is set to zero), was also independently evaluated so that the relative performance of both approaches can be compared. Bayes-C assumes that a fraction ); and LRj is the likelihood ratio that SNP j has a large effect. The log(LRj) equals the log-likelihood of a model with versus without the effect of SNP j (see Appendix for a derivation):mj are the scaled genotypes of SNP j for animals with records; y* are the records corrected for all other effects in the model except that of SNP j; PEV is the prediction error variance matrix of the G-BLUP model; and the The posterior probability that a SNP he model .j now becomes:The effect of SNP r), calculated as the correlation between GEBV and DYD, divided by the square root of the average reliability of the DYD for the trait The primary criterion for evaluating predictive ability was the accuracy of the predictions  gene involved in fat percentage in dairy cattle [The GBC method has some similarity with Bayes-A , i.e. boy cattle . The GBCGBC, as mentioned earlier, simultaneously fits Bayes-C and G-BLUP components. This means that all SNPs are included twice in the model, first in the Bayes-C term, and second in the G-BLUP term. The G-BLUP term includes all the SNPs to explain genomic relationships between the animals, whereas the Bayes-C term answers the question whether a SNP might explain more variance than expected based on its contribution to genomic relationships . Thus, GBC opens the opportunity for SNPs with extra-large effects to be included twice in the model and thereby increasing their weight in the GP.The regression coefficients in Table π) of the SNPs and a normal distribution of effects for the other fraction (π) [π of the SNPs have a large effect with a variance of 0.001 A key difference between Bayes-C and GBC lies in how they estimate and deal with the effects of SNPs. Bayes-C assumes a priori zero effects for a fraction (1 − tion (π) . GBC fitediction  but theientclass1pt{minimaediction –32. In tThere was a general tendency that GBC allocated large effects to (very) few SNPs while Bayes-C identified many more SNPs with moderate to large effects. This implies that, with GBC, only SNPs in high LD with the QTL tend to pick up the genes with major effects while the others are treated as residual SNP effects. Bayes-C also needs to capture SNP genetic relationships by fitting SNPs with large effects, and thus needs to fit more SNPs. Therefore, the observed differences in prediction accuracies between both methods are a reflection of how genomic regions with large and small effects are treated. The ability of GBC to not neglect any SNP effect may explain why it tended towards higher accuracies than Bayes-C. It seems that, the GBC method is also very precise in pointing towards QTL locations. This could be due to GBC showing some similarity to GWAS methods in which a single SNP and a G-BLUP term are fitted, with the G-BLUP term correcting the QTL signal for family relationships. Although the prior distribution of the effects of SNPs and the actual proportion of variance they explain remain unknown, the results of this study indicate that the assumed prior distribution for the effects of SNPs alongside the proportion of variance they explain in GBC tends to yield somewhat higher accuracy than the assumptions underlying Bayes-C.In this study, we assumed that, across the four traits studied, the SNPs with a large effect explained 0.1% of the genetic variance in GBC. This corresponds to the genetic variance explained by the third distribution in Bayes-R . Bayes-ROn the one hand, GBC shares some similarity with other non-MCMC-based Bayesian methods in the sense that it uses an iterative approach. A key advantage of the iterative methods (non-MCMC-based methods) over MCMC-based methods is their shorter computing time. Non-MCMC-based Bayesian methods such as fastBayesB , MixP 1, emBayesWe introduced and evaluated the GBC method for GP, which simultaneously fits G-BLUP and Bayes-C terms. The method was evaluated by using imputed 50 K SNP datasets and its relative performance was compared to G-BLUP and Bayes-C. GBC showed marginal advantages over G-BLUP and Bayes-C for most of the traits in terms of prediction accuracy. For Fkg, GBC performed significantly better than G-BLUP, which agrees with the fact that Fkg is controlled by a few genes with a large effect. Overall in our study, statistically, GBC did not significantly outperform G-BLUP and Bayes-C probably due to a high level of relationship between reference and validation individuals. However, it is a flexible tool in the sense that it simultaneously incorporates some aspects of both linear and non-linear models for GP, thereby exploiting family relationships while also accounting for LD between SNPs and genes with a large effect. Computationally, GBC was much faster than Bayes-C with a computational speed that is comparable to that of G-BLUP. The application of GBC in GP merits further exploration.


Sudan is endowed with a variety of indigenous goat breeds which are used for meat and milk production and which are well adapted to the local environment. The aim of the present study was to determine the genetic diversity and relationship within and between the four main Sudanese breeds of Nubian, Desert, Taggar and Nilotic goats. Using the 50 K SNP chip, 24 animals of each breed were genotyped.IS) did not differ from zero. Fst coefficients for the genetic distance between breeds also did not significantly deviate from zero. In addition, the analysis of molecular variance revealed that 93% of the total variance in the examined population can be explained by differences among individuals, while only 7% result from differences between the breeds. These findings provide evidence for high genetic diversity and little inbreeding within breeds on one hand, and low diversity between breeds on the other hand. Further examinations using Nei’s genetic distance and STRUCTURE analysis clustered Taggar goats distinct from the other breeds. In a principal component (PC) analysis, PC1 could separate Taggar, Nilotic and a mix of Nubian and Desert goats into three groups. The SNPs that contributed strongly to PC1 showed high Fst values in Taggar goat versus the other goat breeds. PCA allowed us to identify target genomic regions which contain genes known to influence growth, development, bone formation and the immune system.More than 96% of high quality SNPs were polymorphic with an average minor allele frequency of 0.3. In all breeds, no significant difference between observed (0.4) and expected (0.4) heterozygosity was found and the inbreeding coefficients  contains supplementary material, which is available to authorized users. Compared to other African countries, goats in Sudan constitute a large part of the livestock population. With an estimated number of 31 million goats (out of 365 million in whole Africa) that produced about 1.532 million tons of milk in 2013, Sudan was the largest producer of goat milk in Africa and the third largest producer in the world ; the version used is available as supplemental file . All animals were genotyped with the Goat SNP52 BeadChip , developed by the International Goat Genome Consortium (IGGC) . The rawThe R language for statistical computing v3.2.3 was used for quality control of the called SNP data . From tho) and expected heterozygosity (HE) for each breed of goats and the total population were analyzed using the diveRsity R package v.1.9.89 [IS) using Sewall Wright’s method [st) were also calculated using the diveRsity package. To detect the level of genetic variation among samples within populations and among populations at different hierarchical levels, the Analysis of MOlecular VAriance (AMOVA) [In order to assess the genetic diversity, the minor allele frequency, distribution and proportion of polymorphic SNPs were computed using the R language for statistical computing v3.2.3 . To measv.1.9.89 . The aves method  and the  (AMOVA)  was perf (AMOVA) . StAMPP  (AMOVA)  to exploTo measure the genetic distance between the four goat populations, hierarchical clustering of SNP data was performed. Pairwise Nei’s genetic distances  between Population structure was determined by using a model-based clustering for assigning individuals from multi locus genotypes to a population using the STRUCTURE 2.3.3 software suite –33. STRUAs a different approach to characterize divergence, the genetic relationship between animals was analyzed by principal component analysis (PCA) using the ‘prcomp’ function in the R language. PCA is a statistical method for exploring and interpretation of big data by reducing the dimension of the data to the few principal components which capture the majority of the genetic variation observed in the genotypes. We visualized the contribution of individuals to the first 10 principal components; from our data we observe that a combination of PC1 and PC2 shows a good separation between different goat breeds. As such we investigate which SNPs allow PCA to separate between the four populations of Sudanese goats; we investigated which SNPs contribute highly to PC1.2) was calculated as the squared variable correlation. Afterwards, we summed up all the cos2 values for PC1 and expressed each SNP contribution as percentage of total variation. We then looked for SNPs which have more than 10 times the expected contribution to PC1 . These SNPs will capture most of the differentiation among breeds separated by PC1. We extracted genes within a region of 500 kb cis-window around these high contributing SNPs according to SNP Annotation and Proxy Search (SNAP) [To select high impact SNPs we calculated the variable correlations with PC1 by multiplying the loading factors with the component standard deviations. Quality of representation for all variables on the factor map (cosh (SNAP) .st, STUCTURE, clustering and PCA we observe that Taggar goats show significant genetic differences compared to the other breeds.The main result from this study is that Taggar goats show significant genetic differences from the other three goat breeds. In all our analysis e.g. FThe percentage of polymorphic SNPs within each breed among all 48,505 SNPs that passed the quality control was very high, ranging from 96.9% (Taggar goat) to 98.2% (Desert goat) Table . After rIS). The population inbreeding coefficients (FIS) ranged from −0.0129 in Taggar goats to 0.0094 in Nilotic goats and did not significantly differ from zero . For Nilotic goats, the expected heterozygosity 0.40) was slightly higher than observed (0.39). The high degree of heterozygosity and the coincidence of expected and observed heterozygosity indicate high genetic diversity and near to zero inbreeding within each breed. This was further confirmed by the inbreeding coefficients (F0 was slist) were low and varied between 0.0053 (Nubian vs. Desert) and 0.0229 (Nubian vs. Taggar) . Only the Taggar breed showed significant genetic differentiation towards the three other breeds. However, pairwise Fst values for the other breeds did not significantly deviate from zero. As such Fst analysis only provides evidence for population differentiation of Taggar goats versus the other three goat breeds, and no such evidence for population differentiation between the Desert, Nilotic or Nubian. We investigated the SNP by SNP Fst values  was attributable to the variation within individuals .The estimates for pairwise genetic differentiation between populations  Fig. . Within P(D)) of the data indicated the most optimal K value to be 2  + 2 SDs. This means that these SNPs show signs of differentiation in Taggar goats versus the other breeds. This is not observed for any of the other three goat breeds. Desert goats versus the other breeds showed only 5 SNPs above threshold, Nilotic goats versus the other breeds showed no SNPs above the threshold, and Nubian versus the rest showed 8 SNPs above the threshold, this means that the regions contributing to PC1 are regions of high differentiation in Taggar goats, but are not regions of high differentiation in the other Sudanese breeds.To further investigate our results that SNPs contributing highly to PC1 are biologically important in the distinction between Taggar goats versus the other Sudanese breeds. We explored this by investigating if these SNPs show higher than average differentiation. We used the Fst values in Taggar versus the other goats, we selected genes from a region of 500 kb around those SNPs. We extracted 208 genes Bone formation , sodium/potassium/calcium exchanger (solute carrier family 24 member 5)Blood  homeostasis Heart and muscle Growth / dwarfism Eye development Coat color  is not available for goats, so we are unable to perform GO testing on these groups of genes. Their location around the SNPs that contributed highly to PC1 and the high FThis study was conducted to contribute to the genetic characterization of the economically most interesting indigenous goat breeds in Sudan. Using the 50 k goat SNP chip, we examined the genetic diversity within and between breeds, the breed differentiation, and the genetics contributing to breed differences.IS = 0.001) was consistent with previous findings for Nubian goats in Ethiopia (FIS = 0.073) [High levels of genetic diversity within the examined goat populations were observed. More than 96% of SNPs that passed the quality check were polymorphic in each breed albeit SNPs on the chip were selected from breeds such as Saanen, Alpine, Creole, Boer, Kacang, and Savanna and did not consider indigenous East African breeds . Since t= 0.073) . This oust values among Sudanese breeds indicate low genetic differentiation among these populations, which in turn mirrors the population history with a likely common origin and the recent husbandry system in connection with nomadic traditions. AMOVA further supported this finding by providing evidence that most variation was distributed within individuals and to a lesser extent genetic variation (6.96%) was explained by differences among the Sudanese goat populations.The low FBy applying different genetic distance clustering methods, STRUCTURE and PCA, we observed clear separation of Taggar goats towards the other goat breeds. Taggar, which is a dwarf goat, and was identified as the most genetically distinct group in respect to other goat populations in this study. The output of the STRUCTURE analysis at K = 2 further supported this finding. It clearly distinguished Taggar from the other Sudanese goat breeds. This finding could be explained by the fact that Taggar goats are geographically isolated in the mountain regions of Sudan. Mountain regions could have caused natural selection of small animals which are more nimble and feed efficient, which over the course of many generations could have led to the dwarfism phenotype we observe currently in Taggar goats. This natural selection might have caused genetic signatures in Taggar not observed in the other three breeds. Additionally, since Taggar goats are geographically separated (mountain versus low-lands) this might have played a limiting role in their ability to mate with the other three low-land goat breeds in Sudan, thus explaining why they show a clear separation from the other goat breeds.It is observed that Desert goats are most scattered amongst the other two goats (Nilotic and Nubian). This could be due to Desert goat husbandry: (1) forced by changing market conditions, they began to shift from dual purpose to dairy goats by crossing their Desert goats with Nubian goats , and (2)Based on Nei’s genetic distance we find a closer relationship between Taggar and a sub-group of Nilotic goats, which could be attributed to the low geographical distance between the two populations in the mountain area of the Southern part of Sudan.Principal component analysis showed that the first two principal components could be used to differentiate between Sudanese goat breeds and to assign individuals to a particular breed. PC1 shows separation of Taggar goats, Nilotic goats, and a mixed of Desert and Nubian goats. PC2 seems to be able to differentiate between Nubian and Desert goats, though some misclassifications still remain. SNPs contributing highly to PC1 allowed us to define regions of the genome at which the Taggar goats are significantly different from the other three breeds. Among the genes in the vicinity of the SNPs contributing to PC1, we identified genes which might be interesting candidates when looking at breed characteristics and adaptation differences between Sudanese breeds studied here. Among them we found genes known to contribute to bone formation, blood homeostasis, heart and muscle development, growth, eye development, and coat color.sclerostin and bone morphogenetic protein receptor type-1A precursor known to have an effect on the bone morphology and formation. Studying differences in these genes could elucidate the underlying genetics of the differences between Sudanese goat breeds in regard to bone formation and body measurements characteristics. The Stanniocalcin-1 gene on chromosome 8 is one of the genes, which is interesting for the discrimination among breeds, since Taggar goats are a species of achondroplastic dwarf goats with lack of ossification at the cartilage joints [Stanniocalcin-1 acts as a paracrine regulator of growth plate chondrogenesis [Within the list of the genes there are genes such as e joints , 14. Staogenesis . This geogenesis .Several other genes that are important for blood metabolism and homeostasis fell within regions in the PC1. This might reflect the adaptation of the Sudanese goat breeds to different environmental conditions. An example is the Desert goat which can survive in the areas where the lack of water resources is dominant. In a similar way, we detected candidate genes in regions which differ between the goat breeds for heart and muscle characteristics. Since natural variation is present between the breeds differences in these genes could provide targets for genomic breeding to improve meat quality.The detection of genomic differences based on the principle component 1 (PC1) which allows for the separation of the Sudanese goat breeds can shed light on the genes underlying the adaptations. Natural variation present between these breeds provide a unique opportunity to improve local breeds through breeding using genomic markers or to breed improved resistance to harsh climates into imported high production breeds. Therefore, further research is required to identify the genomic regions which are associated with different important economical traits in Sudanese goat breeds.st values of these SNPs show high differentiation for Taggar goats, but no significant differentiation for the other breeds under study. Genes in the proximity of these SNPs contributing highly to the first principal component might be interesting candidates when looking at breed characteristics and adaptation differences in Taggar goats.Based on our genome-wide analysis of SNPs in Sudanese goats, this study shows that Taggar goats show significant genetic differences from the other three breeds studied. We further conclude that the first principal components allow us to differentiate between Sudanese goat breeds. Furthermore FAdditional file 1: Table S1.Sample locations. (DOC 44 kb)Additional file 2:Locations of protein coding genes. (TXT 3463 kb)Additional file 3: Figure S1.Reynolds and Manhattan distances. (PNG 40 kb)Additional file 4: Figure S2.Kinship. (PNG 71 kb)Additional file 5: Figure S3.st (PNG 52 kb)FAdditional file 6: Table S2.AMOVA. (DOCX 11 kb)Additional file 7: Table S3.STRUCTURE. (DOC 33 kb)Additional file 8: Figure S4.STRUCTURE analysis of Sudanese goat breeds. (PNG 102 kb)Additional file 9:SNPs and genes contributed to PCA. (XLSX 36 kb)Additional file 10:Raw genotype data obtained from the Goat SNP52 BeadChip. (XLSX 18918 kb)


LD values between adjacent marker pairs ranged from 0.135 to 0.194 and from 0.568 to 0.650 for r2 for |D′| across all chromosomes. The average r2 between all pairwise SNPs on each chromosome was 0.018. SNPs separated by between 0.10 to 0.20 Mb had an estimated average r2 equal to 0.1033. The identified haplotype blocks consisted of 2 to 21 markers. Moreover, estimates of average coefficients of inbreeding and effective population size were 0.04 and 96, respectively. LD estimated in this study was lower than that reported in other species and was characterized by short haplotype blocks. Our results suggest that the use of a higher density SNP panel is recommended for the implementation of genomic selection in the Santa Inês breed.For genomic selection to be successful, there must be sufficient linkage disequilibrium between the markers and the causal mutations. The objectives of this study were to evaluate the extent of LD in ovine using the Santa Inês breed and to infer the minimum number of markers required to reach reasonable prediction accuracy. In total, 38,168 SNPs and 395 samples were used. The mean LD between adjacent marker pairs measured by r This approach has been shown to increase the rate of genetic gain when pedigree-based selection is suboptimal1, which is the case for lowly heritable traits. For instance, GS based on simulated data showed an increase in reliability of breeding values for young animals when using genomic (r2 > 60%) versus parent average (r2 = 32%) information, equivalent to approximately 20 offspring2. Furthermore, genetic gain can be increased using genomic information by shortening the generation interval1. Alternatively, genetic markers scattered across the genome offer an opportunity to conduct genome-wide association studies (GWAS) to characterize genes underlying genetic variation for traits of interest.Genomic information is currently used in animal breeding programs to enable selection for difficult to measure traits, increase the overall rate of genetic gain, and to improve the understanding of genetic and biological causes underlying phenotypic variation. Genomic selection (GS) is an approach which uses genome-wide markers simultaneously to predict breeding values3 because generally only the markers are observed and the casual mutations are unknown. The LD between a marker and a causal mutation can be considered as the proportion of causal mutation variance that can be captured by the marker variance5. Through the knowledge of the degree of LD, it is possible to define the density of genetic markers necessary to achieve a certain accuracy of prediction and to determine when the estimates of genetic marker effects should be updated. It has been well documented that simply increasing marker density does not improve prediction accuracies. Although increased marker density improves resolution, it can also decrease power and add noise to the analyses by the use of non-informative SNP. Furthermore, increased marker density can dilute individual marker effects if, for example, two markers are associated with the same QTL and the two markers are in high LD with each other.The success of GS and GWAS are dependent on linkage disequilibrium (LD) or gametic disequilibrium between the markers and causal mutations6, and it is commonly represented by |D′| and r2 metrics7. The extent of LD can vary between and within species due to evolutionary history and population structure mainly characterized by insertions, deletions, chromosomal rearrangements, or inversions4. This association between markers and causal mutations may change overtime due to recombination and selection4 necessitating the re-estimation of marker effects.LD is defined as a non-random association between alleles at different loci14. Nevertheless, there are few studies that report LD estimates for Brazilian Santa Inês sheep using SNP. Ovine populations have retained a relatively high level of genetic diversity, unlike bovine, which justify the importance of LD mapping in many breeds within species15. Moreover, LD estimates between different breeds can be informative relative to the overall diversity level in a species and the selection level applied to them.Estimates of LD have been reported in ovine for some domestic pure and crossbred populations, as well as in wild sheep by using microsatellites and SNP markerset al.16 mentioned the importance of studies focused on breeds as a chance to identify variation and understand the biological mechanisms that enable these breeds to survive in different local environments.Therefore, the aim of the current study was to characterize LD structure in Brazilian Santa Inês sheep for the first time, given its commercial importance for meat production, reproductive efficiency, and tropical adaptation in Brazil, and compare the LD observed in the Santa Ines breed with other breeds. Beynon 17 and the accuracy of genomic estimated breeding values using different marker panel densities in sheep20. The appropriate panel density could be specific to each species and breed depending on overall LD structure. Unfortunately, the current genotyping costs in sheep are greater than the economic value of breeding animals21. Consequently, we also aimed to provide an estimate of the marker density required for genomic studies in the Santa Inês breed.Many studies have evaluated imputation accuracyet al. in Spanish Churra sheep (0.06 Mb)14. SNPs were evenly distributed throughout the genome as the distances between adjacent markers ranged from 0.064 to 0.085 Mb. The chromosomes differ in size and SNP quantity, with chromosome 24 being the smallest in size - OAR24 (44.21 Mb). Liu et al.14 observed a similar behavior considering the same SNP panel (OAR24- 44.85 Mb), with OAR24 being the smallest chromosome (44.85 Mb) whereas the OAR2 was the largest (263.11 Mb). The number of SNPs per chromosome was proportional to the size of each chromosome. Descriptive statistics of the SNP and LD (r2 and |D′|) for each chromosome are presented in Table After quality control (QC), 38,168 autosomal SNPs remained comprising approximately 53% of the entire panel. The SNPs retained after QC spanned a total of 299.63 megabases (Mb) of the genome, with a mean (standard deviation) distance between adjacent SNP of 0.07 (0.075) Mb. This value was close to that obtained by Liu 22. Extending our comparison to other species, the mean MAF was relatively higher than those found for Bos taurus indicus, with values ranging from 0.19 to 0.2524. The MAF is important because LD, independent of the metric used, is a function of allelic frequency. In general, low MAF may correspond to a larger difference in allele frequency of coupled alleles, which can result in lower estimates of LD as measured by either r2 or |D′|25. Consequently, applying QC and the choice of QC criteria can affect the distribution and extent of LD6.In addition, 35% of the SNPs  had minor allele frequency (MAF) lower than 0.20, with a mean MAF over all SNPs of 0.35. According to another sheep study, 33% of the SNPs had MAF lower than 0.20Ne) were estimated for all chromosomes together and for each chromosome separately, using genomic information. The estimate of F was 0.04, a relatively low coefficient for a population that originated from the same commercial herd. Using pedigree information to estimate the inbreeding coefficient, Pedrosa et al. found values equal to 0.02 in the Santa Inês breed26. Al-Mamun et al. found average inbreeding coefficients for Merino, Border Leicester and Poll Dorset equal to −0.013, 0.09 and 0.02, respectively13. A recently published study in ovine found average inbreeding coefficients based on excess of homozygosity (standard deviation- SD) of −0.008 (0.031), ranging from −0.079 to 0.30112. Compared with Kijas et al.11 and Liu et al.14, the F estimated from the Santa Inês breed was lower. Negative inbreeding coefficients occur when the number of observed homozygous loci is lower than the expected, suggesting that the population is more heterogeneous than expected, perhaps due to the composite nature of the breed.For a better understanding of the population described in this study, inbreeding coefficient (F) and effective population size . Prieur et al. evaluated three different methods to transform the genetic distance in ovine, and concluded that the estimation process using CRIMAP software (v2.503) was more accurate27. However, Prieur et al. also verified that the ranking for r2 and Ne between breeds were not affected by the method used and mentioned that the LD estimator was not different between methods27.In the Ne estimated herein was 96 in the current generation. Kijas et al.15 observed Ne equal to 520 in the Brazilian Santa Inês breed, however, in their study only 47 animals were used. Pedrosa et al. also estimated Ne using pedigree information and found a relatively low value (76) in Santa Inês26. These differences in Ne can be due to the number of animals used  and the source of relationship information (genomics vs. pedigree). Al-Mamun et al. found values of Ne ranging from 140 (Border Leicester breed) to 348 (Merino breed)13. Brito et al.12 found values of Ne in the most current generations in multi-breed sheep populations ranging from 125 to 974. Using a Spanish Churra sheep population, García-Gámez et al.28 and Chitneedi et al.29 estimated Ne equal to 159 and 83, respectively.The Ne over the generations. In this study, Ne ranged from 1,705 to 28,191 between 16 and 296 generations, respectively, before the current generation. Mastrangelo et al. estimated the Ne at 295 generations ago to be 747 animals in Barbaresca sheep30. Liu et al. observed Ne equal to 4,472 and 160 at 2,000 and 5 generations ago, assuming that one Mb is equivalent to one cM14. Brito et al.12 reported estimates of effective population size of 5,537 animals 1,000 generations ago to 687 in the most recent generation. We hypothesize that the large difference in Ne between the current and historic generations could be because the breeds that comprise the composite breed of Santa Inês were divergent historically and, thus, these estimates include multiple divergent breeds. The Santa Inês breed is relatively new, having only begun in the 1950s by non-systematic crossing of the Brazilian Somali, Bergamasca and Morada Nova breeds31. This illustrates that the large estimates of historic Ne reflect time points before the formation of the breed, and even before the domestication of ovine.The presence of artificial selection in the population under study was verified through the reduction of Ne for each chromosome. Chromosome 6, OAR6, exhibited the smallest Ne, which was in contrast to the results of Liu et al. that reported the smallest Ne for OAR1014.We also estimated the 2 and |D′| values estimated between adjacent SNPs from the 26 autosomal chromosomes were 0.166 (0.2189) and 0.617 (0.3349), respectively. Using the dairy sheep breed Frizarta, Kominakis et al. estimated r2 and |Dʹ| equal to 0.18 and 0.50, respectively, at an average inter-marker distance of 0.031 Mb32. Mastrangelo et al. observed average r2 (SD) in Sicilian sheep equal to 0.155 (0.2040)33. Al-Mamun et al. also reported LD estimates from multiple domesticated sheep (Ovis aries) breeds including: Merino (MER), Border Leicester (BL), Poll Dorset (PD) and crossbred populations  and MxB crossed to Poll Dorset (MxBxP)). The authors used the same genotype panel but adopted a different data quality control (MAF < 0.01) and reported a mean r2 of 0.12 (MER), 0.20 (BL), 0.19 (PD), 0.13 (MxB) and 0.13 (MxBxP); and mean |D′| of 0.52 (MER), 0.72 (BL), 0.69 (PD), 0.54 (MxB) and 0.55 (MxBxP)13. In the Barbaresca sheep breed, the mean r2 across autosomes was 0.215, with an average distance between adjacent SNP pairs of 0.063 Mb30.The average (SD) r2 of 0.26 (0.100)12. The estimates of r2 are relatively consistent across sheep populations, with the exception of larger r2 values reported by Brito et al. Nevertheless, we should consider that the distance between markers was much shorter in Brito et al. than herein (4.74 kb versus 70 kb in the present study), which can be one reason for the increase in r2. Additionally, Brito et al. reported LD levels less than 0.10 for SNP located more than 0.04 Mb apart12. A recent study from Michailidou et al. observed a mean r2 equal to 0.121, 0.098, and 0.092 in Boutsko, Chios, and Karagouniko, respectively, with the average intermarker distance 0.27 Mb for all breeds34.A study published with multi-breed sheep reported mean (SD) r2) and 0.69 (|D′|) were estimated from the Australian Holstein-Friesian cattle population using 9,195 SNP with the mean SNP distance equal to 0.25 Mb6. The mean r2 for pigs of Landrace , Yorkshire , Hampshire  and Duroc  breeds were 0.36, 0.39, 0.44, and 0.46 estimated from 40, 144, 39, 110, 32, 370 and 34,129 SNP spaced at average distances of 0.06, 0.06, 0.07, and 0.07 Mb, respectively35.Sheep populations have been associated with lower levels of LD in comparison to other ruminant and nonruminant species. Although the comparison between species is difficult due differences in genome size as well as the quality control applied, mean values between adjacent SNPs of 0.32 (r2 and 0.568 (0.3391) to 0.650 (0.3368) for |D′|  between adjacent SNP within the same chromosome ranged from 0.135 (0.1972) to 0.194 (0.2423) for r2 and |D′| estimated between all pairwise SNPs on the 26 autosomal chromosomes were 0.018 (0.032) and 0.225 (0.213), respectively. In a study which used microsatellite markers to evaluate LD using chromosomes 1–10 of domestic sheep (Ovis aries) with mean distance between markers ranging from 10 to 40 Mb, a mean (SD) value of 0.211 (0.004) for |D′| was estimated10. Al-Mamun et al. who also used domesticated sheep (Ovis aries), found mean r² between all pairwise SNPs (0.05 Mb mean distance) of 0.007 (MER), 0.013 (BL), 0.018 (PD), 0.009 (BxM) and 0.012 (BxMxP); and mean |D′| of 0.168 (MER), 0.29 (BL), 0.27 (PD), 0.18 (BxM) and 0.19 (BxMxP)13. Additionally, Miller et al. using non-domesticated sheep  and the same genotype panel but adopting a different QC (MAF < 0.10), reported a mean r2 (SD) of 0.042 (0.067)4. Considering the confidence interval obtained for the estimates presented in this study as well as in the studies previously reported, it is possible to assume that estimates of r2 and |D′| across all SNP combinations on a chromosome are relatively consistent across sheep populations.The average (SD) for r2 and |D′|, respectively, as a function of the intermarker distance for chromosomes 1 and 24. Supplementary Fig. 2 and |D′|, respectively, for the other chromosomes. Overall, the relationship between LD and intermarker distance suggest that as intermarker distance decreases, LD increases. A notable exception is chromosome 1. On this chromosome, r2 presented secondary high peaks around the interval from 100 to 150 Mb  across all chromosomes. Zhao et al. observed r2 values equal to 0.044, 0.132 and 0.158 in Sunite, German Mutton Merino and Dorper sheep, respectively, in the same marker distance interval36. Additionally, García-Gámez et al. observed r2 equals to 0.086 for SNP also within the same marker distance interval in a Spanish Churra sheep population28. Similarly, Chitneedi et al. observed the average of 0.066 for r2 in Spanish Churra sheep using the high-density imputed genotypes29.The average (SD) ret al., Table 2 ≤ 0.16), medium LD (0.16 < r2 < 0.70), and high LD (r2 > 0.70)24. Higher levels of r2 (greater than 0.70) were found at distances between markers smaller than 0.768 Mb with 3,296 combinations of SNPs . For medium levels of r2 (0.16 to 0.70), distances lower than 5.277 Mb were observed with 273,659 combinations of SNPs (0.849%). Considering low levels of r2 (lower than 0.16) distances found were higher than 15.110 Mb with 31,939,376 combinations of SNPs (99.140%).Using LD categories defined by Espigolan 2, |D′|, MAF, F, and Ne are reported in Table 2 as reported by Zhao et al.37, both LD metrics exhibited the same behavior (Table 2 and |D′|) are related to the weight applied to the allele frequencies. Given |D′| is entirely dependent on the frequency of the alleles, |D′| possibly inflates LD estimates37. On the other hand, the r2 proposed by Hill and Robertson7 aims to reduce this frequency dependence.The relationships between ror Table . This is7, LD (numerator of r2) and F have a linear relationship as shown in the equation below7. In a population under selection, the number of homozygotes tends to increase for many favorable alleles. Consequently, the inbreeding coefficient and LD between these selected alleles increase7.2, 2 estimated by equation  when estimating the correlation between D2 estimated by F  and 38:The expectation of D at generation 2 and effective size (Ne) is expected. Considering Ne as an indicator of selection, lower Ne values are a result of high selection pressure, and consequently a reduction in the number of breeding animals and genetic diversity. A negative relationship between average LD between all pairwise SNPs on a chromosome and Ne was observed (−0.16), as expected. However, the correlation between average LD between adjacent SNPs and Ne was positive (0.35). One potential reason for the observed discrepancy is the fact that Ne was estimated based on the LD between all pairwise SNPs rather than LD between adjacent SNPs. For instance, Lindblad-Toh et al. also observed that the effective population size and the inbreeding coefficient were reduced during dog domestication, resulting in a decrease of LD39.A negative correlation between D, which is the numerator of |D′|, and both r17. The average distance (SD) between markers that formed the haplotype blocks was 0.04 (0.033) Mb. Considering the size of the sheep genome and the average distance between SNP that formed the haplotype blocks, it was possible to indirectly infer the minimum number of markers needed for genomic analyses, which was 61,415 SNPs. However, due to the high standard deviation of the distance between markers that formed the haplotype, it is important to use this number with caution.The construction of haplotypes with only two  to twenty-one (frequency = 1) markers was consistent with the low LD among pairwise SNP reported in this study. The mean size of haplotype blocks and the frequency of the number of SNPs for each chromosome are reported in Table Ovis canadensis and Ovis dalli) and they were lower than the estimates reported in other species. The findings reported in this study will be useful to provide a theoretical reference in determining the number of markers needed for future GS and GWAS in Santa Inês sheep.The extent of LD among adjacent markers for the Santa Inês breed resembled those of previously reported results in other breeds of domesticated sheep. The mean LD values between all SNP pairs on each chromosome were consistent with domestic and wild sheep .40. The G matrix was constructed by using the PREGSF90 software in the BLUPF90 package43. The average relationship between animals (SD) was 0.001 (0.0634), with minimum and maximum values equal to −0.135 and 0.934, respectively. The hierarchically clustered heatmap of the G matrix was constructed using the gplots R package44 and is presented in Fig. 45, although they include full-sib and half-sib relationships. Inside the most defined diagonal block, for example, 13 full-sib animal pairs and 350 half-sib animal pairs are represented. In the population as a whole, there are one twin animal pair, 38 full-sib animal pairs and 3,089 half-sib animal pairs. The structure of this population can be observed by a distribution printed into the left of Fig. To characterize the Santa Inês sheep population, the relationship between animals was estimated using a genomic relationship matrix, G, as described in VanRaden (2008)Longissimus dorsi muscle collected from the left hemi-carcass and stored in 2.0 milliliter (ml) Eppendorf tubes. DNA extraction was performed according to protocols for lysis buffer and RNase. A high-density SNP panel (Illumina High-Density Ovine SNP BeadChip®) containing 54,241 SNP was used for genotyping. Chromosomal coordinates for each SNP were obtained from the ovine genome sequence assembly, Oar_v3.1.DNA was extracted from tissue samples of the 46 for LD analyses47. The PREGSF90 interface of the BLUPF90 program43 was used to edit the genomic data for F, Ne, MAF, and haplotype analyses. SNPs with a call rate lower than 0.90, MAF lower than 0.05 and p-value lower than 0.1 for the Hardy-Weinberg Equilibrium Chi-square test were excluded. One sample with a call rate lower than 0.9 was also removed. Table Quality control (QC) of the genomic data was performed by the GenABEL R package48. This is given byInbreeding coefficient (F) was calculated as a function of the expected and observed homozygote difference by using the PLINK softwareiih animal; iih animal, 48.where Ne) was obtained by the SNeP software49. This software provides a history of the effective population size, that is, the number of past generations based on the relationship between Ne, linkage disequilibrium represented by r2, and recombination rate (c) by using the following equation50.Effective population size  1, and B the allele of the marker 2, and the expected frequency of each allele separately. |D′| parameter ranges from 0 to 1 and it is given by55:The estimation of LD was performed in two ways for each chromosome: (1) between neighboring pairs of SNPs (adjacent SNPs) and (2) pairwise combination of all SNPs (pairwise SNPs) using the function LD in the R package genetics56.Here 2, is expressed as7:The squared correlation between the markers, given by rwhere 2 between adjacent SNPs; and (4) r2 between all pairwise SNPs.In total, four LD estimates were obtained: (1) |D′| between adjacent SNPs; (2) |D′| between all pairwise SNPs; (3) ret al.57 which was implemented via PLINK48. Blocks were partitioned according to whether the upper and lower confidence limits on estimates of pairwise |D′| measure fall within certain threshold values. The desired SNP panel density was estimated by the ratio of the megabase pair over the entire ovine genome and distance between markers that composed the haplotype blocks.The haplotype blocks were identified by following the approach suggested by Gabriel Data are available on request.All experimental procedures involving sheep were approved by the Institutional Animal Care and Use Committee Guidelines from School of Veterinary Medicine of University Federal of Bahia – UFBA and sanctioned by the president Prof. Claudio de Oliveira Romão (n° 07/2016). All experiments were performed in accordance with relevant guidelines and regulations.Supplementary figures and supplementary tables


Next-generation sequencing technologies provide opportunities to ascertain the genetic basis of phenotypic differences, even in the closely related cultivars via detection of large amount of DNA polymorphisms. In this study, we performed whole-genome re-sequencing of two mei cultivars with contrasting tree architecture. 75.87 million 100 bp pair-end reads were generated, with 92 % coverage of the genome. Re-sequencing data of two former upright mei cultivars were applied for detecting DNA polymorphisms, since we were more interested in variations conferring weeping trait. Applying stringent parameters, 157,317 mutual single nucleotide polymorphisms (SNPs) and 15,064 mutual insertions-deletions (InDels) were detected and found unevenly distributed within and among the mei chromosomes, which lead to the discovery of 220 high-density, 463 low-density SNP regions together with 80 high-density InDel regions. Additionally, 322 large-effect SNPs and 433 large-effect InDels were detected, and 10.09 % of the SNPs were observed in coding regions. 5.25 % SNPs in coding regions resulted in non-synonymous changes. Ninety SNPs were chosen randomly for validation using high-resolution melt analysis. 93.3 % of the candidate SNPs contained the predicted SNPs. Pfam analysis was further conducted to better understand SNP effects on gene functions. DNA polymorphisms of two known QTL loci conferring weeping trait and their functional effect were also analyzed thoroughly. This study highlights promising functional markers for molecular breeding and a whole-genome genetic basis of weeping trait in mei.The online version of this article (doi:10.1007/s11105-016-1000-4) contains supplementary material, which is available to authorized users. Prunus mume Sieb. et Zucc., 2n = 2x = 16) is a popular ornamental plant widely cultivated in East Asia. Being an important member of the genus Prunus and a species domesticated for over 3000 years, it plays a pivotal role in phylogenic studies of the Rosaceae. Besides, it owns many highlighted ornamental features, such as colorful corollas, pleasant fragrance, and weeping trait  from QingDao, China  and the weeping cultivar “Fen Tai ChuiZhi” of mei (2001-40) from WuHan, China . These were selected based on their contrasting phenotypic divergence in tree architecture . The quality and quantity of the DNA was checked by agarose gels and Bioanalyzer 2100 . Following quality assessment, genomic DNA was randomly fragmented using sonification (Covaris M220) and was then purified by DNeasy Plant Mini Kit . Terminal repair was conducted for the digested DNA fragments using T4 DNA polymerase and Klenow DNA polymerase (NEB), together with the phosphorylation modification for the 5’ end. A single nucleotide (A) overhang was added subsequently using Klenow Fragment (NEB) at 37 °C. Solexa adapters were ligated to the A-tailed fragments, and then sticked to flow cell to perform bridge polymerase chain reaction (PCR). DNA fragments of the desired length (400–600 bp) were then gel purified and PCR was conducted again to increase the template amount. Then, DNA quantity of the templates were determined by Invitrogen Qubit fluorometer , and bridge amplication was conducted on the surface of the flow cell chip-set. After further amplification in the cluster station, the flow cell was moved to the Illumina HiSeq 2000 system  and pair-end sequencing was performed according to the manufacturer’s recommendations.http://prunusmumegenome.bjfu.edu.cn) using Burrows-Wheeler Aligner (v 0.7.10-r789) algorithm under the default parameters  and “Fen Ban” (2000-15)) were applied to assist detection of DNA polymorphisms closely related to weeping trait  to get analysis-ready sequence alignment files  was created to locate each SNP within annotated transcripts or intronic regions using SnpEff software (ver 3.1h)  and linkage analysis of our research group  class was assigned by searching query gene sequence against KOG database available at NCBI. In addition, GO enrichment analyses were further conducted for these candidate genes.Whole-genome sequencing of “Liu Ban” and “Fen Tai ChuiZhi”, yielded 75.87 million 100-bp paired-end reads, which comprised 6.78 Gb of high-quality raw data. A total of 67,792,732 high-quality reads were obtained from the two cultivars after appropriate quality filtering as described in the “Identical variations detected in “FenTaiChuiZhi” cultivar compared with the three upright cultivars of mei were considered as potential weeping-related variations. In total, 172,381 polymorphisms were detected. The most common variants were SNPs, while InDels were present in similar numbers and proportions; these variants represent in approximately 8.74 % of the total DNA polymorphisms  or transversions  based on nucleotide substitutions. In general, the frequency of transitions was slightly lower than transversions, and the ratio between Ts/Tv was 0.84 , PPR repeat domain , protein kinase domain , and Ankyrin repeats , most of which were consistent to the above Pfam analysis results.The region of two candidate QTLs conferring weeping trait ranged from 8.36 to 11.69 M in chromosome 7 of mei  analysis was performed to investigate the putative functions of these candidate genes. Excluding genes of general functions prediction, genes involved in transcription followed by replication, recombination, and repair were most represented. Besides, the genes related to posttranslational modification, protein turnover, chaperones, and signal transduction mechanisms were also abundant Fig. . Gene onWith the rapid development of next-generation sequencing technologies, it is now much more reliable to discover DNA polymorphisms on a genome-wide scale, which plays a vital role in unraveling the genetic basis of phenotypic differences at genomic level. By re-sequencing a weeping cultivar and an upright cultivar of mei, together with application of former re-sequencing data of two upright mei cultivars, we uncovered 172,381 SNPs and InDels which were expected to be more related with tree architecture. This is a first report on the genome-wide detection of genetic variation for tree architecture, which will be of great value for further genotype-phenotype studies and for molecular breeding of this important ornamental and economical trait.In this study, only 8 % of reads could not be mapped onto the reference genome of mei, indicating a low percentage of repeated sequences in its genome. In addition, the average genome coverage reached 13.3× Table , which iAnalyses of the numbers of mutual SNPs and InDels in “Fen Tai ChuiZhi” compared with the three upright cultivars of mei indicated that the numbers of polymorphisms in each chromosome was proportional to their physical sizes. The distribution of DNA polymorphisms was found to be uneven across and within the mei chromosomes, which was in line with former researches in mei and other species  and no apical meristem-associated C-terminal domain (PF14303.1), with the highest ns/s SNPs ratios, were supposed to be promising functional markers associated with weeping trait. The reason is that the structure of the cell wall in different tree architectures varied from each other Supplementary Fig. 2Characteristics of mutual SNPs and InDels in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars of mei separately. a. Frequency of different substitution types in mutual SNPs detected; b. Distribution of the length of mutual insertions and deletions identified. The x-axis represents the number of nucleotides of insertions (red) and deletions (blue). The y-axis represents the number of deletions and insertions at each length. (TIF 10439 kb)Supplementary Fig. 3Location of the 90 SNPs markers selected for validation in each chromosomes of mei. (TIF 431 kb)Supplementary Table 1Mei cultivars selected for SNP validation and genotyping by the HRM method. (TIF 374 kb)Supplementary Table 2Primers used for HRM analysis and SNP validation. (DOC 42 kb)Supplementary Table 3The detailed information of the mutual SNPs in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars of mei separately. (DOC 149 kb)Supplementary Table 4The detailed information of the mutual InDels in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars of mei sepatately. (XLSX 52034 kb)Supplementary Table 5Distribution of the effects of SNPs in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars. (XLS 10428 kb)Supplementary Table 6The List of the Pfam gene families of mutual SNPs in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars of mei separately. (DOC 36 kb)Supplementary Table 7Distribution of the SNP and InDels effect in ‘Fen Tai ChuiZhi’ compared with the three upright cultivars of mei in QTLs conferring weeping trait. (XLS 1183 kb)Below is the link to the electronic supplementary material.