Cattle are considered to be the most desirable livestock by small scale farmers. In Africa, although comprehensive genomic studies have been carried out on cattle, the genetic variations in indigenous cattle from Nigeria have not been fully explored. In this study, genome-wide analysis based on genotyping-by-sequencing (GBS) of 193 Nigerian cattle was used to reveal new insights on the history of West African cattle and their adaptation to the tropical African environment, particularly in sub-Saharan region. SPRY2 and ITGB1BP1 possibly involved in common physiological activities.The GBS data were evaluated against whole-genome sequencing (WGS) data and high rate of variant concordance between the two platforms was evident with high correlated genetic distance matrices genotyped by both methods suggestive of the reliability of GBS applicability in population genetics. The genetic structure of Nigerian cattle was observed to be homogenous and unique from other African cattle populations. Selection analysis for the genomic regions harboring imprints of adaptation revealed genes associated with immune responses, growth and reproduction, efficiency of feeds utilization, and heat tolerance. Our findings depict potential convergent adaptation between African cattle, dogs and humans with adaptive genes The study presents unique genetic patterns of Nigerian cattle which provide new insights on the history of cattle in West Africa based on their population structure and the possibility of parallel adaptation between African cattle, dogs and humans in Africa which require further investigations.The online version contains supplementary material available at 10.1186/s12864-022-08512-w. Bos taurus taurus circa 7,000 – 4,000 YBP and later the Bos taurus indicus circa 4,000 – 2,000 YBP from their domestication centers [African indigenous cattle are considered to be the most desirable livestock by small scale farmers in the continent due to their vast economic benefits. These ranges from meat, milk, drought power, source of leather, manure and bride price. The origin of the domesticated cattle can be traced to around 10,000 years before present (YBP) in Southwest/South Asia, and in West Asia for indicine and taurine cattle, respectively , before  centers , 4.A large number of African taurine are found in West Africa . HoweverIn the past decade, the advancement of genomic technologies has made it possible to analyze the genomic DNA of individuals using WGS and GBS , 12. TheIn this study, we evaluate the efficacy of datasets generated by GBS versus WGS and the subsequent application in downstream population genetics analyses. To the best of our knowledge, we applied GBS approach for the first time in assessing the genetic diversity and adaptation mechanism of cattle samples from Nigeria. Several studies have been carried out to analyze the population structure and selection signatures of modern African cattle , 20, howThe 5-sample dataset generated a total of 924,152 and 23,955,622 GBS and WGS SNPs, respectively, of high-quality filtered genotypes  for comparisons  at an average mapping rate of 99.13% and jointly merged and genotyped with 75 publicly available genomes [B. indicus X B. taurus crossbreeds) and the outgroup samples  , the African B. taurus (represented by Muturu in green and N’Dama in brown) and Asian B. indicus (orange) [B. taurus and Asian B. indicus possibly suggest the admixed (B. taurus X B. indicus) background of the majority of African zebu cattle [The structure of the cattle populations was extrapolated by principal component analysis (PCA), admixture and phylogenetic tree. PCA results depict three major clusters of cattle Fig. a and sho(orange) , 25. Theu cattle  and thatu cattle . Principu cattle . PC1 andK = 3 as the optimum ancestral populations for inferring the genetic structure and admixture  tree was constructed by TreeMix software in order to infer evidence of gene flow between cattle populations from Europe, Asia and Africa using sequentially migration edges 1 to 4)  approach implemented in SweeD and PBS  supporting the viability of GBS data. Basically, the windows of the 1% threshold level from PBS and CLR test included in total 2674 and 240 PSGs, respectively, indicating that 2613 and 179  were unique to PBS and CLR, respectively, whereas 61 of all the detected PSGs overlapped in both analyses , 2027 PSGs were detected in Nigerian zebu cattle following their separation from the common ancestor with European cattle  and conrum (IL4 )  [SPRY2 PSG was also found as an evidence of common ways of adaptation between African N’Dama (B. taurus lineage) [B. indicus lineage) when enrichment analysis was performed using their 29 overlapping PSGs  and humans among which 7 overlapped also with dogs   revealedatabase) , 44. SPRlineage)  and NigeTo explore the potential of GBS approach, we applied it to understand the population genomics of Nigerian zebu cattle through the analysis of genetic structure, and signatures of selection. We firstly generated optimum evidence of its applicability on cattle genomic studies by comparing it with WGS. We obtained high rates of concordance for the variants calls (SNPs) between GBS and WGS datasets  and SPRY2  found to overlap between Nigerian zebu cattle, humans and N’Dama, the African taurine . Therefore, more efforts are still needed to determine and characterize the mechanisms of convergent adaptation in particular those conferring to resistance of diseases such as those endemics in West Africa in order to inform appropriate strategies befitting conservation, survivability of livestock, production improvement and applicability in biomedical research models for human related diseases.n = 36), Kano (n = 6), Katsina (n = 7), Oyo (n = 2), Plateau (n = 50), Sokoto (n = 37), Taraba (n = 44), and Zamfara (n = 11). Genomic DNA extractions were performed following phenol–chloroform method [Whole-blood samples 10 ml) were collected from 193 cattle coming from eight different States in Nigeria as follows , where the GBS approach was carried out following the GBS protocol [MseI , T4 DNA ligase (NEB), ATP (NEB), and MseI Y adapter N containing barcode. Fragment read length of 150 bases (PE150) were then sequenced using the Illumina HiSeq2500 platform with TruSeq SBS Kit v3-HS (Illumina). The whole genome-resequencing for five samples were also conducted at Beijing Novogene.Briefly, the DNA PCR extracts were then sent to Bejing Novogene , Fisher strand bias (FS), quality by depth (QD), the read position rank sum test (ReadPosRankSum) and phred score (GQ). The values for each parameter were QD > 2.0, FS < 60.0, MQ > 40.0, MQRankSum > -12.5, GQ > 20, QUAL > 50.0, ReadPosRankSum > -8.0, and ((MQ0 / (1.0 * DP)) < 0.1)” > ”. After filtration, only the high quality biallelic SNPs with genotyping call rate > 90% were retained for downstream analyses. The density of SNPs in each chromosome and the allelic distribution of minor alleles are provided in Additional file We randomly selected five of the 193 cattle samples and re-sequenced them using WGS method in order to assess the accuracy of GBS. Common number of SNPs, and genotypes as generated by GATK were used for concordance evaluation. Notably, a Pearson correlation (r) method was used to determine the correlation between the computed distance matrices by GBS and WGS data using cor.test test R function. More details of the evaluation assessment can be obtained in Supplementary Notes.B. taurus and B. indicus cattle of both African, European, and Asian lineages as well as B. javanicus and B. bubalis which define the outgroup. Only the overlapping genomic regions between GBS and WGS were considered using the merge parameter flag -intersection in GATK. Detailed information on the newly generated GBS data can be accessed in Additional file To perform population genetic analyses of the 193 genomes of Nigerian cattle GBS data, we also integrated 75 WGS genomes datasets publicly available from previous studies –23 repreFor PCA , EIGENSOB. bubalis as outgroup. The outputs were plotted using the R v4.0.2 software. We furthermore tested for the introgressions between populations by performing D-statistics analysis for all possible combinations [The ML tree was computed following the proposed protocol in TreeMix v1.13 software  in orderinations .FST statistics [Filtered biallelic SNPs from 193 samples were processed through VCFtools and GATK software bioinformatics tools with a genotyping rate of at least 90%, and minimum phred scaled genotype quality of 20 to produce a VCF file consisting of high-quality set of 3,282,427 SNPs. To avoid any significant false positives, the computation for the signatures of selection was done using the estimated allele frequencies from the common genomic regions between Nigerian cattle (GBS data) and other cattle populations (genotyped by WGS) merged together during PBS analyses. We used Sweep Detector (SweeD) tool that implements a composite likelihood ratio (CLR) test and the population branch statistics (PBS) , 72 to iatistics  in non-oFST) between Nigerian cattle (N) and other cattle populations (OT) recorded from each of the four scenarios stated above and between these populations (N and OT) and the distantly related species (D) represented herein by the outgroup samples, the water buffalo and banteng. Sequentially, the divergence time (t) of Nigerian cattle from the other populations is also determined.where; PBS estimates the pairwise allele frequency (B. taurus UMD 3.1 Gene Transfer Format file expressed by an extension (.gtf) from Ensembl release 90 [P value of 0.05 was used as threshold level for statistical significance. The same protocol for accessing the adaptation information imprinted in the Nigerian cattle genomes was also implemented using ARS-UCD1.2 reference genome in order to assess the reliability of our findings.The annotation of the candidate regions was based on the lease 90 . Functiolease 90  based onlease 90  and the lease 90 . A BonfeAdditional file 1. Additional file 2.


A new major QTL on chromosome 7 (qSRN7) was identified. Differentially expressed genes (DEGs) based on RNA-Seq were identified between two inbred lines with no SRN and multiple SRN at two periods of seminal roots primordia formation. A total of 116 DEGs detected in at least one period were identified within the qSRN7 interval. Three DEGs  associated with SRN were identified through regional association mapping. When compared with reported domestication-related selective sweeps, Zm00001d021572 was selected during maize domestication. Our findings provide important insights into the genetic basis of SRN and identify a promising candidate gene for further studies on SRN.Seminal roots play an important role in acquisition of water and nutrients by maize seedlings. Compared with its teosinte ancestor, maize underwent a change in seminal root number (SRN). Although several key genes controlling SRN have been cloned, identification and utilization of new genes from teosinte would be useful for improving maize root architecture. In this study, a maize-teosinte BC It was domesticated ~9,000 years ago from Mexican lowland annual teosinte (Zea mays ssp. parviglumis). Maize has several major morphological differences from its ancestor  protein involving in seminal and crown root primordium formation and maintenance  transporter, and regulates the timing and rate of initiation of seminal roots and plant lateral organs in both seed and plant development  derived from a cross between teosinte and inbred line PH4CV and genotyped by resequencing. Two inbred lines with large differences in SRN from an association panel were used for RNA-Seq analysis to identify DEGs related to the formation and development of SRN. Regional association mapping of SRN was carried out in an association panel containing 351 inbred lines. Combining the results from the two approaches, candidate genes underlying SRN were identified, and would provide promising genetic resources for improving maize root architecture.In this study, we performed high-resolution QTL mapping for SRN, using a BC22.11 seed from a cross between inbred line PH4CV as female parent and Zea mays ssp. mexicana (CIMMYT accession number 249743). In addition, an association panel including 351 elite inbred lines derived from a large association mapping population  with 150-bp paired-end reads. After checking and filtering sequence quality, the remaining sequences were mapped to the B73 reference genome (B73_V4). A total of 62,217,878 single nucleotide polymorphisms (SNPs) were identified in the population. To obtain high-quality SNPs, we removed low-quality SNPs with missing rate >60%, minor allele frequency (MAF) > 0.175 or MAF <0.075, and heterozygosity rate >5%. Finally, 138,208 high-quality SNPs were used to construct a recombination bin map.The R package “binmapr” was used to fix genotypes using a windows size set to 15. Redundant markers were removed by the “bin” function in IciMapping software. The genetic distances between the final 1,951 bin markers were determined using a Kosambi method in IciMapping software with the “map” function. The linkage map and recombination bin map were drawn in R software with the “LinkageMapView” package and “plot” function, respectively.2.3To phenotype SRN, fifteen seeds of each line from the TP population and association panel were surface sterilized with 6% hypochlorite for 10 min, followed by three washes in distilled water, transferred to wet filter paper, rolled up and placed in plastic buckets containing distilled water. The buckets were incubated in darkness at 25°C. Three days later, the buckets were transferred to a new incubator at 23°C\8h darkness and 28°C\16h light. The distilled water in the buckets was replaced for every two days. After five days, the number of seminal roots for 10 healthy plants of each line was manually counted and average values of SRN were subsequently used in analysis.2.4QTL analysis of SRN based on linkage map and phenotypic values from the TP population was conducted using WinQTL Cartographer v2.5 software by composite interval mapping (CIM) , with a 2.5th and 30th days after pollination, respectively. Four biological replicates for each line were collected at each sampling time. All samples were immediately frozen in liquid nitrogen and stored at -80°C for RNA isolation. RNA extraction and library preparation for each sample were performed by Novogene Corporation. Sixteen libraries were sequenced using the Illumina HiSeq 2000 platform , and 150 bp paired-end reads were generated. The raw reads were filtered to remove sequencing adapters as well as low quality reads (base number of Qphred ≤ 20 accounting for more than 50% of the read length). HISAT2 software was used to compare clean reads quickly and accurately with the reference genome to obtain the locations of reads on the AGv4 reference genome  matrix were calculated using 43,252 SNPs identified by the 50K SNP chip in the association panel. A mixed linear model (MLM) with PCA and K matrix was used to conduct regional association mapping in TASSEL 5.0 software (2 ≥ 0.2). After pruning, the number of independent SNPs in the qSRN7 interval was determined to be 6,070. We then selected 1.65×10-4 (1/6070) as the threshold of association signals.The genotype data from the association panel of 351 inbred lines in the software . To dete33.1The TP population was genotyped using whole genome resequencing technology. A total of 138,208 high-quality SNPs were identified in the TP population and used to determine bin markers. Finally, a total of 1,951 recombination bins were obtained by using the “binmapr” package in R software Figure 13.2RTCS gene that has been cloned and regulates SRN, was located near the QTL peak on chromosome 1. The new QTL on chromosome 7 was named qSRN7, and a total of 345 candidate genes were located within the QTL interval.We observed that the SRN showed a large phenotypic difference between the PH4CV and teosinte parents  (RTCS (Zm00001d027679) and BIGE1 (Zm00001d012883) at 30th days after pollination, both genes regulate initiation of seminal roots in maize. We also identified promising DEGs associated with root development and growth. For example, ZmEXPB2 (Zm00001d029899) encoding an expansin-B4 protein, which increases primary root length  > 3.78 , an associated SNP (S7_156440809) located within the UTR3 region, could form two haplotypes. Inbred lines with the GG haplotype had significantly more seminal roots than those with TT haplotype  is adversely affecting crop yields worldwide. To facilitate adaptation to climate change, breeders will need to produce new varieties displaying both higher yield as well as improved adaptation to different environments. Hence, it is important to use more and new genetic resources to develop stress-resilient varieties for adaptation to extreme environments. Teosinte, the closest wild relative of maize is adapted to a diverse range of environments , and hasth and 30th days after pollination, respectively, with 4,241 DEGs shared between both stages. Genes RTCS and RUM1 underlying SRN were identified previously. The RTCS gene encodes an LBD protein involved in auxin signal transduction (RTCS is activated by binding of auxin response factor (ARF) 34 to LBD elements in the promoter . Five associated SNPs were identified. When combining associated SNPs and DEGs within the QTL, we identified three SNPs significantly associated with three DEGs . When genes selected during maize domestication reported by Previous studies suggested that regional association mapping was an effective method to identify candidate genes underlying agronomic traits. In this study, we used regional association analysis to identify SNPs significantly associated with SRN within the 5qSRN7 interval, three high-confidence candidate genes underlying SRN were identified. Of the three genes, Zm00001d021572 reported in a previous study was selected during maize domestication. The results of this study provide insight into the genetic basis of SRN and potential candidate genes for improving seminal root system in maize.This study dissected the genetic basis of SRN in maize, through conventional QTL mapping in a maize-teosinte population genotyped by resequencing, RNA-Seq and regional association mapping. A new QTL underlying SRN was identified. A total of 4,241 DEGs shared in two sampling times were identified. Among those DEGs, we identified known genes controlling maize SRN and many candidate genes involved in root development and growth. Combining data on DEGs and associated SNPs obtained by regional association mapping within the The data presented in the study are deposited in the Genome Sequence Archive, accession number PRJCA013887.CL, JC and JG conceived and designed the experiments. KW, ZZ and PY collected the samples to conduct resequencing and RNA-Seq. KW, ZZ and XS performed phenotypic measurement and conducted data analysis. KW, ZZ and CL wrote the manuscript. CL, JG and JC provided experimental support and participated in revising the manuscript. YXL, DZ, XL and GH participated in data analysis. TW and YL supervised the project. The authors read and approved the final manuscript.


Plant height of soybean is associated with a haplotype block on chromosome 19, which classified 211 soybean accessions into five distinct groups showing significant differences for the target trait.Genetic variation is pivotal for crop improvement. Natural populations are precious genetic resources. However, efficient strategies for the targeted utilization of these resources for quantitative traits, such as plant height (PH), are scarce. Being an important agronomic trait associated with soybean yield and quality, it is imperative to unravel the genetic mechanisms underlying PH in soybean. Here, a genome-wide association study (GWAS) was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with PH in a natural population of 211 cultivated soybeans, which was genotyped with NJAU 355 K Soy SNP Array and evaluated across six environments. A total of 128 SNPs distributed across 17 chromosomes were found to be significantly associated with PH across six environments and a combined environment. Three significant SNPs were consistently identified in at least three environments on Chr.02 (AX-93958260), Chr.17 (AX-94154834), and Chr.19 (AX-93897200). Genomic regions of ~ 130 kb flanking these three consistent SNPs were considered as stable QTLs, which included 169 genes. Of these, 22 genes (including Dt1) were prioritized and defined as putative candidates controlling PH. The genomic region flanking 12 most significant SNPs was in strong linkage disequilibrium (LD). These SNPs formed a single haplotype block containing five haplotypes for PH, namely Hap-A, Hap-B, Hap-C, Hap-D, and Hap-E. Deployment of such superior haplotypes in breeding programs will enable development of improved soybean varieties with desirable plant height.The online version contains supplementary material available at 10.1007/s00122-022-04120-0. Glycine max L.) is an important oilseed crop, grown globally as a major source of edible protein and oil , and these QTLs are located across 20 soybean chromosomes. Most of the previously reported studies used linkage mapping with low-resolution markers to identify such QTLs, which often possess low genomic resolution. This has hampered successful deployment of PH QTLs in breeding programs to develop improved soybean varieties with desired PH , sequencing-based trait mapping strategies have been successful in identifying QTLs/ MTAs in several crops  at three different locations in China. This soybean germplasm represents the cultivated pool selected from different ecological regions of China, previously described by Wang et al.  and Du elme4” package in R environment. The analysis of variance (ANOVA) and predicted means [best linear unbiased predictions (BLUPs)] were calculated with genotype set as random. Predicted means (BLUPs) for individual environment were estimated using the model:Yijk is the trait (PH), µ is the overall mean effect, and Repi denotes the effect of the ith replicate/block. Genj represents the effect of the jth genotype; εij is the effect of the error associated with the ith replication/block, and jth genotype.In each environment, five consecutive plants of each genotype were selected from the middle of each plot for PH phenotyping in each replication. PH was measured in centimeters (cm) by using a measuring tape stretched from the cotyledonary node to the top of the plant at the maturity stage. The data obtained from individual environments (location × year combination) and the combined environment were evaluated using “i and Envi × Genk are the ith environment and the G × E interaction effects, respectively.The ANOVA and predicted means (BLUPs) for the combined environment were estimated using the following model:σ2g/(σ2g + σ2ge/n + σ2ε/nr) and σ2g/(σ2g + σ2ε/r), respectively, where σg is the genetic variance, σ2ge is the interaction of genotype and environment, σ2ε is the residual error, n is the number of environments, and r is the number of replication/block .www.phytozome.net). The VIP variants include 60,800 SNPs from SoySNP50K15, 2103 SNPs from SoyBase, and 3671 SNPs from the sequencing data of 31 soybean genotypes. A total of 355,595 SNPs representing 609,883 probe sets were obtained following the Affymetrix filtering .Genome-wide association analysis was conducted with Genomic Association and Prediction Integrated Tool (GAPIT) package in R environment . All markers associated with PH on chromosome 19 (Chr.19) within the LD ± 130 kb , using Glyma2.0 gene model. The functional annotations of genes were downloaded from SoyBase, and were screened manually. The RNA-seq data available at SoyBase (https://www.soybase.org/) were extracted for the genes underlying the QTL intervals from different soybean tissues  at different growth and developmental stages, viz., young leaf, flower, one cm pod, pod shell 10 days to flowering (DAF), pod shell 14DAF, seed 10DAF, seed 14DAF, seed 21DAF, seed 25DAF, seed 28DAF, seed 35DAF, seed 42DAF, root, and nodule) (Supplementary Table 4). These RNA-seq data were previously generated and deposited in the SoyBase by Severin et al. , environment (E), and the genotype × environment interaction (G × E) had significant effect (P < 0.0001) on PH (Table P < 0.0001) across different environments evaluated (Supplementary Table 5).The descriptive statistics including mean, range , standard deviation, skewness, kurtosis, broad-sense heritability, and coefficient of variation (CV) for PH in 219 soybean accessions evaluated across six different environments and the combined environment are presented in Table In the present study, a total of 211 soybean accessions and 291,962 SNP markers were retained after quality control analysis Table . These mqPH2, qPH17 and qPH19, respectively. These QTLs/genomic regions represented stable genetic elements regulating PH in soybean.A total of 128 SNPs were found to be significantly associated with PH across six environments and the combined environment, based on GWAS results Fig. . These in = 102), followed by Hap-B (n = 72), Hap-C (n = 16), Hap-D (n = 17), and Hap-E (n = 04)  within the LD decay of ± 130 kb was subjected to haplotype analysis. The results showed that only significant markers identified within the LD decay flanking the SNP AX-93897200  on Chr.19 formed a haplotype block. No haplotype block was detected for other two significant SNPs within the referenced LD decay (± 130 kb) on Chr.02 and Chr.17. Hence, the SNP (AX-93897200) along with 11 other SNPs  were found to be in strong LD and formed a single haplotype block , which consisted of 18, 35, and 116 genes, respectively (Supplementary Table 7). Of the 169 genes detected, the RNA-seq data for only 124 genes were available online at SoyBase (https://www.soybase.org/) for 14 soybean tissues across different stages of growth and development (Supplementary Table 4). Expression pattern of the genes obtained from the RNA-seq data is represented in the form of a heatmap in Fig. qPH2, five genes underlying qPH17, and 15 genes underlying qPH19 . In accordance with previous studies  in the soybean natural population across all six environments, which is consistent with previous studies  associated with PH has been previously reported in the genomic region between 15,993,654 and 41,032,570 bp by Sun et al.  and Plant height 10–4  on Chr.19  were identified via linkage mapping by using low-density markers such as simple sequence repeat and restriction fragment length polymorphism, possessing a relatively low genomic resolution. These marker systems have low selection accuracy, which has been the biggest obstacle for their deployment in the MAB programs. QTL mapping involves the use of bi-parental mapping populations, which are associated with poor resolution in the detection of target loci regulating traits of interest  flanking significant SNPs were referred as QTLs related to PH based on the LD decay. These QTLs/genomic regions represent stable genetic elements regulating PH in soybean. The QTL on Chr.02  gene lying within the physical interval of qPH19 was considered as the candidate gene. The same locus on Chr.19 underlying the Dt1 locus was also identified previously  were defined as putative candidates controlling PH by taking into consideration their annotations and gene expression patterns. However, further verification and functional validation of these genes are required to precisely pinpoint their role in the regulation of PH in soybean.In the practical breeding programs, the identification of potential markers/candidate genes for the trait of interest underlying a particular genomic region is the ultimate objective (Ganie and Ahammed GmCHX1) was subjected to haplotype analysis. Among various haplotypes identified for GmCHX1, SV-2 was found to provide maximum salinity tolerance  associated with PH, which were consistently detected in at least three environments. Out of these three QTLs, the QTLs co-localizing with qPH2 and qPH19 have been previously detected by other researchers. However, no QTL was found to be associated with qPH17, implying this locus to be a novel genomic region regulating PH. A total of 22 candidate genes underlying these three QTL regions were also prioritized. The putative candidates defined within the intervals of qPH2, qPH17, and qPH19 need further functional validation before they can be used in soybean breeding programs. The haplotype analysis detected five haplotypes  within the stable genomic region of Chr.19, which regulated PH ranging from dwarf ➝ intermediate ➝ tall. The superior haplotypes identified in the Chinese soybean germplasm will serve as a potential resource for haplotype-based breeding of PH in soybean.The present study identified 128 significant SNPs and three stable QTLs/genomic regions (Supplementary file1 (DOC 1587 KB)Supplementary file2 (XLSX 22 KB)Supplementary file3 (XLSX 103 KB)Supplementary file4 (CSV 195568 KB)Supplementary file5 (XLSX 28 KB)Supplementary file6 (XLSX 9 KB)Supplementary file7 (XLSX 21 KB)Supplementary file8 (XLSX 65 KB)Supplementary file9 (DOCX 23 KB)Below is the link to the electronic supplementary material.


Composition and technofunctional properties of the almond flours of 12 mango varieties from savannah zones of Cameroon  are assessed in order to highlight their potential use in breadmaking, through their effect on dough rheology. The compositions of almonds display starch as the major constituent (60–65%) with more than 80% of amylopectin and a significant presence of phenolic compounds (1–7%), lipids (7–13%), and proteins (4–7%), depending on mango varieties, with local varieties showing the highest polyphenol and lipid contents. Almond flours are characterized by high WAC (water absorption capacity) and OAC (oil absorption capacity) and pasting properties influenced by starch lipid and starch protein complexes. In wheat-mango almond composite flours, the pasting properties are mango variety and almond flour treatment (native or delipidated) dependent, with a substitution threshold effect variable from one variety to another. Alveographic profiles of the composite flours result in dough characterized by high tenacity (P) but low swelling index (G) and baking strength (W). These effects are intensified with increasing substitution of wheat. However, up to 10% wheat substitution, the composite flours seem acceptable in breadmaking, with 9 mango varieties hypothetically convenient for the reduction of bread staling. Mangifera indica L.), with an estimated world production of more than 39 million tons, is the fifth fruit production after citrus fruits, grapes, bananas, and apples. The fleshy and juicy pulp of the fruit is eaten directly or processed into juice, nectar, jam, or dried. The kernel and the skin are generally discarded as waste, often causing environmental nuisance during the production season. However, the skin and particularly the almond of mango kernels, with regards to their composition and properties, display functional potential as food ingredients, antioxidant, antimicrobial, and cosmetics, carried by high content in polyphenols and lipids [Mango , and the resulting flour splits into two batches: one batch left as native mango almond flour and the second batch delipidated by extraction of oil with hexane using Soxhlet. Starch was extracted from the delipidated flour with SDS using the method of Mahmood et al. [Twelve varieties of mangoes, among the most cultivated and consumed in the savannah zone of Northern Cameroon, were harvested at physiological maturity, peeled, and pitted, and the kernels shelled manually using stainless steel knives. The two cotyledons of the kernel almonds were separated and dried  in an electric forced-convection dryer, to a residual water content less than 10%. The dried almonds were then ground to a particle size of 200 μL, elution using BPX 70 column  with temperature gradient of 120°C-250°C, 8.6°C/min, hydrogen as carrier gas, flame ionization detection at 280°C, and quantification using a calibration curve of fatty acid standards.The water and ash contents of the mango almond flours were determined, respectively, by drying in an oven at 105°C for 24 hours and incinerating in a muffle oven (PROLABO no. 54786) at 550°C, until obtaining decarbonated white ash. Lipid content was determined by extraction with 95% hexane. The fatty acid composition of the oil was determined by gas chromatography under the following conditions: methylation with diethyl ether and tetramethylammonium, injection volume: 0.1 The protein content (N x 6.25) was determined by the Kjeldahl method.Soluble sugars were extracted with 80% ethanol and determined using the DNS  colorimetric method .α-amylase  and amyl glucosidase  from Bacillus, for the hydrolysis of the polysaccharide into D-glucose and DNS for the determination of sugar released [2-Amylose complex displaying a strong blue color which intensity is measured at 720 nm. The amylose content of the sample was derived from a standard curve of potato amylose  treated in the same conditions as the assays.The starch content was determined by enzymatic method , using αreleased , which vreleased . The amyreleased , based oThe crude fiber content was determined using the method of Wolff .Total phenolic compounds were extracted with acetone (70%) and determined using the Folin Ciocalteu reagent with spectrophotometric measurement at 725 nm and a calibration curve with tannic acid as standard . The tan2 solution, in a pH 5.5 buffer, at 40°C, followed by centrifugation. The mixture was incubated at 60°C, and the optical density, representative of the residual starch content, recorded every 15 seconds for 3 minutes.An assay to evaluate the possible presence of amylase in mango almond flours was performed by mixing an aqueous flour extract with a starch suspension and following the possible hydrolysis of the polysaccharide over time . The aquWater absorption capacity (WAC) and oil absorption capacity (OAC) of almond flours were determined following the method described by Beuchat , while eTheir hydrothermal behavior in dispersed solution, using the Rapid-Visco-Analyzer (RVA) . The flour suspension, previously homogenized by stirring at 960 rpm for 10s, was treated, still under stirring (160 rpm), according to the following diagram: (i) hold for 1 min at the initial temperature (50°C), (ii) temperature rise from 50°C to 95°C at a speed of 12°C/min, i.e., in 3.75 min, (iii) maintain at 95°C for 5 min, (iv) cooling from 95°C to 50°C at 12°C/min in 2.25 min, and (v) maintain at 50°C for 1 minAlveographic behavior  of their pastes according to the AACC standard method .The evaluation of technological properties focused on the rheological behavior of wheat/almond flour mixtures according to ratios: 100/0, 95/5, 90/10, 85/15, and 80/20. The tests were performed with native and delipidated almond flours, as well as with starch extracted from mango almond flours and purified with sodium dodecyl sulfate (SDS) according to the method described by Mahmood et al. [p = 0.05).All measurements were performed in triplicate, and the results expressed as mean ± standard deviation. The analysis of variance (ANOVA) of the means made it possible to compare the influence of the variation factors , and level of wheat substitution) on the responses measured, and Duncan test was used to rank significantly different means  the significant presence of phenolic compounds confirms their proven application as a source of bioactive compounds, in particular through their antioxidant activity , 2 and pAnalysis of the oil composition shows a fatty acid profile that is practically at parity between unsaturated and saturated fatty acids, ranged, respectively, between 48%–59% and 40%–52%, mainly carried by palmitic (10–17%), stearic 23–40%), oleic (39–46%), and linoleic (1–10%) acids, depending on mango varieties. The high content of saturated fatty acids in all varieties of almonds (40-52%) suggests a potential for reducing bread staling, as these fatty acids are likely to form a complex with amylose, limiting thus the retrogradation of the polysaccharide, the main carrier of the staling phenomenon [–40%, oleα-amylase.Amylase activity is an important parameter in reducing the rate of bread stale . AlmondsFrom functional point of view, mango almond flours are characterized by a relatively high WAC and OAC , correspOverall, the chemical compositions of mangoes, in particular their high starch content, their richness in amylopectin, the possibility of the formation of starch-lipid complexes, and the significant presence of phenolic compounds, are all elements that militate in favor of a potential impact of these flours on the rheology of the dough during breadmaking.The RVA indicators of native and delipidated flours, as well as of starch from mango almond flours, are shown in The defatting of almond flour results in an increasing trend of “Peak Time” and “Peak Viscosity” which are approaching the values of wheat flour . On the If the relative variability of the rheological indicators is certainly linked to the structural variability of the starch in the samples, in particular the amylose/amylopectin ratio, which is likely to justify the variability of their water absorption capacity , it coulIn the native mango almond flours, the relatively low pasting indicators  could be attributed to constraints imposed by nonpolysaccharide materials which limit the swelling of granules . Defatti1&2). On the other hand, the maximum swelling (peak viscosity) seems subject to a substitution threshold effect, with a tendency of swelling increase for substitution between 0 and 2.5%, followed by a gradual decrease beyond that substitution level, to apparently stabilize for substitutions between 10% and 15%, values beyond which no significant variation in swelling is observed (1&2). The same is true for the gelatinization time (1&2). This behavior of composite flours during gelatinization could be the result of a competition of the compounds present for water, following the observations of Collar et al. [Partial substitution of wheat by mango almond flours (0-20%) does not induce a significant modification of the pasting temperature which, for all substitution levels, is around 63-65°C 1&2. On tobserved 1&2. The ion time 1&2. Thisr et al. , who sho1&2), which would indicate low heat resistance of the starch granules in composite flours. This behavior presents an apparent threshold, in terms of substitution rate, around 2.5% and 5% substitution, above which an inversion (or stabilization) of the breakdown evolution is observed, depending on the mango variety. However, there is a relative difference between wheat flours substituted by native almond flours and those substituted by delipidated flours. The increase in the amplitude of granule bursting is clearer and more regular in the blends containing the delipidated flours (2), the evolution being linear as a function of the degree of substitution, with all samples presenting a comparable pace. On the other hand, the amplitude of the breakdown is contrasted in blends containing native flours, for which beyond the apparent threshold, the magnitude of granule bursting remains relatively stable for some varieties or decreases for others, sometimes to the level or even below the bursting level of the gelatinized wheat starch granule (1). The latter is for instance, the case of the blend with native almond flour of the Julie variety. This flour is also the one which presented, in individual hydrothermal treatment, the lowest breakdown  while all the flours of the other varieties reduce it.The contrast between native and delipidated mango almond flours is also observed on the setback behavior of the flour blends, comparable to what is observed above for the breakdown. If the retrogradation of all blends containing delipidated flours decreases regularly with the increase in the substitution rate, a double trend is noted with native mango almond flours; the substitution of wheat by native flours of Julie, Indécinard, and local varieties (Maroua and Ngaoundéré) increases the retrogradation 1 while aThe above findings suggest that the functional use of mango almond flours would be variety dependent and should take into account, in addition to the rheological properties of the flours, their pretreatment conditions (defatting or not), and their chemical composition and potential interactions between the constituents, depending on the envisaged processes. It should be noted that these functional conditions of mango almond flours, provided by the RVA studies, respond to applications in dispersed medium and could well be perceived differently in an application such as breadmaking where the dough is less fluid.The partial substitution of wheat by mango almond flours results in an overall increase in the tenacity (P) of the dough which shifts the tenacity-extensibility balance (P/L) in favor of tenacity. This results in a decrease of the swelling index (G) and of the baking strength (W) . These vOn a hypothetical basis, the low expansion induced by the substituted flours could be attributed to the high-water absorption capacity of the mango almond flours, which suggests that, under the conditions of the analysis, wheat gluten, due to competition for water with mango almond flour, would not have had the possibility to absorb enough water for its swelling, like what was observed above in the RVA monitoring of the hydrothermal behavior of substituted flours . A probacm3 ≤ G ≤ 24 cm3. 0.45 ≤ P/L ≤ 0.7) from countries such as France and Algeria [All our flours, including the reference wheat used, have a swelling index (G) and a tenacity-extensibility ratio (P/L) outside the current standards (20  Algeria . The nonW ≥ 130.10−4 J) [However, up to 10% wheat substitution, wheat-mango almond composite flours have a strength within the range authorized in various countries (.10−4 J) , 32. It Comparative analysis of the behavior of our composite flours with respect to the technological standards applicable in different countries shows that:The almonds of the Indécinard and Smith varieties contribute mainly to the hydrothermal swelling (RVA) of the mixtures, with the highest values of PV, BD, and SB, regardless of the substitution rate. The retrogradation induced by these flours appears as a negative indicator in dough rheology for breadmaking, insofar as a high susceptibility to retrogradation is likely, in breadmaking, to lead to the rapid stale of the bread The almonds of the Springfield Local Ngaoundéré and Davis Haden varieties contribute to the swelling (G) and to the strength (W) of the dough, which is an indicator of an acceptable rise of the dough during fermentationThe almonds of the Brook's, Local Maroua, Julie, Haden and, to a lesser extent, Kent, and Alphonse varieties are characterized by their impact on the increase of the tenacity-extensibility ratio (P/L) of the bread dough, which may lead to a deleterious behavior of the bread doughThe integrated comparison of the technofunctional behavior (RVA and Alveogram) of wheat-mango almond flour blends, in the substitution range between 0% and 10%, allows  to dividSince the technofunctionality is carried by the composition of the mango almond flours, the integrated comparison of the latter in relation to the constituents liable to influence the functionality of the flours shows  that, alThe chemical compositions of mango almonds , coupled to their technofunctional (pasting and viscoelastic) properties, militate in favor of the use of their flour as partial substitute of wheat, at a maximum level of 10%, in breadmaking, with respect to potential reduction of bread staling. Nine varieties of mango from savanna regions of Central Africa appear suitable in this regard. Meanwhile, based on the varietal and property diversity of mango, breadmaking processing factors may need to be optimized, in order to define the best varieties, substitution and processing conditions for convenient composite bread to be able to resist the staling phenomenon.


Ralstonia solanacearum is a major challenge for peanut production in China and significantly affects global peanut field productivity. It is imperative to identify genetic loci and putative genes controlling resistance to R. solanacearum (RRS). Therefore, a sequencing-based trait mapping approach termed “QTL-seq” was applied to a recombination inbred line population of 581 individuals from the cross of Yueyou 92 (resistant) and Xinhuixiaoli (susceptible). A total of 381,642 homozygous single nucleotide polymorphisms (SNPs) and 98,918 InDels were identified through whole genome resequencing of resistant and susceptible parents for RRS. Using QTL-seq analysis, a candidate genomic region comprising of 7.2 Mb (1.8–9.0 Mb) was identified on chromosome 12 which was found to be significantly associated with RRS based on combined Euclidean Distance (ED) and SNP-index methods. This candidate genomic region had 180 nonsynonymous SNPs and 14 InDels that affected 75 and 11 putative candidate genes, respectively. Finally, eight nucleotide binding site leucine rich repeat (NBS-LRR) putative resistant genes were identified as the important candidate genes with high confidence. Two diagnostic SNP markers were validated and revealed high phenotypic variation in the different resistant and susceptible RIL lines. These findings advocate the expediency of the QTL-seq approach for precise and rapid identification of candidate genomic regions, and the development of diagnostic markers that are applicable in breeding disease-resistant peanut varieties.Bacterial wilt disease (BWD), caused by  In Arabidopsis, a recessive RRS1-R encoding a Tir-NBS-LRR resistant protein with a WRKY domain in resistant line Nd-1 was first identified and cloned by fine mapping  for RRS were identified in 100 F9 recombinant inbred lines (RILs) from another cross of Col-0 × Ler , encoding an NBS-LRR resistance protein and a receptor-like protein kinase, respectively, were identified by reverse genetics. Transgenic tobaccos that overexpressed these two genes conferred a significantly increased level of resistance to RRS, indicating that both R genes and RLKs are involved in resistance mechanisms against BWD  in the aforementioned RRS study accounted for 21% and 12% of the resistance phenotypic variance in the F2 generation, respectively. Only two side-by-side QTLs were found at the qBW-1 locus on the B02 chromosome in the F8 generation. The resistant resource of Yueyou 92 was from a Chinese landrace Xiekangqing, which is a major source of parental types used for breeding BWD-resistant variants in South China  on chromosome B02 was identified, which was significantly associated with RRS in three environments. Moreover, two SNP sites were confirmed in diverse breeding lines and cultivars. Unlike Yueyou 92, Yuanza 9102 was derived from the wild species Arachis diogoi that was resistant to BWD (via both linkage mapping and QTL-seq tools in a resistant peanut cultivar (qBWRB02-1 was identified on chromosome B02 as previously reported (qBWRB02-1 locus were segmented into qBWRB02-1-1 (2.81-4.24 Mb) and qBWRB02-1-2 (6.54-8.75 Mb) (QBWRB02-1-1 accounted for 49.43–68.86% phenotypic variation explained (PVE), which was higher than that for qBWRB02-1-2 (3.96–6.48% PVE). Moreover, this was validated by competitive allele-specific PCR (KASP) markers in different RILs and natural populations  . QBWRB02ulations .In this study, we utilized a QTL-seq approach to identify concomitant genomic regions, candidate resistance genes and diagnostic markers in a bacterial wilt-resistant peanut variety, Yueyou92. A 7.2 Mb candidate genomic region was elucidated on chromosome 12 significantly associated with RRS. This study reports successful discovery of followed by candidate resistance genes and validated markers for potential use in marker-assisted selection (MAS) for RRS in peanut breeding programs.R. solanacearum strains from different parts of China. In comparison, Xinhuixiaoli (XHXL) was a Chinese landrace that was highly susceptible to BWD. Their resistance validation was stable during multiple years of field assessment .Yueyou 92 (YY92), a variety that is highly resistant to BWD, was bred by the Guangdong Academy of Agricultural Sciences, China. It stemmed from Xiekangqing, which was resistant to sessment Figure 111, F12, and F13 generations and parents were cultivated in two-row plots with 20 seeds in each season. One-month to 40-day-old RILs seedlings were inoculated via a previously described artificial method  and in 2017 spring (2017S). The RILs of Fl method . Resistaxi : disease grade value, xmax : the highest disease grade value, and yi: the number of diseased plants corresponding to the disease rating.Where,The average DI was calculated for the three replications in a single environment. Statistical analysis of variance (ANOVA) was performed using the DPS7.5 software , Values are expressed as the mean ± standard deviation or standard error as indicated. Differences between groups were evaluated using one-way ANOVA. Statistical significance was set at P<0.05.via the Illumina HiSeq 2500 platform  with a sequencing depth of approximately 30× of the cultivated peanut genome (~2.7 Gb) for each pool and about 40× for parental plants. The raw sequencing data of the four libraries have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject ID PRJNA851221.The average DI for each RIL was calculated based on phenotyping data from the 2016S, 2016A, and 2017S seasons. We selected 30 resistant and 30 susceptible lines to construct the extreme R/S pool. To develop the resistant bulk (R-Bulk) for RRS, we selected 30 RILs with a low mean disease index and pooled the same amount of DNA from each into one. Similarly, DNA samples of 30 RILs with a high mean disease index were pooled to construct the susceptible bulk (S-Bulk) for RRS. The genomic DNA of these two extreme pools and those of the two parents was used to construct DNA sequencing libraries. Paired-end reads (151 bp) of four libraries were generated http://bio-bwa.sourceforge.net/) with the default parameters (Arachis hypogaea (Shitouqi) were downloaded from the Peanut Genome Resource website (http://peanutgr.fafu.edu.cn/) and used as the reference sequences. Duplicated reads were identified and filtered using Picard (http://broadinstitute.github.io/picard/) after mapping the clean reads to the reference genome. To determine the locations and effects of the SNP/InDel variants, we detected and filtered variants in the four libraries using the Genome Analysis Toolkit  (https://pcingola.github.io/SnpEff/) (A QTL-seq approach was used to identify the QTLs for RRS . The quarameters . For furg/gatk/)  and annoSnpEff/) .To identify the candidate genomic regions associated with RRS, we further filtered high-quality reads from S-bulk and R-bulk libraries by removing unpaired reads. To equalize the number of reads from each bulk, the filtered reads of the R-bulk were randomly reduced to the same number of the filtered reads of the S-bulk. During the analysis, the SNP sites with genotypes that differed between the two bulks were used to calculate both the sequencing depth of each base in the different bulks and the Euclidean Distance (ED) value of each site. To eliminate the background noise, the original ED value was processed by power. In this study, the fifth power of the original ED was taken as the correlation value to eliminate the background noise. Then the distance method was used to fit the ED value. For every SNP and InDel in each bulk, ED values were calculated with the formula:Each A, G, C, and T letter represented the frequency of its corresponding DNA nucleotide in the resistant and susceptible bulks. The higher the ED value, the stronger the association between the variant with the target characteristic.The SNP index value was calculated as follows:ΔSNP-index = SNP-index (aa)- SNP-index (ab)ΔSNP-index was calculated by subtracting the SNP-index of the R-bulk from the SNP-index of the S-bulk. SNP-index plots were generated using sliding window analysis with a window size of 2 Mb and increments of 50 Kb. The SNPs with SNP-index <0.3 or read depth<10 in both bulks were culled . The DI value of Yueyou 92 was significantly lower than that of Xinhuixiaoli in three consecutive crop seasons  and susceptible “Xinhuixiaoli” (SP) varieties as parents to create multiple generations of segregating RILs populations  as reference, the Euclidean distance (ED) and SNP index, including the ΔSNP-index, were calculated for each genome-wide high-quality SNPs, from RP and SP (P<0.01) for the SNP. A 6.40 Mb (0.77–7.17 Mb) interval on Chr02 was also identified. By SNP-index and ΔSNP-index, only a genomic interval of 5.83 Mb (4.16–9.99 Mb) on Chr12 deviated from the threshold with the confidence level of P<0.01  and 5.83 Mb (4.16–9.99 Mb) intervals on Chr12 were collectively identified as candidate region associated with RRS, at 95% and 99% confidence levels, respectively  in F8  of the existing genetic map to the previously published and newly developed SNP markers. The QTL map had two QTLs located in LG1 (ChrB02) and LG10 in F2) in F8 . The QTL2) in F8 . We remaAH12G01510, AH12G01540, AH12G01550, AH12G01560, AH12G01570, AH12G01600, AH12G01900, AH12G01920, AH12G01980, AH12G02020, AH12G02090, AH12G02120, AH12G02130, AH12G02310, AH12G02330, AH12G02370, AH12G02390, AH12G02410. AH12G02880, AH12G03230, AH12G03600 and AH12G06320  (12G06320 Table 2.qRRS18 and qRRS19) in AH12G03230 and AH12G06320 genes co-segregated with RRS and may thus be deployed for RRS breeding  could be further developed and used to distinguish between homozygotes and heterozygotes in the segregating population; i.e., susceptible lines will have a 302-bp allele from the marker ‘qRRS18’ and resistant lines will have a 217-bp allele from the marker ‘qRRS19’. These two markers can be used as diagnostic marks for breeding resistant bacterial wilt varieties via MAS approach.To evaluate the specificity of the allele marker of the resistant and susceptible peanut cultivars, we targeted 44 SNPs from candidate NBS-LRR genes for RRS for the development of allele-specific markers , belongs to the subsp. fastigiata, as does the parents of the population, and its high-quality genome sequence was recently reported  were identified for RRS, which were in the LG1 and LG10 linkage groups based on RAD- and BSA-seq techniques in F2 plants. One QTL linked to two QTL peaks on ChrB02 was identified in an F8 RIL population (qBWRB02.1 that possibly spanned a 5.14 Mb (0.81–5.95 Mb) interval on chromosome B02 based on its flanking SSR markers . Two major QTLs that were both named qBWRB02.1, were identified from the cross of Yuanza 9102 × Xuzhou 68-4. Yuanza 9102 is a popular resistant cultivar whose resistance stemmed from A.diogoi  were robust and co-segregated with RRS  domain when compared to the R4 gene that conferred a susceptible phenotype (qRRS18 and qRRS19) of the candidate AH12G03230 and AH12G06320 NBS-LRR genes were validated in the RIL lines  and tools (markers) could be used in genomics-assisted breeding programs to accelerate the development of peanut varieties with enhanced RRS as well as to increase insights into RRS molecular mechanisms.The original contributions presented in the study are publicly available. This data can be found here: NCBI, PRJNA851221.WZ and RV conceived the original research plan and designed the experiment. CZ, WX, HF, YC, HC, TC, QY, YZ, KC, and XZ performed the experiments and analyzed the data. CZ and WX wrote the manuscript, while WZ, MP, and RV reviewed and edit the manuscript. All authors analyzed the data and approved the submitted version.


A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology.About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST).spectral results between the states (A-EC vs. D-EC) and conditions (EO vs. A-EC). The influence of state and condition was far less pronounced for connectivity analyses, with only minimal differences between D-EC and EO in the AECc in the delta band. There were no effects of drowsiness on any of the MST measures.Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance. Quantitative analyses of neurophysiological data in terms of spectral changes and alterations in  connectivity and network topology have changed our thinking on how neuropathology can lead to cognitive disability  or drowsiness. Specifically, when MEG measures, such as oscillatory slowing or altered, functional connectivity and network properties, are studied as a potential biomarker for disease.Existing resting-state MEG recordings of 66 healthy controls were selected from the larger Amsterdam multiple sclerosis (MS) cohort. The Amsterdam MS cohort is a prospective cohort that is focused on finding imaging and biomarker determinants of disease progression in MS. Visits consist of a clinical assessment of neurological functioning, routine MRI, and MEG recordings. Healthy controls were included in the Amsterdam MS cohort for a case-controlled comparison , while participants were in supine position inside a magnetically shielded room . The recording protocol consisted of 3-min eyes-open followed by a 5-min recording with the eyes closed in the majority of subjects. Refer to A sample frequency of 1,250 Hz was used. An anti-aliasing filter of 410 Hz and a high-pass filter of 0.1 Hz were applied online, and other artifacts were removed offline using the temporal extension of signal space separation (tSSS) in MaxFilter software with a sliding window and correlation limit of 10 and 0.9, respectively   for the presence of an equal number of non-overlapping eyes-closed (A-EC), eyes-open (EO), and drowsy (D-EC) artifact-free epochs of 4,096 samples   epochs were selected from the first 3 min of the recording. They were characterized by the absence of a clearly visible posterior dominant “background” pattern and the presence of eye-blink artifacts . The other epochs were selected from the second part of the recording refer to . Alert en = 10) and eyes-closed alert (n = 10) epochs could be used. Also, only 15 cases had 10 artifact-free epochs in the awake and drowsy states. Results of this second analysis of only 15 cases but with more data per case (10 epochs) are presented in the To retain a sufficient number of subjects with enough and a comparable number of epochs in all states, we decided to start with the selection of subjects with at least 5 epochs per condition/state per subject . A total of 19 subjects  could be included based on this selection criterion. After the analysis of the dataset with 5 epochs, we decided to reanalyze our data with 10 epochs per subject. Unfortunately, a majority of these cases had artifacts in the eyes-open part of the recording, so only the drowsy , theta (4–8 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta (13–30 Hz), and gamma (30–48 Hz)] by fast Fourier transform of the data; setting activity outside passband to zero, followed by inverse Fourier transform . This resulted in 6 sets of 78 time series . From these time series, the relative power for each band and peak frequency (frequency with the maximum power in the 4–13 Hz range) were calculated. Global cortical relative power for the different frequency bands was constructed as mean relative power over all 78 cortical ROIs and 5 epochs (or 10 epochs for dataset 2). Similarly, average peak frequency was estimated as the mean peak frequency over all 78 cortical ROIs and 5 (or 10) epochs. Refer to Subsequently, we estimated functional connectivity between 78 AAL regions using two frequently used methods; the phase lag index (PLI)  subgraph was constructed using the PLI or AECc connectivity matrix  (a star-like topology). Leaf fraction is the leaf number divided by the maximum possible leaf number: Lf = L/M. Tree hierarchy (TH) characterizes a hypothesized optimal topology of the efficient organization while preventing information overload of central nodes. For a line-like topology Th = 0, for a star-like topology Th = 0.5, and for trees with a configuration between these 2 extreme situations, Th can have values of Th → 1. BC of a node u is defined as the number of shortest paths between any two nodes i and j in the network that are passing u, divided by the total number of shortest paths. BC ranges between 0 (leaf node) and 1 . The BC of the tree was characterized by the BCmax, i.e., the BC of the node with the highest BC in the tree. Nodes with a high BC are considered “hub nodes” not only based on their number of connections, but also on their importance for global communication in the network. Maximum BC describes the importance of the most central node, which is a measure of central network organization  using the false discovery rate (FDR). An alpha <0.05 after the correction was considered statistically significant. All analyses were performed for the dataset with all states  as well as for the dataset with more epochs, but fewer cases (subjects with A-EC and D-EC data). All statistical analyses were performed in IBM SPSS Statistics 26 and Microsoft Excel 2016.Differences between average values per subject or group  and individual ROIs per subject or group  were compared between the 3 different states or conditions  using the pairwise Time series of 66 healthy controls were screened for the presence of at least 5 epochs in the alert eyes-closed, drowsy eyes-closed, and eyes-open states or conditions. Recordings were on average of 35 epochs long and consisted of 13 epochs in the EO state for all of the subjects (by design) and a median of 22 epochs (range 13–33 epochs) for the eyes-closed state. The eyes-closed epochs  were then scored for the presence of the alert (A-EC) and drowsy (D-EC) states. There were 793 (55%) drowsy epochs and 544 (37%) alert epochs. We excluded 97 (6%) epochs with a clear artifact (mainly frequent eye-blinking in the EO condition) and 14 (1%) epochs with spontaneous eyes opening during the eyes-closed condition. n = 5) in each state or condition, so a total of 15 epochs were included per case. There were 13 women and 6 men. The mean age was 40.3 (SD 8.4) years. As also mentioned in the Section Methods, we additionally analyzed how many cases of these 19 subjects we could use if we were to include 10 epochs in each state. Then, only 15 cases  had sufficient epochs in the awake and drowsy states (n = 10). Results of this second analysis of only 15 cases but with more data per case are presented in the Ultimately, only 19 subjects had sufficient epochs  and drowsy (D-EC) states. Relative theta power was significantly higher in D-EC epochs vs. A-EC epochs whereas alpha2 power, beta power, and peak frequency were significantly lower in drowsy epochs. A second analysis [fewer subjects but more epochs (10 instead of 5)] in the alert and drowsy states confirmed these findings refer to .Not surprisingly, the differences in spectral power also translated to regional differences  and the studied MST metrics (based on the PLI and AECc) in both datasets refer to , 2. RepeThere were no differences in functional connectivity with the PLI between the A-EC and EO groups. Regarding network topology, however, there was a significantly higher MST TH in the theta and beta bands in the EO condition using the PLI.There was only a difference in the AECc in the delta band between the EO and A-EC conditions. Opened eyes were related to a significantly higher delta band connectivity with the AECc. These differences also translated to changes in network topology. There was a higher LF and a higher BC in the EO condition. There were no differences in the tree hierarchy. Delta band regional BC did not differ between both states refer to .This study investigated the effects of drowsiness and opened or closed eyes on neurophysiological measures of activation, connectivity, and network topology. Drowsiness was present in a large part of eyes-closed resting-state epochs. There were significant spectral changes between alert (A-EC) and drowsy (D-EC) states and between the eyes-open (EO) and eyes-closed conditions. Estimates of functional connectivity only differed for the AECc in the delta band between the eyes-open (EO) and eyes-closed (A-EC) conditions. Additionally, there were changes in network topology between eyes-open (EO) and eyes-closed (A-EC) conditions. Drowsiness was of far less influence on connectivity metrics or network properties.State fluctuations in subjects during resting-state recordings are a commonly encountered phenomenon , and the absolute and relative power densities are quite consistent when computed from artifact-free time series of 20 s to 4 min  and MST metrics are far less—if at all—influenced by state changes. Previous work in EEG suggested differences in coherence between sleep stages  for the presence of sufficient drowsy, awake, and eyes-open epochs. We felt it to be important that sufficient data were available for all three states to be able to properly assess inter-individual differences with the appropriate pairwise tests. Ultimately, only 19/66 subjects had enough epochs in all 3 states. We do however believe that it is possible to draw valid conclusions on this subset. The states clearly differ in terms of the spectral analysis . In addition, although there is a significantly altered relative power, connectivity measures remain rather stable or unchanged. Additionally, a very recent (2022) publication by Wiesman et al. shows that the stability of spectral resting-state MEG measurements can be robustly estimated in most cortical regions when derived from relatively short segments of 30–120 s of resting-state data. We have used a minimum of 5*13 s —have been applied to EEG data , and this has the potential to influence brain connectivity studies significantly. It is of note that we found many of these events in healthy controls, and it may be even more extensively present in persons with neurological diseases that lead to cognitive disturbances. Epoch selection by trained raters is therefore of utmost importance, especially when performing spectral analyses. For functional connectivity analyses using the AECc and/or PLI, as well as studies on network topology, it seems mainly important to assess whether subjects really held to the eyes-closed/eyes-open instructions. Also, for future studies into neurodegenerative diseases, especially when connectivity metrics are used other than the AECc/PLI, it is important to test the influence of state to rule out any physiological effect coming from the spectral changes that are the result of drowsiness.Drowsiness during eyes-closed resting-state MEG/EEG recordings was present in the majority of epochs. This had considerable influence on spectral power but not on connectivity and network topology. These findings are important for future studies that use EEG/MEG to study the dynamics of neurological disease. Recordings should be evaluated for the presence of drowsiness when spectral analyses are performed. For connectivity analyses or studies on changes in network topology, this is far less important.The raw data supporting the conclusions of this article will be available to qualified researchers upon request from the corresponding author. The MEG time-series cannot be shared publicly due to privacy regulations regarding data sharing outside of the European Union.The studies involving human participants were reviewed and approved by METc Amsterdam UMC, Locatie VUmc. The patients/participants provided their written informed consent to participate in this study.ES, YT, AH, and CS conceptualized the study, IN, SK, DS, LR, and MS collaborated, performed the data analysis, interpreted the results, wrote the first draft, reviewed, and edited the manuscript for intellectual content. DS, LR, and MS collaborated in interpreting the results and in reviewing and editing the manuscript for intellectual content. All authors contributed to the article and approved the submitted version.MS has received compensation for consulting services or speaker honoraria from ExceMed, MedDay, Atara, Sanofi-Genzyme, and Biogen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


CWC27 and CLUAP1), ultraviolet protection , and pigmentation (e.g. PDE4D) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds , and breeds with a non-white headed phenotype ; b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds  and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian , and the largest in Deep Red . By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g.,  Traditional native breeds are an important source of genetic variability adapted to local environments. They might harbor genetic variants unique to the breed due to ecosystem adaptation and, e.g. provide resistance to local diseases and/or extreme climatic conditions. Detailed analyses of the genomic structure of those native breeds can contribute to improving the knowledge about breed formation, and identify genes and variants with a significant impact on the adaptation processes that shaped animal phenotypes –4. This The skin and coat color variation in livestock breeds are important traits that impact the adaptation of breeds to the environment –8. In thThe Groningen White Headed (GWH) breed, originated from the Groningen province of the Netherlands, is a dual-purpose cattle known for its longevity, minimal veterinary costs, and high fertility rate . The firt (UV) radiation in this region [In cattle, Ambilateral Circumocular Pigmentation (ACOP) can be distinguished by a white face and colored areas around the eyes in breeds such as the GWH  and Flecs region , which ws region .The molecular genetic background of GWH breed has not been extensively studied . TherefoThis study was conducted following the animal experimentation policy of Wageningen University & Research. The cattle blood samples were collected by a veterinarian during yearly routine health inspections with written informed consent by the owners. Therefore, no Ethics Committee approval for animal care was needed for this research.https://www.optibov.com/) and in collaboration with the respective breed associations, including 5 Holstein Friesian; 21 GWH; 23 Meuse-Rhine-Yssel; 23 Dutch Belted; 24 Dutch Friesian; and 24 Deep Red. In total, 92 cows and 28 bulls were included in this study (for more details see S1 Table). Secondly, white headed animals with no ACOP were retrieved from two more breeds  included in the 1000 Bull Genomes Project (Run9 version) [We used 170 animals from eight breeds. We first sampled 120 unrelated animals as part of the LEAP-Agri project OPTIBOV (The GENTRA Blood kit (Qiagen N.V.) was used for the isolation of genomic DNA from EDTA blood samples. The quantification and quality of the obtained DNA were assessed using the Qubit fluorometer (Qiagen N.V.). DNA was paired-end sequenced (read length of 150 base pair) as single-indexed genomic libraries using the Illumina Novaseq6000 . Finally, raw reads were preprocessed by trimming the adapter sequences and removing the reads with 50% of low-quality nucleotides and fewer than 36 base pairs in length with fastp v0.23.1 .The mem option from BWA v0.7.17-r1188  was used2 < 0.5 between variants within a window of 50 SNPs and with a genotyping rate > 0.95. The results from the PCA were visualized using the R package ggplot2 v3.3.5 [We used PC analysis to assess the population structure of the Dutch cattle breeds. This analysis was conducted using the variance-standardized relationship matrix  with PLI2 v3.3.5 .K ranging between 2 and 6. The analysis was performed with a total of 120 unrelated animals from Dutch breeds and included 1,354,139 autosomal variants with a r2 < 0.5 within windows of 50 variants over the genome and a minor allele frequency (MAF) > 0.05.Individual ancestry was evaluated by a model-based clustering method with the ADMIXTURE software v1.23 . This mey was the binary phenotype, one for the GWH individuals with ACOP and zero for Dutch Belted, Deep Red, Meuse-Rhine-Yssel, Dutch Friesian, and Holstein Friesian; W the matrix of incidence for the fixed effects; α the intercept vector of ones; x contains the vector with SNP genotypes by sample; δ represents the marker effect size; u contains a vector with the random genetic effects that follow a n-dimensional multivariate normal distribution with u ∼ MVNn  for n individuals and being λ the ratio from two components of variance, τ− 1 is the variance of the residual error, and K the kinship matrix derived from the genotypes from each sample; ε  ∼ MVNn  the vector containing the errors, with I representing the identity matrix. The nominal p-values from the association study were corrected using the false discovery rate (FDR) approach implemented in the R function p.adjust [q-value (from the FDR test) lower than 0.001 as significantly associated. Here, a QTL and the co-localization between QTLs and significant selective sweeps were defined following the method reported by Gonzalez-Prendes et al. [A genome-wide association study was used to identify and characterize genome regions that differ between GWH and other breeds to find out candidate genes funtionally related with pigmentation and/or the eye protective phenotypes, e.g. ACOP. We used a mixed-model approach developed by Zhou and Stephens  in the Gp.adjust  and Benjp.adjust  method. s et al. . In brieIn a second step, variants from two additional breeds  with white heads and no ACOP were retrieved from the 1000 Bull Genomes Project (Run9 version) , 25 to pv3.0.3 [ω-statistic is calculated based on patterns of linkage disequilibrium close to a recently fixed mutation. A high value of ω-statistic at a specific genomic location can indicate a genomic region under selection. In this method, we used the same window size of 5 kb bins across the genome and outlier regions with the highest values (top 1%) of ω-statistic were considered significant. Finally, only candidate selective sweeps within the 1% of the highest scores obtained by both methods were annotated using Ensembl 101 [The variants identified in each sample were used to explore the presence of genomic regions under selection in each breed with two complementary methods. First, Sweep Detector (SweeD) v3.0  softwarev3.0.3  was applembl 101  databaseThe detection of ROH in the GWH breed was implemented with detectRUNS v0.9.6  program.S1 Table). The number of variants per breed, biallelic variants and variants that are specific to each breed are shown in Table 1. The overall number of annotated variants was 21,313,663, and the number of SNPs per animal  and per breed  are within the range of that obtained in other studies on B. taurus [S2 Table).After the mapping of the Dutch cattle breeds and Holstein Friesian short read sequences to the bovine reference genome (assembly ARS-UCD1.2), the depth of coverage across samples, in average, was 8.7X ranging from 7X to 13X  supported the high differentiation of the GWH breed at K = 3 in an independent genetic cluster. The separation of Dutch Friesian and Dutch Belted breeds occurred at K = 4, and finally the Meuse-Rhine-Yssel and Deep Red formed two distinct clusters at K = 5, which had the smallest CV error (0.54), reflecting their close genetic relationship [The genetic relationships between samples were evaluated using a PCA approach. As shown in ulations . While, ulations , from alulations . The PC2tionship . In thistionship . In the tionship .Fig 1B). Animals represented in Fig 1B were used for the GWAS in the second step. The PC1, which explains 30% of the observed variation, divided the animals with and without ACOP and confirms the genetic differentiation of the GWH breed. The PC2, divided the Hereford and Simmental breeds into two clear clusters indicating two separate populations in accordance with previous reports [S1 Fig).With the separation of the GWH population from the non-white-headed breeds, we decided to investigate if this breed, with ACOP, is also isolated from white headed breeds without pigmentation around the eyes, that are, Hereford and Simmental (without ACOP). The PCA separated the breeds into three clusters based on their genomic information , 137 genome hits  with more than one significantly associated variant were detected. The associated regions were distributed across the 29 chromosomes  and the regions with the most significant associations  and with the highest number of associated variants (>100 significant associations) mapped to BTA4 (20.0–29.9 Mb and 116.8–118.8 Mb), BTA5 (10.1–19.7 Mb), BTA12 (12.0–18.5 Mb), BTA15 (50.6–59.8 Mb and 60.3–67.8 Mb), BTA20 (10.2–19.9 Mb and 20.2–29.5 Mb) and BTA21 (0.4–8.8 Mb). A total of four genomic regions co-localized with those detected by Pausch et al. [Pausch et al. [Pausch et al. [The GWA study was used to identify and characterize genome regions that differ between GWH and other breeds to find candidate genes possibly associated with pigmentation and/or eye protective phenotypes e.g. ACOP, which is typical of GWH breed. Animals with ACOP (GWH) were classified as cases and animals of the Dutch Belted, Deep Red, Meuse-Rhine-Yssel, Dutch Friesian, and Holstein Friesian breeds were considered as controls. At the genome-wide level , and 100 (53.4%) co-localized with the QTLs identified when the six breeds were included in the analysis (S4 Table). This result may suggest that those regions specifically affect the GWH breed and may be associated with its color phenotype. Interestingly, the QTL on BTA5 , was also identified by contrasting GWH vs breeds without white head . Pausch et al. [As there were no GWH animals with a completely white head and without ACOP, 50 animals from Hereford and Simmental breeds were selected from the 1000 Bull project . These dOur GWAS analyses were limited by the fact that significantly associated genomic regions can be observed due to the different genetic backgrounds between the breeds. This confounding effect should either be eliminated through a better study design (e.g. F2 crosses with another white face breed that does not show ACOP) –62 or byS5 Table). The candidate regions were distributed across the 29 autosomes  with sizes that ranged from 3.4 kb to 140.4 kb and a mean of 17.8 kb. The breed with the lowest number of candidate regions was GWH (31), followed by Meuse-Rhine-Yssel (40), Dutch belted (41), Dutch Friesian (46), Holstein Friesian (48), and Deep Red (51). The highly significant BSSS migth indicate “divergence signals” between breeds [Fig 4). When we evaluated the co-localization between the BSSS (±500 kb up-and downstream) in GWH and QTLs detected by GWAS, eight genomic regions were also mapped with all methods  as follows: one on BTA4 (70.2–71.3 Mb), one on BTA5 (10.0–19.7 Mb), one on BTA10 (26.9–29.4 Mb), one on BTA13 (60.0–61.4 Mb), one on BTA15 (55.5–59.8 Mb), two on BTA20  and one on BTA25 (0.5–9.2 Mb).We used the whole genome resequencing data from six cattle breeds  to find out breed-specific selective sweeps (BSSS) in the GWH breed with two complementary methods: SweeD, which detects selective sweeps based on the variant frequencies using a composite maximum likelihood approach ; and OmeS6 Table). Several of the candidate selective sweeps highlighted loci which were mainly associated with milk quality, milk production, feed efficiency, body weight, and several meat-related phenotypes. To be noted, these results are in line with the economic objective established for the studied breeds; Dutch local cattle  have been selected for dual-purpose characteristics including milk production. Although our candidate selective sweeps were selected as unique in each breed, we still can find BSSS affecting the same trait. This can be explained by the fact that in livestock populations, including traditional cattle breeds, the selection for economically important traits, e.g. complex traits, might happen across many loci with small effects [We also evaluated if the candidate selective sweep co-localized with known bovine QTLs deposited in the AnimalQTLdb  database effects , 65. The. With this approach, 57 genomic regions were detected as ROH hotspots. With their genomic coordinates, we were able to reveal genomic regions that co-localize with the previously detected BSSS and GWAS signals. Five genomic regions that mapped to BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 Mb and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb) were overlapped between the three methods, and thus genes on those regions are probably under selection in the GWH breed [The identification of the genomic regions in ROH in the GWH breed was implemented as a complementary method to confirm and reduce the number of candidate genomic regions that co-localize between the GWAS signals and BSSS. We found 4,911 ROH regions that cover on average a total of 207.4 Mb of the genome. Of these ROH regions, around 73%  can be classified as small (0.5–1 Mb) regions, indicating more ancient consanguinity or population founder effects . This reWH breed , 69.Fig 5). These regions included 141 genes (S9 Table), of which some are functional candidate genes. For example, on BTA 5 (12–17 Mb), the transmembrane o-mannosyltransferase targeting cadherins 2 (TMTC2) located at 12.2 Mb, is associated with calcium ion homeostasis [KITLG) locus , which encodes a ligand for the receptor-type-tyrosine kinase KIT and contributes to coat color in various species, including cattle [DEPDC1B (DEP domain-containing protein 1B) gene at position 18.5–18.6 Mb, which is associated with the hyperproliferation of abnormal melanocyte cells [DEP domain [cell polarity [We also investigated whether the function of the positional candidate genes are specifically associated with pigmentation and/or metabolism of melanocytes. First, we focused on genes that mapped to regions that overlapped between ROH hotspots, BSSS, and the GWAS signals   in our list related with retinal diseases, for example CWC27 (CWC27 Spliceosome Associated Cyclophilin) associated with Retinitis Pigmentosa [CLUAP1) in the vertebrate eye is important for ciliogenesis and photoreceptor maintenance [Michot et al. [RP1 gene that causes loss of vision in cattle populations.Interestingly, there are two genes . We studied the top ten significant SNPs in these regions to identify putatively associated genes. Nine of these SNPs mapped to four genes , and 11 of them to intergenic regions (Table 2). The linkage desequilibrium between those SNPs was high, ranging from r2 = 0.91 to one (Table 2), and one of these SNPs  mapped to the 3′UTR of the PDE4D gene. SNPs located in 3′-UTR sequences may abolish or create a microRNA target and consequently may lead to different activities of the gene thereby contributing to interindividual variability [The analysis of the whole genome resequencing data allowed to identify variants within candidate genomic regions that can help to clarify the cause of the phenotypic differences that exist between GWH and the remaining breeds. We investigated the genomic regions on BTA20 (10.0–19.9 Mb and 20.0–22.7 Mb) because those regions contained the most significant associations at three levels  mapped to the Phosphodiesterase 4D (PDE4D) gene. PDE4D is involved in the degradation of the Cyclic AMP. In humans, the skin pigment production and its protection against the UV radiation improved with the up-regulation of cAMP in melanocytes [PDE4D loci mediated by the MC1R-cAMP-MITF pathway led to a reduced melanocyte pigmentation in mice [NDUFAF2) or Related Protein Rab-3C (RAB3C) genes with coat color or melanogenesis. However, the RAB3C gene is part of the Rab GTPases proteins, which were involved in cell membrane trafficking and associated with melanosomes [p-value = 1.74E-24) is the ERCC Excision Repair 8 (ERCC8) gene, involved in protein ubiquitination and UV response. In humans, the ERCC8 gene is associated with Ultraviolet-sensitive syndrome [Four of the most significant SNPs , UV protection (e.g. ERCC8), or retinal degeneration . This finding contributes to characterizing the genetic background of the GWH breed and provides insights to further investigate the biological pathways and causative mutations influencing skin pigmentation and/or eye protective phenotypes e.g. Ambilateral Circumocular Pigmentation, and the biological implications of skin pigmentation for animal adaptation.The used integrative approach based on the combined use of GWAS, selective sweep and ROH analyses identified several regions of the cattle genome  as candidates to explain phenotype variation in the GWH breed. Importantly, those regions contained breed-specific genetic markers and candidate genes that are functionally related with pigmentation  were distantly positioned from all other breeds.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.