Agriophyllum squarrosum) make it attractive for use as an alternative crop in response to concerns about ongoing climate change and future food security. However, a lack of genetic information hinders understanding of the mechanisms underpinning the morphological and physiological adaptations of sand rice. In the present study, we sequenced and analyzed the transcriptomes of two individuals representing semi-arid [Naiman (NM)] and arid [Shapotou (SPT)] sand rice genotypes. A total of 105,868 pairwise single nucleotide polymorphisms (SNPs) distributed in 24,712 Unigenes were identified among SPT and NM samples; the average SNP frequency was 0.3% (one SNP per 333 base pair). Characterization of gene annotation demonstrated that variations in genes involved in DNA recombination were associated with the survival of the NM population in the semi-arid environment. A set of genes predicted to be relevant to heat stress response and agronomic traits was functionally annotated using the accumulated knowledge from Arabidopsis and several crop plants, including rice, barley, maize, and sorghum. Four candidate genes related to heat tolerance , seed size , and flowering  were subjected to analysis of the genetic diversity in 10 natural populations, representing the core germplasm resource across the area of sand rice distribution in China. Only one SNP was detected in each of HsfA1d and DAR1, among 60 genotypes, with two in ELF3 and four in LHY. Nucleotide diversity ranged from 0.00032 to 0.00118. Haplotype analysis indicated that the NM population carried a specific allele for all four genes, suggesting that divergence has occurred between NM and other populations. These four genes could be further analyzed to determine whether they are associated with phenotype variation and identify alleles favorable for sand rice breeding.The extreme stress tolerance and high nutritional value of sand rice ( Agriophyllum squarrosum), has been among crops used for army provisions since the Tang Dynasty (AD 618–907) and is still an important component of local food for people inhabiting the Hexi Corridor along the ancient Silk Road in the northwest of China , is essential for in-depth analysis of genes and alleles involved in plant evolution and environmental adaptation . Extensitication .Figure 1]. Moreover, the flowering of NM sand rice occurs earlier than that of SPT at the common garden; SPT individuals are more tolerant to heat stress. Large scale analyses of genetic variation will be crucial for understanding the genetic mechanisms underlying adaptation of sand rice to climate features or local environmental conditions, and provide a foundation for the discovery and isolation of markers useful for its subsequent domestication.The climate varies considerably across the geographic range of sand rice, and precipitation and mean temperature of the coldest quarter strongly influence its distribution . FurtherArabidopsis have elucidated complex pathways regulating heat stress response, seed size, and flowering time (late elongated hypocotyl (LHY) and early flowering 3 (ELF3) are morning- and evening-phased components involved in circadian rhythm regulation in Arabidopsis  was used to amplify the fragments with 1.5 μl template DNA in 20 μl reaction volume. The PCR reaction was performed in a C1000 TOUCH thermal cycler and the amplified products were sequenced using an ABI Prism 3730xl sequencer at Majorbio .HsfA1d, DAR1, LHY, and ELF3). Samples from two individual plants from the SPT and NM populations were first amplified and sequenced in both directions to confirm the suitability of primers for population sequencing. PCR and sequencing were performed as described above. Population sequencing results for each candidate gene were aligned using BioEdit version 7.2.5  as described in on 7.2.5 . After ron 7.2.5 .Figure 2A). The average GC content was 39.14, and 69.36% of the total Unigenes had GC contents more than 35% . All Unigenes were then blasted against six public protein databases, including the Nr, Swissprot, COG, GO, KOG, and KEGG, with a threshold of less than 1E-5. To increase the number of annotated Unigenes, predicted proteins were also aligned with the Pfam database. As shown in Table 1, 34,939 Unigenes (38.02%) could be matched to protein sequences or domains available in these databases.An NM sand rice transcriptome library was constructed by mixing shoot and root RNA in a 1:1 ratio and pair-ended sequencing yielded 16.4 million clean reads. These reads were assembled, together with those from an individual sand rice plant from the SPT region : 81,980 were inter-individual, which were homozygous in both samples; 3,499 were specific to NM, 20,389 to SPT, these were heterozygous in one individual but homozygous in the other. Fifty Unigenes were randomly selected for validation the predicted SNPs, of which 30 were successfully amplified and sequenced. The failed amplication might be from amplicons spanned a large intron or primers located at the exon/intron boundaries. Finally, 38 out of 50 SNPs were validated in these 30 Unigenes and another six were confirmed as homozygous SNPs. The predicting accuracy of our SNP dataset reached 76% . A histogram of SNP frequency revealed a peak at 0.2 and 61.24% Unigenes were under the average SNP frequency of 0.3% . Among the 24,712 Unigenes, 22,538 contained inter-individual SNPs, and 1,534 and 9,278 contained NM- and SPT-specific SNPs, respectively . There were 16,756 Unigenes containing only one type of SNP, while 682 had all three types. Categories were named according to the gene number in each set in the Venn diagram . The mean SNP frequency values for all categories were lower than 0.45%; however, the four categories  including two or three types of SNP, exhibited higher frequencies than the other categories . Detailed histograms illustrating SNP numbers and frequencies for each category are presented in Supplementary Figure Single nucleotide polymorphism distribution and frequency are important indices for genome wide SNP development. In this study, all pairwise SNPs were distributed in 24,712 of 91,884 reference Unigenes, with approximately 57% 14,130) containing fewer than three SNPs  were separately subjected to GO enrichment analysis (Table 2 and Supplementary Table p = 0.04). The GO term, ‘DNA recombination’ (GO: 0006310) was enriched for NM-specific category  and not for any of the other categories. Interestingly, the GO term, ‘thylakoid membrane organization’ (GO: 0010027), was found in the enrichment results of SPT-specific category (SR_9278) and SR_24712, suggesting genes in SR_6614 were largely responsible for this annotation (p = 0.03). SR_14777 included only genes containing inter-individual SNPs  and was enriched for seven GO terms, including ‘response to misfolded protein’ (GO: 0051788) and ‘response to cold’ (GO: 0009409). Of note, the GO term ‘embryo development ending in seed dormancy’ (GO: 0009793) was also enriched in categories SR_24712, SR_22538, and SR_14777.Genes in each category   from the Gurbantunggut desert (FK), Kumutage desert (DH), Qinghai-Tibetan Plateau (QHH and YJ), central deserts , and eastern sandy regions (NM), were used to investigate the genetic diversity of four candidate genes. Six genotypes in each population were sequenced and the successful genotypes for each gene ranged from 51 to 54 (Table 3). The HsfA1d gene was conserved in nine populations, with a single transversion SNP (T/A) leading to an amino acid change (Glutamate/Aspartate) detected in NM population . Alignment based on the RBH results demonstrated that this SNP was located between the regions encoding the transactivation domain (AHA) and the nuclear export signal (NES) at the C terminus of Arabidopsis HsfA1d. For the seed size gene DAR1, an intron (125 bp) was included in the PCR products and population sequencing identified a single transversion SNP (G/C), specific to the NM population. This synonymous SNP encoded an amino acid located between the LIM and the LIM-associated C-terminal domains  and one was a transversion (A/T) leading to an amino acid change  was NM-specific and synonymous, while the other (C/T) was observed in five of six genotypes in the JB population and encode an Alanine to Valine amino acid substitution  to 0.00118 (LHY), while haplotype diversity ranged from 0.208 (HsfA1d) to 0.352 (ELF3).Phylogeographic analysis has revealed that sand rice originated from the Gurbantunggut desert and then dispersed into the central desert region and eastern sandy lands in China . In thisFigures 2, 3). There were a larger number of SNPs specific to SPT  than to NM , implying that sand rice in the SPT region may exhibit a higher degree of outcrossing, and, consequently, increased genomic heterozygosity. GO enrichment analysis identified only one term  significantly enriched for SR_1534 (Table 2), suggesting that variations in specific genes were required for the survival of the NM population in the semi-arid environment.Dissection of the genetic variation of sand rice is essential for understanding its extreme stress tolerance and phenotypic variability and will facilitate its future domestication. In this study, a total of 105,868 pairwise SNPs, distributed in 24,712 Unigenes, were identified between SPT and NM samples, with an average SNP frequency of 0.3% , which is involved in silencing the stress-induced expression of transposons and thereby in preventing the transgenerational transmission of epigenetic memory , resulting in ineffective activation of the transcription network, which would be consistent with the reduced heat tolerance phenotype associated with NM plants  between M plants .LHY and ELF3 are important regulators of the circadian rhythm (LHY and ELF3 alleles which were separated from other populations. These results suggest that variants in these two genes could confer some fitness advantage, such as early flowering, in the semi-arid environment. Furthermore, two transition SNPs of LHY, were shared between JB and NM populations. This is congruent with the speculation that the sand rice colonization pathway originated from the Gurbantunggut desert and subsequently dispersed into other desert regions (n rhythm . The eas regions .PZ conceived and designed the project. JZ prepared the RNA for sequencing. CQ and X-FM supplied the population DNA samples. QZ and XZ helped to perform experiments. PZ conducted the experiments, analyzed the results, and wrote the manuscript and GC revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


EPHA2) have been associated with inherited forms of pediatric cataract, whereas, frequent, non-coding, single nucleotide variants (SNVs) have been associated with age-related cataract. Here we sought to determine if germ-line EPHA2 coding SNVs were associated with age-related cataract in a case-control DNA panel (> 50 years) and if somatic EPHA2 coding SNVs were associated with lens aging and/or cataract in a post-mortem lens DNA panel (> 48 years). Micro-fluidic PCR amplification followed by targeted amplicon (exon) next-generation (deep) sequencing of EPHA2 (17-exons) afforded high read-depth coverage (1000x) for > 82% of reads in the cataract case-control panel  and > 70% of reads in the post-mortem lens panel . Novel and reference (known) missense SNVs in EPHA2 that were predicted in silico to be functionally damaging were found in both cases and controls from the age-related cataract panel at variant allele frequencies (VAFs) consistent with germ-line transmission (VAF > 20%). Similarly, both novel and reference missense SNVs in EPHA2 were found in the post-mortem lens panel at VAFs consistent with a somatic origin (VAF > 3%). The majority of SNVs found in the cataract case-control panel and post-mortem lens panel were transitions and many occurred at di-pyrimidine sites that are susceptible to ultraviolet (UV) radiation induced mutation. These data suggest that novel germ-line (blood) and somatic (lens) coding SNVs in EPHA2 that are predicted to be functionally deleterious occur in adults over 50 years of age. However, both types of EPHA2 coding variants were present at comparable levels in individuals with or without age-related cataract making simple genotype-phenotype correlations inconclusive.Rare germ-line mutations in the coding regions of the human EPHA2 gene ( Cataract(s) is a clinically heterogeneous disorder that causes clouding or opacification of the crystalline lens and, thereby, impairs refraction and focusing of light onto the photosensitive retina of the eye. Typically, cataract is acquired with aging (> 50 years) and, despite surgical treatment, age-related cataract remains a leading cause of adult visual impairment 17%-33%) and blindness (33%-51%) worldwide 7%-33% an–6. In aderythropoietin-producing hepatocellular-carcinoma (EPH) sub-family of receptor tyrosine kinases (RTKs) that play critical signaling roles in embryonic development, adult tissue homeostasis, and cancer development and progression [eph-receptor interacting (ephrin) ligands and cytoplasmic  domains including a tyrosine kinase (TK) signaling domain and a sterile-α-motif (SAM) domain implicated in receptor clustering and protein-protein interactions [EPH-receptor A2 (EPHA2) is a member of the gression –20. Struractions ,22. Firsractions , EPHA2 iractions ,25, wherractions –29.EPHA2) on chromosome 1p that underlie inherited forms of pediatric cataract exhibiting both autosomal dominant and recessive modes of inheritance [EPHA2-related cataract may present at birth , during infancy or during childhood and displays variable clinical morphology including posterior polar opacities, nuclear opacities, cortical opacities and total lens opacities (https://sites.wustl.edu/catmap). Currently, the EPHA2 mutation spectrum includes 14 missense mutations predicted to result in amino-acid substitutions, one nonsense mutation, and five frame-shift mutations predicted to result in either C-terminally truncated or extended proteins. Most of these mutations (13/20) occur in cytoplasmic domains of EPHA2 with four mutations clustered within the SAM domain and two in the TK domain. Ectopic overexpression studies in cultured cells suggest that mutations in the SAM domain destabilize the receptor and/or impair targeting to the plasma-membrane [Genetic studies have identified germ-line mutations in the human EPHA2 gene  and mixed forms of lens opacities in Caucasian/European, Asian/Indian and Chinese populations [in silico prediction analysis suggests that several other missense SNVs in EPHA2  may have deleterious effects on receptor function [EPHA2 coding SNVs  in cultured lens epithelial cells has been associated with receptor destabilization and increased susceptibility to oxidative stress [EPHA2 may increase susceptibility to age-related forms of cataract. Here we sought to determine whether rare coding SNVs in EPHA2, of either germ-line or somatic origin, were associated with lens aging and/or age-related cataract.Beyond rare mutations, single nucleotide polymorphisms/variants (SNPs/SNVs) across the ulations , 46–50. ulations . Furtherfunction  and expre stress . These oEthical approval for this study was obtained from the University of Parma, the National Eye Institute, and Washington University (IRB ID #: 201111056 and 00–0320), and written informed consent was provided in accordance with the tenets of the Declaration of Helsinki.Genomic DNA was extracted using standard methods from blood samples donated by a case-control cohort of unrelated individuals age ≥50 years form Northern Italy that were ascertained from the Clinical Trial of Nutritional Supplements (CTNS) and Age-Related Cataract Study ,54. Catahttp://ndriresource.org/). Lens genomic DNA was extracted using the DNeasy Kit  essentially according to the manufacturer’s protocol with the following modifications to mitigate the high protein-to-DNA content of the lens. Each lens was homogenized  in buffer ATL (360 ul) then digested  with proteinase K (40 ul 15 mg/ml). Samples were then diluted with buffer ATL (360ul) and re-digested  with proteinase K (40 ul) followed by centrifugation  to remove excess protein before processing through spin-columns according to the manufacturer’s instructions. DNA was eluted from the spin-columns in buffer AE (200 ul) and quantified (OD260) using a spectrophotometer . If necessary, samples were concentrated by air-drying in a laminar-flow hood and re-suspended in ultrapure water to give a minimum concentration of 50 ng/μl required for amplicon sequencing.Post-mortem human donor lenses  were obtained (on dry-ice) from the National Disease Research Interchange (www.novocraft.com) and processed using the Sequence Alignment/Map (SAM) tools software package and Picard programs (http://samtools.sourceforge.net/). Variants were called using the FreeBayes program (https://arxiv.org/abs/1207.3907) for germ-line variants and the VarScan 2 program for somatic variants [http://varscan.sourceforge.net). Finally, selected SNVs were confirmed in both directions by manual inspection using the Integrative Genomics Viewer (IGV) browser [http://software.broadinstitute.org/software/igv/).Targeted-amplicon deep-sequencing was performed using the Access Array Integrated Fluidic Circuit (IFC) System with custom designed and validated gene-specific adaptor-primers . Each IFC enables nanoliter-volume high-throughput PCR to generate amplicons (≤200 bp) across 48 samples in a single run for subsequent next-generation (deep)-sequencing (NGS). Briefly, DNA samples (50 ng) and primers were mixed ‘on-chip’ (48.48 Access Array IFC/pre-PCR IFC Controller AX), and PCR amplified (FC1 Cycler). Amplicons for each sample were pooled on-chip (post-PCR IFC Controller AX) then indexed with sample barcodes and NGS adaptors (Access Array Barcode kit) to produce 48 sequencer-ready libraries. Sequencing-by-synthesis was performed on the MiSeq platform . Paired-end reads were aligned to the human reference genome (hg19) with Novoalign  value of < 0.05 after correction for multiple testing was considered significant.Genetic association analysis and logistic regression analysis of selected SNVs found in the cataract case-control panel was performed using the Golden Helix SNP and Variation Suite 7 . Statistical comparison of somatic SNVs found in the post-mortem lens panel was performed using Fisher’s Exact Test by means of the online spreadsheet at p = 0.21). The sex distribution was 50% female and 50% male in the cases and 44% female and 56% male in the controls. There was no association between any cataract and sex in the case-control panel using chi-square test (p = 0.51).The cataract case-control panel comprised 225 leukocyte DNA samples from 161 patients with age-related cataract (age 50+) and 64 age-matched clear lens controls from the N. Italian population ,54. The p = 0.45). The sex distribution was 23% female and 77% male in the cataract lenses and 49% female and 51% male in the clear lenses. Despite the numerical sex difference in the cataract lenses there was no significant association between any cataract and sex in the post-mortem lens panel using chi-square test (p = 0.095).Post-mortem donor lenses were briefly examined at the time of procurement for the presence or absence of obvious age-related cataract prior to cryopreservation. However, the donor information report did not identify age-related cataract sub-types . Further, we cannot exclude the possibility that cataract in some of these donor lenses may have been associated with causes other than aging (e.g. uveitis). The post-mortem lens panel comprised 118 genomic DNA samples extracted from 74 clear lenses (37 pairs) and 44 cataract lenses (22 pairs) all obtained from Caucasian donors (age 48+ years). Two of the clear lens pairs failed amplicon sequencing and/or QC criteria leaving 114 lens samples  for variant analysis. The mean age of cataract lenses = 65.5 ± SD 6.67 (range 48–74 years) and the mean age of clear lenses = 64.06 ± SD 7.37 (range 48–78 years) with no significant difference between the two groups (EPHA2 (GeneID: 1969) spans ~31.8 Kbp on the short (p) arm of chromosome 1 (cytogenetic band region 1p36.1) [http://www.ncbi.nlm.nih.gov/gene/1969). Currently, the gene reference sequence (NG_021396.1) comprises 17 coding exons generating two transcript variants, NM_004431.4 and NM_001329090.1, encoding protein isoforms of 976 amino acids (NP_004422.2) and 922 amino acids (NP_001316019), respectively. For comparison with EPHA2, we simultaneously performed amplicon sequencing of the gene coding for cellular tumor antigen p53 (TP53)—a tumor suppressor gene that is known to acquire somatic mutations in several cancers (e.g. cutaneous melanoma) (http://cancer.sanger.ac.uk/cosmic). TP53 (Gene ID: 7157) spans ~19.15 Kbp on chromosome 17p13.1  and the gene reference sequence (NG_017013.2) comprises 11 coding exons giving rise to 8 transcript variants and 12 protein isoforms (a-l) ranging from 182–393 amino-acids (http://www.ncbi.nlm.nih.gov/gene/7157).We performed targeted-amplicon deep-sequencing of the coding regions (exons) of the human EPHA2 gene to identify germ-line single nucleotide variants (SNVs) in the cataract case-control panel and somatic SNVs in the lens panel.  1p36.1)  with a pEPHA2 and 15 amplicons for TP53. Across the cataract case-control panel the mean total number of reads was 418,214 with > 99% on target of which > 82% attained 1000x coverage (EPHA2 (part of exon-1) likely due to its high G/C content.Optimal custom design of PCR primer pairs (Fluidigm) to amplify exons for deep-sequencing resulted in 35 amplicons for coverage . Similarcoverage . All ampin silico predictions of damaging or deleterious effects at the protein level determined using appropriate algorithms (e.g. SIFT and PolyPhen-2). Binary versions (.bam files) of the Sequence Alignment/Map (.sam) files have been deposited with the NIH Short Read Archive (SRA Accession no. PRJNA384802).Following sequencing, germ-line SNVs in the cataract case-control panel (blood leukocyte DNA) were called using the FreeBayes program. Variant allele frequencies (VAFs) were calculated as a percentage by dividing the number of individual variant reads by the total number of amplicon reads and those SNVs with VAFs ≥ 20% were designated germ-line. Somatic variants in the lens DNA panel were called using the VarScan 2 program that was originally designed to call low-frequency (> 1%) somatic variants from deep-sequencing data derived from matched tumor (case) versus control tissue samples ,57. For EPHA2 in the cataract case-control panel detected 10 novel SNVs  and 20 reference SNVs (18 transitions) in the exon regions of EPHA2 at VAFs >20%—consistent with germ-line transmission  and four damaging . Since nine of the novel SNVs occurred only once in the panel, and the other only twice, we were unable to perform further statistical analysis.Exon deep-sequencing of smission . Of the p = 0.032) with nuclear cataract and nuclear cataract + PSC using Fisher’s Exact Test (p > 0.24). The remainder of synonymous reference SNVs occurred in cases and/or controls but were comparatively rare in the panel (MAF < 1%) hampering further statistical analysis.Of the reference SNVs, 12 were synonymous and eight were predicted to result in missense amino-acid substitutions . Of the act Test . CorrectTP53 in the cataract case-control panel detected no novel SNVs and only nine reference SNVs (5 transitions) of which five were also present in the COSMIC database (in silico (SIFT) to be damaging, with one  present at relatively high frequency in Caucasians (MAF 0.25) and in multiple cases and controls. However, rs1042522 was not associated with any type of cataract (p > 0.33) using Fisher’s Exact Test (p = 0.85). The other SNV  occurred at unknown frequency in the population and in only one case of cortical cataract preventing further statistical analysis.Exon deep-sequencing of database . Two of act Test . CorrectEPHA2 in the lens panel detected a total of 935 discordant SNVs (VAF > 1%) in 35 pairs of clear lenses and 726 discordant SNVs in 22 pairs of cataract lenses suggesting a somatic origin  to be damaging (Exon deep-sequencing of c origin  Tables. c origin . Of thesdamaging . Surprisdamaging .EPHA2 SNVs occurred with a VAF ≥ 3% in 10 of the 22 cataract lens pairs with only two excluded due to low read-depth (in silico (SIFT) to be damaging , damaging SNVs (p = 0.85), or novel SNVs (p = 0.64) using Fisher’s Exact Test (EPHA2 SNVs (p = 0.62), damaging EPHA2 SNVs (p = 0.63), or novel EPHA2 SNVs (p = 0.70).In the cataract lenses, 35 discordant ad-depth . The remad-depth . Of thesad-depth . Of the damaging . All 19 act Test . CorrectTP53 in the lens panel detected a total of 392 discordant SNVs (VAF > 1%) in 35 clear lens pairs and 298 discordant SNVs in 22 cataract lens pairs (in silico (SIFT) to be damaging and five occurred at UV-susceptible di-pyrimidine sites. Apart from the splicing SNV, none of the synonymous SNVs or UTR SNVs occurred at di-pyrimidine sites  occurred with VAFs > 3% in five of the 22 pairs of lenses including five synonymous SNVs, 12 non-synonymous or missense SNVs, and one UTR-3’ SNV , damaging SNVs (p = 0.77), or novel SNVs (p = 0.78) using Fisher’s Exact Test (p = 0.39), damaging TP53 SNVs (p = 0.71), or novel TP53 SNVs (p = 0.57).In the cataract lenses, 18 discordant R-3’ SNV . Of thesR-3’ SNV  Tables. R-3’ SNV . Overallact Test . CorrectEPHA2—particularly novel missense variants predicted in silico to result in deleterious amino-acid substitutions—that may be associated with lens aging and/or age-related cataract. First, we profiled germ-line SNVs (VAF > 20%) in EPHA2 for association with age-related cataract in a Caucasian case-control panel that had previously revealed association with common reference SNVs flanking EPHA2 [EPHA2 SNVs (n < 20) limited the power of this study to detect disease association. For example, of two novel SNVs located in the extracellular LBD of EPHA2 one (p.I142T) was present in a control, while the other (p.G171E) occurred in a case with cortical cataract. Similarly, one of the reference missense SNVs, rs116506614 , located in the TK domain of EPHA2, that has previously been associated with age-related cortical cataract [In this study we utilized targeted-amplicon (exon) deep-sequencing to identify germ-line and somatic variants of ng EPHA2 . Exon decataract , was preEPHA2 (VAF ≥ 3%) that arose in post-mortem lenses procured from Caucasian donors over 48 years of age  at relatively high frequencies (~27%). By contrast, EPHA2 harbors some 275 somatic mutations including 164 missense substitutions that have been detected in multiple tumor samples , at relatively low frequencies  (http://cancer.sanger.ac.uk/cosmic). These observations suggest that novel somatic variants in EPHA2 that are predicted to be functionally deleterious are detectable in aging human lenses. Overall, our data are in agreement with a recent study that employed targeted-hybridization deep-sequencing of human lens epithelial samples to identify somatic variants in a panel of 151 cancer-related genes [EPHA2 were present at comparable frequencies in both clear lenses and those with age-related cataract we are unable to determine if such variants are causative for disease.Second, we profiled putative somatic SNVs in A2 VAF ≥ % that arA2 VAF ≥ % that ardamaging  Tables. ed genes . To the EPHA2 detected here was the high frequency of transitions  versus transversions . Theoretically, transversions should occur twice as often as transitions; however, a review of the germ-line variation annotated in the EPHA2 reference sequence reveals that the vast majority of missense variants involve C/T or G/A transitions (http://www.ncbi.nlm.nih.gov/variation/view/). The occurrence of somatic C>T transitions is of particular interest since they may result from exposure to solar UV radiation [EPHA2 and TP53 raising the possibility that they represent UV-signature mutations , connexins (e.g. GJA8) and ocular transcription factors (e.g. HSF4) [A striking feature of both the germ-line and the somatic missense SNVs in adiation . Absorptadiation . Among ts Tables  Tables. g. HSF4) ,15 may pS1 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.


Enhancers are non-coding DNA sequences, which when they are bound by specific proteins increase the level of gene transcription. Enhancers activate unique gene expression patterns within cells of different types or under different conditions. Enhancers are key contributors to gene regulation, and causative variants that affect quantitative traits in humans and mice have been located in enhancer regions. However, in the bovine genome, enhancers as well as other regulatory elements are not yet well defined. In this paper, we sought to improve the annotation of bovine enhancer regions by using publicly available mammalian enhancer information. To test if the identified putative bovine enhancer regions are enriched with functional variants that affect milk production traits, we performed genome-wide association studies using imputed whole-genome sequence data followed by meta-analysis and enrichment analysis.We produced a library of candidate bovine enhancer regions by using publicly available bovine ChIP-Seq enhancer data in combination with enhancer data that were identified based on sequence homology with human and mouse enhancer databases. We found that imputed whole-genome sequence variants associated with milk production traits in 16,581 dairy cattle were enriched with enhancer regions that were marked by bovine-liver H3K4me3 and H3K27ac histone modifications from both permutation tests and gene set enrichment analysis. Enhancer regions that were identified based on sequence homology with human and mouse enhancer regions were not as strongly enriched with trait-associated sequence variants as the bovine ChIP-Seq candidate enhancer regions. The bovine ChIP-Seq enriched enhancer regions were located near genes and quantitative trait loci that are associated with pregnancy, growth, disease resistance, meat quality and quantity, and milk quality and quantity traits in dairy and beef cattle.Our results suggest that sequence variants within enhancer regions that are located in bovine non-coding genomic regions contribute to the variation in complex traits. The level of enrichment was higher in bovine-specific enhancer regions that were identified by detecting histone modifications H3K4me3 and H3K27ac in bovine liver tissues than in enhancer regions identified by sequence homology with human and mouse data. These results highlight the need to use bovine-specific experimental data for the identification of enhancer regions.The online version of this article (doi:10.1186/s12711-017-0331-4) contains supplementary material, which is available to authorized users. Genomic selection is a powerful tool that has rapidly accelerated genetic gains in the dairy industry . GenomicRecent research has indicated that much of the variation that affects complex traits lies in the non-coding genome , particuThe aim of this study was to identify bovine enhancer regions in silico based on sequence homology with functional annotation data in other species in addition to bovine ChIP-Seq data. We evaluated the influence of mutations in enhancer regions on complex production traits by performing a multi-breed genome-wide association study (GWAS) with imputed whole-genome sequence data in 16,581 cattle followed by meta-analysis and enrichment analysis.We used four public mammalian enhancer datasets to search for bovine putative enhancers, i.e. VISTA , FANTOM5Illumina BovineHD BeadChip 800 K SNPs  were available for 3311 Holstein bulls, 8478 Holstein cows, 875 Jersey bulls and 3917 Jersey cows. Among these individuals, 145 Holstein bulls and 47 Jersey bulls were from the 1000 Bull Genomes Project , and mosPhenotypes for the genotyped animals were available for milk production traits including fat yield (FY), milk yield (MY) and protein yield (PY) from the national dairy database operated by DataGene . The phenotypes used in the analyses were trait deviations (TD) for cows and daughter trait deviations (DTD) for bulls. TD were calculated based on cows’ lactation records (three lactations on average) and corrected for known fixed effects as per DataGene routine evaluations from the April 2013 official breeding value run. DTD were generated from nationwide progeny test data collected on many bulls’ daughters, and were corrected for known fixed effects such as herd, year and season. The animals used in our study were the same as or overlapped with those in previous publications , 37, 38.The reference genomes used in this analysis, GRCh38.p4 (hg38), GRCm38.p4 (mm10) and UMD3.1.1 (bostau8), along with their annotation files, were downloaded from the National Centre for Biotechnology Information (NCBI) Reference Sequence Database (RefSeq)  on 20 AuThe human and mouse enhancer regions from VISTA, FANTOM5 and dbSUPER were mapped to the bovine reference genome assembly UMD3.1.1 via command line applications Nucleotide Basic Local Alignment Search Tool (BLASTn)  (defaultiftOver)  (defaultiftOver) . The lififtOver) . The BLAiftOver)  were dirA multi-breed genome-wide association study (GWAS) was performed to detect imputed WGS variants that were associated with FY, MY and PY. Following the approach described by , Holsteies as in ; and \dotructure .The standard error of SNP effects is calculated as follows:P value output from EMMAX.The inverse-variance weight for each SNP is then calculated as:mentclass2pt{minimThe inverse-variance weighted effect for each SNP is then calculated as:The SNP effect from the meta-analysis that combines gender cohorts is calculated as:from Eq. , and \doentclass1pt{minimaThe variance of the SNP effect from the meta-analysis that combines gender cohorts is calculated as:entclass1pt{minimaThe P value from the meta-analysis that combines gender cohorts is calculated as:from Eq. , and \dofrom Eq. .The multi-breed GWAS results from bull and cows were combined using an inverse-variance weighting meta-analysis within a fixed effect model as described by . We did Variants with no effect or with a P value of 1 were removed from the downstream analysis. Of the 28,899,038 imputed WGS variants input for FY cohort, after the meta-analysis, 23,462,606 variants remained. Of the 28,899,038 imputed WGS variants input for MY cohort, after the meta-analysis 23,462,606 variants remained. Of the 28,899,038 imputed WGS variants input for PY cohort, after the meta-analysis 23,470,573 variants remained. Significant variants from the meta-analysis were selected using the same threshold as in the GWAS The bovine candidate enhancers were categorised into five enhancer sets based on their input databases: VISTA, FANTOM5, dbSUPER, Villar H3K4me3 or Villar H3K27ac. Two enrichment analyses, i.e. permutation test and gene set enrichment analysis (GSEA), were performed to examine if any of the bovine candidate enhancer sets were enriched with variants associated with FY, MY or PY. The permutation test compared the number of significant SNPs in an enhancer set with the null distribution sampled from the rest of the genome. However, the need for a predefined threshold for statistical significance in the permutation tests may result in not detecting relevant biological differences that are modest relative to the noise that is inherent to the data . This inThe permutation test was run for 10,000 random repeats to test if the number of significant SNPs in an enhancer set was significantly larger than that obtained by random chance. The numbers of SNPs and of significant SNPs in an enhancer set, and the number of SNPs in a random draw are denoted as The GSEA statistics was the cumulative sum of the effects of SNPs in putative enhancers computed from the sorted list of all SNP effects. Here, the effect was assessed by –log10 . At each point in the GSEA algorithm, the test statistic The significance of each GSEA was determined in a similar way as that for the permutation test described above. We randomly shuffled the SNPs within the sorted list while retaining the sorted positions of −log10  and recalculated the Two aligners, BLASTn and liftOver, were used to map human and mouse enhancers on the bovine genome Table . All setA pair-wise comparison was performed to examine the degree of overlap between the sets of bovine putative enhancer regions Fig. . Villar Given that enhancers are highly tissue-specific, we compared only liver-specific enhancers from homologous enhancer sets and Villar ChIP-Seq enhancer set. Only eight VISTA enhancers were liver-specific, which generated 236 bovine putative liver enhancers. We could not determine from which tissue FANTOM5 sequences originated. No liver data was available in the dbSUPER database. The bovine putative VISTA-liver enhancers overlapped very little with the bovine-liver H3K27ac (27%) and did not overlap at all with the bovine-liver H3K4me3 enhancers  [see Additional file The first goal of this study was to identify and improve the annotation of enhancer regions in the bovine genome. To create a library of bovine enhancers, we used publicly available human and mouse enhancer databases from VISTA, FANTOM5 and dbSUPER, along with the bovine enhancer data that were detected by ChIP-Seq from the Villar D et al. 2015) study. VISTA contains ultra-conserved developmental enhancer sequences with more than 96% of these being mapped to the bovine genome . Therefore, the homology-based enhancers that are relevant to milk production may not be present in our downloaded databases and therefore cannot be considered in this study. Second, although VISTA, FANTOM5 and dbSUPER may contain sets from tissues that are involved in the physiological processes that are fundamental for the regulation of milk production, the procedure to map these sequences to the bovine genome is based on the identification of conserved sequences with human and mouse sequences, and as a result, the bovine-specific mutations within the homology-based enhancers cannot be captured . Our resDGAT1 gene [DGAT1 mutation, we re-ran our GWAS on chromosome 14 by correcting for the effect of the DGAT1 gene by including the causative mutation in the model as a fixed effect. The correlations of the SNP effects  between before and after the correction were 85% (59%), which showed that there were other significant SNPs on chromosome 14 apart from the DGAT1 mutation. After correction, no significant SNPs remained in the VISTA and FANTOM5 enhancer sets for all milk production traits, but 34 to 67% significant SNPs remained in the Villar H3K4me3, Villar H3K27ac and dbSUPER enhancer sets [see Additional file DGAT1 gene. In addition, while the Villar H3K4me3 and dbSUPER enhancer sets had no corrected significant variants within the DGAT1 gene, the Villar H3K27ac enhancer set include one such significant variant (Chr14: 1797137 in FY and MY cohorts) in the first intron of the DGAT1 gene.On chromosome 14, the observed enrichment signal in enhancer regions could be due to SNPs in linkage disequilibrium (LD) with the well-known mutation in the AT1 gene . To accoMGST1 have been reported to be responsible for the QTL effect on chromosome 5 for milk production traits [Several candidate regulatory variants that affect the expression of n traits , 55. We GC gene. An RNA-Seq analysis [GC was most highly expressed in the liver and over-expressed in the mammary gland, and that there was a strong allele-specific expression in liver compared to 17 other bovine tissues [see Additional file Several studies have reported that the variant Chr6:88741762 is significantly associated with milk production traits . This vaanalysis  showed tThis study used mammalian enhancer prediction data and bovine trait association to provide a functional variance analysis of candidate bovine enhancer regions. Overall, our findings agree with previous research that enhancer sequences are species-specific and rarely conserved across species. We conclude that bovine-specific histone data such as H3K4me3 and H3K27ac are essential for the successful functional annotation of bovine enhancer regions. Although the amount of bovine enhancer information is limited, we have successfully identified many genomic regions as potential enhancers and demonstrated that variation in these regions is associated with variation in animal production traits. Future studies will benefit from the combination of information from topological domain association, expression quantitative trait loci and bovine ChIP-Seq data, such as that generated from the Functional Annotation of Animal Genomes (FAANG) consortium , to acceAdditional file 1: Figure S1. Comparison of the similarity between GWAS significant SNPs and meta-analysis significant SNPs (P ≤ 10−8). A meta-analysis significant variant was counted twice, one for the bull and one for the cow to account for the identity of a GWAS significant variant.Additional file 2: Figure S2. Comparison of the significant SNPs (P ≤ 10−8) between the Villar2015 H3K27ac and H3K4me3 enhancer sets.Additional file 3: Figure S3. Comparison of the functional class of significant SNPs (P ≤ 10−8) between the bovine liver H3K4me3 and H3K27ac enhancer sets.Additional file 4: Table S1. Properties of GSEA core SNPs in the Villar (H3K4me3 and H3K27ac) enhancer sets. The proportion of significant GSEA core SNPs in the H3K27ac set doubled when the P value threshold decreased from 10−8 to 10−5 whereas the proportion of GSEA core SNPs in the H3K4me3 set did not increase as much.Additional file 5: Table S2. Proportion of GSEA core SNPs in the H3K4me3 set that are also in the H3K27ac set, and proportion of the GSEA core SNPs in the H3K27ac set that are also in the H3K4me3 set.Additional file 6: Table S3. Number of significant enhancer SNPs (P ≤ 10−8) that remained on chromosome 14 after correcting for the effect of the well-known DGAT1 mutation.Additional file 7: Table S4. SNPs highlighted in previous studies [ studies , 55 thatAdditional file 8: Figure S4. Manhattan plot showing the enhancer signals around the MGST1 and GC genes.


A Benjamini-Hochberg multiple testing correction reveled significant SNPs. Corresponding genes were identified using the Bovine Genome UMD 3.1 annotation. Principal Components Analysis (PCA) confirmed differentiation. An analysis of Copy Number Variations (CNVs), obtained from signal intensities, revealed no evidence of association between ovarian pathologies and CNVs. In addition, a haplotype frequency analysis showed no association with ovarian pathologies. Results show that SNP signal intensity, which captures not only information for base-pair genotypes elucidation, but the amount of fluorescence nucleotide synthetization produced in an enzymatic reaction, is a rich source of information that, by itself or in combination with base-pair genotypes, might be used to implement differentiation, prediction and diagnostic procedures, increasing the scope of applications for Genotyping Microarrays.Hypoplasia and ovarian cysts are the most common ovarian pathologies in cattle. In this genome-wide study we analyzed the signal intensity of 648,315 Single Nucleotide Polymorphisms (SNPs) and identified 1338 genes differentiating cows with ovarian pathologies from healthy cows. The sample consisted of six cows presenting an ovarian pathology and six healthy cows. SNP signal intensities were measured with a genotyping process using the Axiom Genome-Wide BOS 1 SNPchip. Statistical tests for equality of variance and mean were applied to SNP intensities, and significance  Hypoplasia and ovarian cysts are two of the most common pathologies in cattle. Ovarian hypoplasia is a congenital malformation that is characterized by the lack of proper growth and development of the ovaries, presenting a decreased or subnormal size. The literature has reported that ovarian hypoplasia can occur in one or both ovaries. However, when it occurs in one ovary of the animal , the remaining one is also affected. Only 9% of females suffer from a bilateral hypoplasia together with an offspring’s womb and an offspring’s mammary gland . On the Single Nucleotide Polymorphism (SNP) Genotyping microarray biotechnology is currently widely used for genome-wide studies, due to its capability of simultaneously measuring the allelic content from a specific position in millions of DNA fragments ,11. By sp-values and cluster visualization allowed us to establish differences between data at the signal intensity level that was not possible at the haplotype level.In this work we present a new application of SNP signal intensities. We performed a genome-wide SNP signal intensity scanning looking for genes differentiating between cows suffering from ovarian pathologies and healthy cows. The data consisted of 648,315 informative SNP markers assayed with the Axiom Genome-Wide Array Bos 1  on 12 cows, from which six were suffering ovarian pathologies. We present a methodology based on the normalized signal intensity that is capable of distinguishing between the two sets of data . The methodology implements a genome-wide scanning, using statistical tests for equality of variance and mean, to vectors of normalized signal intensities, and a PCA clustering analysis for differentiation. Examination of The Autonomous University of Baja California  animal care and use committee deemed it unnecessary to obtain ethical clearance for this study, as all blood samples used for DNA extraction were collected under the directives on animal research of the Institute for Research in Veterinary Science UABC (IICV-UABC) on the basis of the Mexican laws on animal studies (NOM-003-ZOO-1994 and NOM-062-ZOO-1999).®, Franklin Lakes, NJ, USA) with EDTA anticoagulant. DNA extraction and purification was performed using a QIAGEN kit . All DNA samples were analyzed by spectroscopy and agarose gel electrophoresis, and stored at −20 °C for future analysis.None of the animal samples were related to each other. They came from the experimental Holstein cattle herd of the Veterinary Science Research Institute of the Autonomous University of Baja California, in Mexicali . The samples consisted of blood, obtained by venipuncture of the coccygeal vein, using vacutainer tubes . In order to find a possible cause to justify their null reproduction and low milk production, a physical examination was performed following the procedures defined by Sisson et al. . In the 2 ratio. The calculation was made using a Perl script with the following procedure: first, for each marker, a reference was developed using the equation T = A + B, where A and B are the signal intensity values of each allele. For each SNP, a reference is established using M = median . The second step is the estimation of the intensity for each individual sample using the equation log2 (T/M) T, whose mean vector M and covariance C are described by:Given a vector 1, λ2, …, λ3, and the eigenvectors P1, P2, …, dP; arrange them according to their magnitude.Calculate the eigenvalues λd eigenvectors to represent the n variables, d < n. Then, the P1, P2, …, dP are called the principal components [Select mponents . We usedmponents .We estimated the haplotype pair for each sample using PHASE 2.1 algorithm ,28. The p-values in order to control the False Discovery Rate (FDR). The approach was implemented as follows: first, all p-values were ordered from smallest to largest denoting the i-th smallest p-value by P(i), for each i between 1 and m , then, starting from the largest p-value P(m), compare P(m) with 0.05 x i/m. Continue as long as P(i) > 0.05 x i/m. Let k be the first time when P(k) is less than or equal to 0.05 x k/m, and declare the differences corresponding to the smallest k p-values as significant [A Benjamini and Hochberg correction for multiple testing was applied to nificant .F-test for equality of variance and a T-test for equality of means were applied to each of the 4532 selected genes. After applying Benjamini-Hochberg multiple testing correction, only the p-values of 1338 genes resulted significant.We inspected the complete list of autosomal genes  and selected those having at least 15 SNPs within their DNA sequence. As a result, 4532 genes were selected for this study. For each selected gene, two vectors were generated. The first vector included the normalized signal intensity from the six animals suffering the disease. The second vector included the normalized signal intensity from the healthy animals. An RBFOX1 (RNA binding protein fox-1 C. elegans homolog 1) with 970, and had the smallest p-value of 1.57 × 10−16. In addition 55 genes had the lowest number of SNPs within their sequence, with 15. For more details, see From the 1338 significant genes, 729 are the best reference sequences (RefSeq best), 414 are messenger RNA (mRNA), 162 are uncharacterized RNA sequences (maseq), and 33 code for proteins. The chromosomes with the highest number of involved genes were chromosome four, one and two, with 88, 82 and 83 genes, respectively. Chromosomes with the lowest number of involved genes were twenty nine, twenty eight and seven, with 17, 20, and 21 genes respectively. The family groups with more genes involved were zinc finger with 28 genes, solute carrier family with 17, and family with similar sequence with 15 genes. The gene with the greatest number of SNPs within its sequence was FSHR, which is a follicle stimulating-hormone, and the gene LHCGR, which is a luteinizing/choriogonadotropin receptor hormone [LOC537580 known to have no relation to ovarian disease. We obtained a p-value of 0.0002 for the FSHR gene, indicating a highly significant difference between disease and healthy samples. In contrast, an average p-value of 0.44 was obtained for a group of five SNPs to the right, and a group of five SNPs to the left of the FSHR gene location, indicating no difference between disease and healthy samples. Consistently, for the LHCGR gene, we obtained a p-value of 0.0013, while we obtained an average p-value of 0.33 for the surrounding SNP groups. Additionally, we randomly selected a DNA region without encoded genes and with no relationship to ovarian functions in cows. The region start position is at 32,108,743 and an ending position at 32,305,585 in chromosome 20. The total length is 196,842 base-pairs covering 40 SNPs. A p-value of 0.58 was obtained for this region, showing no difference in the mean signal intensity between the two groups.As a measure of accuracy, we inspected the results of the statistical analysis of two genes that are well documented as highly related to ovarian diseases: the gene  hormone ,32,33. Whttp://www.genome.jp/kegg/pathway.html) [FSHR and LHCGR genes are involved in the Calcium Signaling Pathway (bta04020), in the cAMP Signaling Pathway (bta04024) for signal transduction, in the neuroactive ligand-receptor interaction (bta04080) for signaling molecules and interaction in the environmental information processing section, and in the prolactin signaling pathway from endocrine system   for molestem see .In addition to the previous analysis, we conducted a PCA using the initial vectors generated for each animal and each gene. The results show clear differentiation between the two kinds of samples. FSHR. In the plot, bars represent average loading values of normalized signal intensity for every sample. The differentiation appears even clearer when we apply PCA individually to genes we know are strongly related to ovarian pathologies. FSHR gene from this study.In order to identify a possible association between copy number variants (CNVs) and cystic ovarian follicles (COFs), we performed a CNV detection using the signal intensities from the six animals with ovarian pathology. We used two different algorithms, PennCNV and QuantiSNP, with default parameters, and found 84 and 111 CNVs, respectively. None of them showed overlapping with the hormone genes involved in ovarian pathology processes considered in this study. In addition, we performed a review of several studies reporting CNVs, from a total of 6580 samples from different cattle breeds ,22,23,24LHCGR gene. In this example, as in the rest, we found no evidence of haplotype association.In order to discover if there were haplotypes associated to COFs, we obtained the base-pair allele genotypes for all SNPs covered by the genes in the study. We used the files AxiomGT1.Calls.TXT and Axiom_GW_Bos_SNP_1.na32.annot.CSV (provided by the Affymetrix genotyping service) which contain the final cluster of genotypes and annotation for cattle UMD3.1 annotation. For each gene, we inferred haplotype pairs using PHASE 2.1 algorithm ,28, and FSHR and LHCGR were selected for further analysis because it is known they are highly related to ovarian diseases. For differentiation between disease and healthy groups, the normalization of SNP signal intensities allowed us to implement statistical tests for equality of variance and mean, which in turn gave us the first insight of differentiation, based on a measure of data location. From the four differentiation tests , all confirmed the same results. The first two with a clear differentiation and the other two with no differentiation at all, judging by p-values from statistical test. As a complementary technique, PCA allowed us to make differentiation based on data variability. The results presented in  was to verify if the sequences of the genes from the hormones in our study were affected by the recombination process and also if an aberration generates the formation of CNVs. Our results showed that from the 6736 CNVRs previously reported in the literature, only 4.45 × 10The fact that there were no haplotype blocks, or haplotype patterns, consistent just for one group, either healthy or disease, makes clear that this differentiation analysis is not possible at the genotype, or haplotype level. This also shows the missing of important information when mapping from signal intensities to base-pair genotypes information. Therefore, complementing information at the intensity level, with information at the genotype level might increase the power of analysis and the scope of applications for SNP genotyping microarrays.This work presents the first genome-wide SNP signal intensity scanning looking for genes differentiating cows suffering ovarian pathologies from healthy cows. Statistical test for equality of mean and PCA of signal intensity provided by a SNP genotyping microarray revealed a total of 1338 differentiating genes. Differentiation achieved from signal intensity was not possible at the genotype, or haplotype level, which highlighted the missing of important information when mapping from signal intensities to base-pair genotypes information.Finally, our results show that SNP signal intensity, which captures not just information for base-pair genotypes elucidation, but the amount of fluorescence nucleotides synthetization produced in an enzymatic reaction, is a rich source of information that, by itself, or in combination with base-pair genotypes, might be used to implement differentiation, prediction, and diagnostic procedures, increasing the scope of applications for genotyping microarrays.http://www.ebi.ac.uk/arrayexpress.The complete raw data, supporting the results are available in the ArrayExpress repository, under the accession number E-MTAB-4874 in


Indigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, genetic characteristics of each breed have been poorly studied.We performed the comparative genome-wide analysis to assess evidence for subspeciation within species at the genetic level in trypanotolerant N’Dama cattle. We analysed genetic variation patterns in N’Dama from the genomes of 101 cattle breeds including 48 samples of five indigenous African cattle breeds and 53 samples of various commercial breeds. Analysis of SNP variances between cattle breeds using wMI, XP-CLR, and XP-EHH detected genes containing N’Dama-specific genetic variants and their potential associations. Functional annotation analysis revealed that these genes are associated with ossification, neurological and immune system. Particularly, the genes involved in bone formation indicate that local adaptation of N’Dama may engage in skeletal growth as well as immune systems.Our results imply that N’Dama might have acquired distinct genotypes associated with growth and regulation of regional diseases including trypanosomiasis. Moreover, this study offers significant insights into identifying genetic signatures for natural and artificial selection of diverse African cattle breeds.The online version of this article (doi:10.1186/s12864-017-3742-2) contains supplementary material, which is available to authorized users. Cattle are vital resources for African economy and society. Approximately 150 breeds of indigenous cattle have been found in sub-Saharan Africa . In partRapid development of large-scale genetic variant inventories has brought attention to the identification of the genes or loci controlling phenotypic traits . This trT. congolense, T. vivax and T. brucei groups are the main African pathogenic trypanosomes for cattle [African trypanosomiasis is a matter of great concern that can lead to serious economic losses and health crisis in Africa. Trypanosomes are infectious agents that are transmitted by tsetse fly. It can cause lethal diseases in mammals including human and livestock. In particular, r cattle . Most car cattle , 14. To r cattle , 15. Morr cattle , ticks ar cattle , and strr cattle  in Africr cattle . Moreover cattle –20.Many studies on the tolerance to cattle trypanosomiasis-susceptibility have mainly focused on comparing N’Dama and Boran breeds. However, not many studies have carried out comparative research between N’Dama and other trypano-susceptible breeds. Herein, we concentrate on the analysis of the genetic variations between N’Dama and Ogaden cattle in order to discover N’Dama-specific genetic signatures. Ogaden cattle are one of the representative breeds that play a role as a valuable economic resource including the production of beef and dairy products, but they are known to be susceptible to trypanosomiasis .In this study, a comparative genome-wide analysis of diverse cattle breeds was carried out to identify genetic distinctiveness of N’Dama breed. We investigated the genome of five indigenous African breeds and four commercial breeds using the combined methods based on information-theoretic and statistical approaches. This study identified new genetic patterns from cattle genome, and also detected selective pressures which cause an increase in genetic differentiation among populations. The proposed approaches on the analysis of the selected SNPs confirmed the differences of genomic patterns between N’Dama and other cattle breeds. Moreover, the identified associations between genes with N’Dama-specific genetic variations are related to the regulation of ossification, neurological system, and immune system development which might be involved in the evolution of N’Dama-specific phenotypes including the tolerance to African trypanosomiasis. This study reveals insights into detecting the breed-specific genetic signatures from the genome.We performed a comparative genome-wide analysis of diverse cattle breeds to discover genetic signatures of N’Dama cattle using the combined methods based on information-theoretic and statistical approaches Fig. .Fig. 1Sc6.5 billion reads or ~644 Gbp of sequences with ~11 X genome coverage in total were generated from individual genomes of five indigenous African cattle  and four commercial cattle breeds . The reads were aligned to the reference genome sequence UMD 3.1 with an average alignment rate of 98.84% that covered 98.56% of the reference genome . To overcome any bias caused by the small sample size, a lower p-value threshold was selected for estimating statistical significance  compared to those in other studies [The candidate SNPs to distinguish N’Dama and other cattle breeds were extracted using an information-theoretic method, mutual information (MI) which estimates the association strength between the SNP positions and breeds. Thus, our analysis was designed to detect the discriminative SNPs with a high dependence between the haplotypes of two adjacent loci and breeds. Approximately 2.6 hundred thousand SNPs were identified by averaging the results between N’Dama and other five breeds along with 2,793 common genes  the maximum, (ii) the mean, and (iii) the sum of MI values of all the SNPs in a gene were calculated in addition to (iv) the number of SNPs counted for each gene. Figure We performed the analysis with the weighted mutual information (wMI) in order to scan the genome for breed-specific SNPs. For a given gene, wMI is defined as the summation of two factors: the normalized number of SNPs assigned to the gene and the mean MI value of SNPs of the gene. The proposed wMI is considered as the degree of the genetic variation in the gene and as the discriminative information between the breeds. Figure Thirty genes containing the distinctive SNPs between N’Dama and Ogaden were identified by wMI analysis   in the constructed network. Enriched terms were related to cognitive functions , perceptual systems  and neurological systems    in the constructed network. The significantly enriched terms included ‘regulation of hormone secretion’ and ‘regulation of ossification’   due to genes including SP1 and SP7  in the constructed network showed significantly enriched terms, ‘immune system development’  showed homogeneity in SNP variation Fig. . Notablyet al.  reportedFinally, we looked into variations on a protein level by focusing onto non-synonymous SNPs and investigated whether such variations caused any physiological change in N’Dama cattle. N’Dama-specific missense or nonsense variants with their annotated genomic locations and coding effects for the identified genes were observed after performing the three measures . This SNP was located in RANBP17 which is a member of the importin-β super family of nuclear transport receptors. In human, RANBP17 is the loci of recurrent chromosomal 5 breakpoints detected in T-cell acute lymphoblastic leukemia, and the transcriptional activation of this gene occurs during hematopoietic process with enhancer elements of the TCR delta gene [Also, the 15 mutations out of 20 missense mutations resulted in alteration of chemical properties. Eleven mutations were located in functional domains, while the rest nine were in inter-domain region , human and mouse Fig. . InteresThe development of large-scale genetic variant inventories has triggered a number of studies on the identification of distinct genome patterns which give rise to breed-specific traits. For instance, several researches attempted to detect genetic divergences that are associated with trypanosomiasis in African cattle from genome data , 10. In p-values less than 1.0e-2), indicating greater selection pressures to these chromosomes during the evolutionary history of N’Dama.Comparison of N’Dama genome with other indigenous African cattle and commercial breeds resulted in the identification of N’Dama specific SNPs. MI analysis for the detection of breed-specific SNPs successfully distinguished genotypic profiles among Boran, N’Dama and Ogaden. In addition, the combination of either MI and XP-CLR or MI and XP-EHH allowed us to screen positively selected SNPs in N’Dama genome that are presumed to have occurred during natural and artificial selection. Genetic regions uncovered by XP-EHH and XP-CLR often represent biologically meaningful variations that may explain adaptive traits. Moreover, it is possible to produce larger lists of likely selective sweeps, and as a result, this may allow us to better understand how selection has affected the variation of a specific-breed . Some ofThe correlation network is constructed based on the similarity of genotype between genes. If a SNP variation value at gene level is close to 0, this means that the gene possesses similar genotypes to the reference. On the other hand, if the SNP variation at gene level is higher, the gene is likely to possess relatively more heterozygous or alternative homozygous genotypes. The interaction between genes in the correlation networks shows similarity in their genotypes. If calculated trends of genotypes for two genes are homo-homo or hetero-hetero, their correlation will be high (close to 1) and the edge will be red as shown in the Fig. Comparison of genomes among different cattle breeds using wMI identified statistically significant SNPs and the genes where these SNPs are located. From the analysis based on wMI approach, many genes of the constructed network and the majority of enriched GO terms indicated that N’Dama may have a distinguished sensory and neurological system related to startle response  and genomic predictions for feed efficiency and growth traits in commercial beef and dairy cattle, the positive or negative regulation of ossification and bone mineralization is commonly observed in relation to traits like average daily gain, or mid-test metabolic weight , 59.Enrichment in the term “ossification” may also indicate physiological difference between N’Dama and Ogaden. Ossification has several functions: for instance, skeletal growth, mineral storage, blood cell production, and energy storage. The genes associated with ossification were implicated in biological process such as calcium homeostasis (CALCR), phosphate homeostasis and vitamin-D metabolism (FGF23), cell cycle progression (CDK6), and the regulation of transcription (SP1 and SP7) involved in multiple functions .T. cruzi that causes human sleeping sickness [T. brucei. In addition, haematopoietic stem cells (HSCs) in bone marrow give rise to the different types of mature blood cells and immune cells. Our results imply that N’Dama may possess specific genetic factors that confer immunity to supress activities of trypanosomes more effectively. A previous genome-wide study performed with West African cattle revealed that genes involved in immune response were under strong balancing selection in trypanotolerant N’Dama breeds [According to previous studies on trypanosomiasis, responses to trypanosoma infection in cattle include immunosuppression, inflammatory response and anaemia , 19, 60.sickness . This maa breeds  which ala breeds –64.Through examining exonic SNPs that results in missense or nonsense mutation Table . AlthougIn conclusion, our results illustrate that trypanotolerant N’Dama displays clear genetic differences compared to other African cattle and commercial breeds. The adaptation of N’Dama to the environment may implicate unique bone formation related to growth traits, immuno-genetic mechanisms that allow them to tolerate regional diseases including trypanosomiasis, and neurological processes which involved in the development of favorable behaviors for survival. Our analysis provides advanced knowledge in genetic selection of N’Dama and its adaptation to the local environment.https://www.illumina.com/documents/products/datasheets/datasheet_hiseq2000.pdf). Finally, sequence data were generated using the Illumina HiSeq system. The details of data are described in [Whole-blood samples (10 ml) were collected from indigenous African cattle  and commercial cattle . The DNA was isolated from the whole blood using G-DEXTMIIb Genomic DNA Extraction Kit  and pair-end reads were generated from the isolated DNA using Illumina HiSeq 2000. The Covaris System was used to shear 3 μg of genomic DNA into the ~300 bp inserts. The fragments of the sheared DNA were end-repaired, polyA-tailed, adaptor ligated, and amplified using the TruSeq DNA Sample Prep. Kit . Pair-end sequencing was performed on the Illumina HiSeq 2000 platform using the TruSeq SBS Kit v3-HS  . The pair-end sequence reads were aligned to the UMD 3.1 using Bowtie [ftp://ftp.ensembl.org/pub/release-75/fasta/bos_taurus/) from the Ensembl database (release 75) was used as the bovine reference genome for the assembly. The size of reference genome sequence UMD 3.1 is 2.67Gb. The overall alignment rate of the reads to the reference genome was 98.84% with an average read depth of ~10.8X genome coverage. On average across the whole samples, the reads covered 98.56% of the reference UMD3.1 genome , derived from the genomes of five indigenous African cattle  and four commercial cattle breeds , via the fastQC package (g Bowtie  with thehttp://broadinstitute.github.io/picard/) and SAMtools [p-value using Fisher’s exact test) > 200. BEAGLE [We used Picard  using BovineSNP50 Genotyping BeadChip . After filtering out SNPs based on GeneCall score less than 0.7, common loci of SNP chip and DNA resequencing data were extracted and examined to assess concordance  or 2 by 3 (co-dominant coding / breed-specific group information) contingency tables, and performed Fisher exact and Cochran-Armitage trend tests for the 2 by 2 and 2 by 3 contingency tables, respectively. A total of 37,363,436 SNPs were applied in the tests, and we used Bonferroni correction for multiple correction testing. After identifying significant N’Dama breed-specific enriched SNPs, we annotated each SNP using snpEff  . SNPSifti-th SNP is transformed as follows:For effectively representing breed-specific SNP variations, all SNP alleles of the samples are converted into binary values including 0 and 1. ‘0’ denotes the major allele of a SNP position for all the samples while ‘1’ represents minor values regardless of its alleles. This biallelic representation explicitly characterizes the ratio of major and minor alleles of each SNP position per breed, thus allowing breed-specific SNPs to be effectively discovered. To be specific, the allele of the SNPi* and Major(i) are the i-th SNP allele and the most frequent allele in the i-th SNP position for all the cattle samples. The values 0 and 1 in a SNP position per each breed denote “conserved” and “mutated”, respectively.where Information-theoretic measures have emerged as a useful way to quantify the dependencies of many genetic variables . In partX = {x1,…,xn} and a breed class variable y, we define a function F that selects variables by measuring the associations between SNP positions and breed classes:In genetic association studies, MI can be used to measure the dependencies between genetic factors and phenotypes by defining genetic features and phenotypic classes as random variables. Extracting the discriminative genetic variations from tens of millions of SNPs can be addressed as finding the distinct variables from a huge-scale variable set. Given a SNP position variable set X* is the selective SNP variable pair set, xi and xj denote two SNP variables in a chromosome, and θ indicates the threshold for selecting the SNP variables. Also, MIE denotes mutual information estimator.where SNP and C, denotes a genetic variable and phenotypic class, respectively, the value set of a SNP consists of its possible alleles, and the value set of C is defined as {N’Dama, other cattle}. The MI I quantifies the reduction in the uncertainty of the phenotypic class C due to the information contained in the genetic variation of SNP:When the two random variables, H(SNP) is the entropy of SNP. H(SNP|C) denotes the conditional entropy of SNP for a given C, and it can be found using the chain rule. Thus, by the definition of the entropy H, the MI can be reformulated with the joint probability distribution p as follows:where I is nonnegative and is only zero when p = p(SNP)p(C), indicating that there is no association between SNP and C. Intuitively, then, MIEs can be used for measuring the main effect of a genetic variable SNP on the breed C.In this study, we calculated conditional mutual information  to quantify the associations among three or more variables as the MIE function and to measure the influence of two-locus haplotypes on the breeds. Conditional MI is defined as follows:I as an MIE. MIEs can be obtained by the chain rule for MI:We defined I is also nonnegative and becomes zero when no dependency exists among all three variables. This property allows the method to be suitable for identifying distinct two-locus haplotypes determining the phenotype of cattle.The MIE quantifies the associations between SNPs at two loci and breeds. i-th gene and breed variable C (wMI) is defined by interpolating the number of SNPs annotated by the gene and the mean MI of the gene:Weighted MI between the gi is the set of SNPs annotated by the i-th gene and α denotes the constant for moderating two factors. When a gene possesses more SNPs and mean MI between the gene SNPs and the breed variable is larger, the wMI of the gene provides a larger value.where https://reich.hms.harvard.edu/software) for observation of selective sweeps which involve modeling the multi-locus allele frequency differentiation between two populations [We performed cross-population composite likelihood ratio (XP-CLR) and cross-population extended haplotype homozygosity (XP-EHH) tests for detecting the selective pressures in the N’Dama and Ogaden cattle. The XP-CLR scores are computed using XP-CLR 1.0  to detect alleles with an increase in frequency to the point of fixation or near-fixation in one of the populations. It means that it detects SNPs which are under selection in one population but not in others. So, the extreme XP-EHH scores potentially represent the selection of a particular population. XP-EHH scores are also directional. A positive score means that selection is likely to have happened in population A, while a negative score indicates the selection probably occurs in population B [i, the fraction of windows with a value of the statistic greater than that in i is defined as the empirical p-value [In addition, we used the XP-EHH to identify the loci of selection based on the comparison of genome-wide SNP genotypes between populations. The XP-EHH scores are calculated using software xpehh  for each gene. Note that this mean value is defined as the variation of a gene.We calculate the Pearson correlation coefficient of all the gene pairs from the gene variations of cattle breed samples computed in (4):gi and gj denote the i-th and the j-th gene variations. σi and Cov mean the standard deviation of gi and the covariance of gi and gj.where A gene corresponds to a node and two genes with a significant correlation coefficient are connected to each other.A gene correlation network characterizes the correlation of the variations of genes for cattle breeds. The patterns of genetic variations based on the converted SNP alleles which are distinguishable from cattle breeds are used to build the gene-gene interaction networks. The networks constructed from the annotated genes and their quantitative variation degrees in a gene level are as follows:For investigating N’Dama-specific traits including trypanotolerance, the gene interaction networks are constructed from N’Dama and Ogaden breeds. Also, the positive and the negative thresholds are selected for connecting two genes. We implemented the source code for ourselves using the scipy package of Python 2.7 in order to calculate correlation coefficients between the extracted genes, and used Cytoscape 3.2.1 for network visualization.https://david-d.ncifcrf.gov/tools.jsp) [p-value adjustment than the naïve Fisher exact test [Finally, we conduct functional analysis for the genes of the constructed networks using the Database for Annotation, Visualization and Integrated Discovery (DAVID) ver. 6.7 (ols.jsp)  to statiact test . We also


All hollow spheres are realized with outer diameters of 10−60 nm, an inner cavity size of 2−30 nm and a wall thickness of 2−15 nm. The microemulsion approach allows modification of the composition of the hollow spheres, fine-tuning their diameter and encapsulation of various ingredients inside the resulting “nanocontainers”. This review summarizes the experimental conditions of synthesis and compares them to other methods of preparing hollow spheres. Moreover, the structural characterization and selected properties of the as-prepared hollow spheres are discussed. The latter is especially focused on container-functionalities with the encapsulation of inorganic salts , biomolecules/bioactive molecules  and fluorescent dyes  as representative examples.A wide variety of nanoscale hollow spheres can be obtained via a microemulsion approach. This includes oxides (e.g., ZnO, TiO In. In−2  as compared to the initial hollow spheres   .3, for instance, a series of TEM images of selected hollow spheres at first shows a slight growth in diameter 3 with evaporation of water occurs with a massive and continuous weight-loss from 200 °C up to 600 °C. If dehydration at ambient pressure starts at 200 °C, a dehydration occuring under TEM and full-vacuum conditions is not a surprise.Although the melting point of metal oxides is much higher compared to metals, oxide hollow spheres turned out to be highly sensitive to electron-beam induced damage, too. In the case of La(OH)diameter g−i 56]..3, for i2 hollow spheres are shown here  can be a useful option. Here, the acceleration of the electron beam can be reduced to about 20−30 kV as compared to 200−300 kV for standard TEM analysis. By this technique the presence of the inner cavity can be reliably proven on the one hand, and on the other hand, a statistically relevant number of particles is pictured—ranging between SEM images displaying several tens to hundreds of particles and TEM images with only a few particles visible on one image. As an example, STEM images of CuS and TiOown here . To diff“transport agents”—with hydrophilic and hydrophobic features—can be dissolved in w/o- or o/w-microemulsions prior to establishing the sphere wall. In contrast to the most common concepts . A closer look, however, clearly indicates the most intense vibration of KSCN . This holds especially true if spectra of KSCN@AlO(OH) were recorded with an increased amount of sample to guarantee for sufficient intensity [10x indicates 10 mg of KSCN@AlO(OH) per 400 mg KBr].The container-type functionality of nanoscale hollow spheres is first validated based on the encapsulation of typical inorganic salts, namely KSCN, K3)3  is shown. In brief, 100 mg K2S2O8 were encapsulated in about 120 mg of AlO(OH) hollow spheres. K2S2O8 as a strong oxidizing agent was again dissolved inside the aqueous micelle prior to establishing and closing the sphere wall (cf. 2.4). After separation and washing of the hollow spheres, the prescence of K2S2O8 was qualitatively proven upon addition of Mn(Ac)2 and few drops of concentrated H2SO4 [2+ is oxidized by [S2O8]2− to form [MnO4]−. The latter can be easily recognized by its deep reddish-violet color   .3 hollow spheres [KF@La(OH)3] (cf. 2.5) . Its intense fluorescence thereafter was recorded by fluorescence spectroscopy . In detae of R6G . Thus, Re of R6G .2O)6]3+.Controlled release of R6G from R6G@AlO(OH) was initiated by HCl-driven dissolution of the sphere wall. For this purpose, concentrated hydrochloric acid (32%) was added with a microliter injection syringe in portions of 10 µL to 4 mL of an aqueous suspension containing 8 mg of R6G@AlO(OH) hollow spheres. The course of the reaction was followed by photoluminescence upon 480 nm excitation a. Obviou−7 M); (2) nonfilled AlO(OH) hollow spheres (2 mg/mL), which were suspended in aqueous R6G solution (10−7 M). The photoluminescence intensity in each case was calculated by mathematical fitting of the peak area of the relevant emission spectra (shown for (1) only). In contrast to R6G@AlO(OH)  (1) is compared to two references b: (3) an@AlO(OH) a, the emAs a second fluorescent dye, flavinmononucleotide (FMN)—a derivative of vitamin B2—was encapsulated in AlO(OH) . Here, 5Accordingly, the broad absorption of AlO(OH) is superpositioned by the strongest vibrations of the fluorescent dye, which indicates the presence of FMN. Moreover, FMN@AlO(OH) exhibits intense emission of green light upon excitation with UV-light or a blue-emitting LED . The res2, SnO2, AlO(OH), La(OH)3] and metal sulfides . Controlled adjustment of sphere diameters  and fine-tuning of the inner cavity  is possible, too. Profound characterization of hollow spheres requires the use of different and independent analytical tools with electron microscopy in all its variations as the most important one. Besides the variety of materials that come into range, the microemulsion approach is especially beneficial when aiming at container functionalities of hollow spheres. Encasulation of selected inorganic salts , biomolecules and bioactive molecules  and fluorescent dyes  impressively prove this aspect.The microemulsion approach opens up a broad access to nanoscale hollow spheres that are composed of various materials. These include metals , as well as metal oxides [e.g., ZnO, TiONanoscale hollow spheres—no matter what the actual strategy of preparation might be—as a class of material with a special morphological structure will be of growing interest with regard to properties and application. Some of the most fascinating aspects are displayed in Altogether, many features of nanoscale hollow spheres such as diffusion and mass transport through the sphere wall, mechanical stability, magnetic and conductive properties are yet right at the beginning of fundamental exploration and research. Albeit—A wide variety of materials with precise control of size and composition is required to go for the above properties and applications. The microemulsion approach as presented here will surely be a route to do so.


BovineHD1400007259 (p < 10−8) within the well-known gene region PLAG1-CHCHD7 on BTA14, was found to govern variation of the knuckle, biceps and shank traits. The QTN accounted for 8.6% of phenotypic variance for biceps. In addition, 16 more SNPs distributed on BTA14 were detected as being associated with the carcass traits.Carcass traits of beef cattle have been genetically improved to increase yield of high quality meat. Genome-wide association study (GWAS) is a powerful method to identify genetic variants associated with carcass traits. For the 770K genotyped SNPs from 1141 Chinese Simmental cattle, we used the compressed mixed linear model (CMLM) to perform a genome-wide association study for knuckle, biceps and shank of beef carcass traits. Seventeen significantly associated SNPs were found, which are located on BTA6, BTA14 and BTA15. Interestingly, one pleiotropic quantitative trait nucleotide (QTN), named  NCAPG Ile442Met and the others were identified within gene PLAG1-CHCHD7 in the region on BTA14 [PLAG1 with carcass weight was found in Hanwoo steers [NCAPG-LCORL was found to be associated with carcass traits in multi-breed beef cattle [PLAG1 and NCAPG were found to segregate in Japanese Black steers [NCAPG also plays a role in decreasing the subcutaneous fat thickness and increasing carcass yield [HS3ST1, DVL1, PRKCQ and HIVEP3, were significantly associated with eye muscle area, backfat thickness and marbling score [NCAPG and LCORL on BTA6 were found to control hot carcass weight, ribeye area and lower adjusted fat thickness [GNAQ gene [Genome-wide association studies (GWAS) have enabled us to detect many genetic variants associated with quantitative traits in human  and farmon BTA14 . The asso steers . Gene NCf cattle . Both PLk steers . Meanwhiss yield . Previoung score . Also, shickness . SeveralNAQ gene . In addiNAQ gene .Statistical power of SNP detection in GWAS can be improved by increasing sample sizes, increasing marker density and choosing advanced statistical methods. In recent years, Bovine 10K, 35K and 77K, Bovine 50K chip had been widely applied for GWAS in cattle , 21–27. Carcass can be dissected into various body compositions, following the standard of slaughter practice. Body compositions consist of tissue pieces and organs. The separated pieces of meat or tissue not only differ in body part and edible quality, but their qualities are also determined by body morphological structure and construction. The knuckle, biceps and shank are important body compositions of carcass. In this study, we carried out GWAS on separated meat tissue pieces using BovineHD 770k in 1242 Simmental cattle. The objective of this study was to search for QTNs controlling knuckle, shank and biceps in Simmental cattle using the GAPIT software.All procedures strictly followed the guidelines developed by the China Council on Animal Care, and all protocols were approved by the Science Research Department of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS) . Samples were collected during regular quarantine inspections on the farms. All farm owners agreed the use of the animals and provided fields for this study.The experimental population consisted of 1242 young Simmental cattle born in 2009–2014. This population was derived from the Simmental cattle resource population established in Ulgai, Xilingol league, Inner Mongolia of China . After weaning, cattle were moved to Beijing Jinweifuren fattening farm for feedlot finishing under the same feeding and management system. Cattles were well rested for 24 hours before slaughtering to relieve from fear and then made bloodletting after electrical stunning. All measurements of traits were in strict compliance with the Institutional Meat Purchase Specifications for fresh beef guidelines and GB/T 27643–2011. Experimental individuals were slaughtered at 16–18 months of age and at roughly the same time, after which beef carcasses were separated and carcass traits were measured.For the three carcass traits of interest, knuckle is the quadriceps femoris mucle of beef cattle. Biceps is a piece of bovine fore leg muscle, which is composed of two short-fibred heads separated longitudinally by a thick internal tendon which stretches from the origin on the supraglenoid tubercle to the insertion on the medial radial tuberosity. Shank is meat part along the tibia to the femur in the back of hind leg. During slaughtering, the three traits were dissected following their definitions.Collection of blood samples were operated along with regular quarantine inspection on the farms. Genomic DNA was extracted from blood samples using the TIANamp Blood DNA Kit , DNAs with an A260/280 ratio ranging between 1.8 and 2.0 were subjected to further analysis. The IlluminaBovineHDBeadChip with 774,660 SNPs were chosen for individual genotyping. The SNPs were uniformly distributed on the whole bovine genome with a mean inter-marker space of 3.43 kb. SNP chips were scanned and analyzed using the GenomeStudio software package.−7 were removed from the data. Part of the data are uploaded to Dryad, Digital Repository: doi: 10.5061/dryad.4qc06.For the quality control, individual cattle and their SNP genotypes were handled by the PLINK software package (v1.9) : First, y is a vector of phenotypic values of the trait under consideration, μ is the population mean, ew is the live weight of calf entering fattening farm and b1 is effect of live weight, fd is the number of fattening days, b2 is effect of fattening days, sy is slaughtering year, b3 is the slaughtering year effect, cs is calving seasons including the three levels , b4 is the effect of cs, and e is a vector of the residuals. The estimated residuals, denoted by y*, were eventually used as the adjusted phenotypic values in the subsequent association study.Prior to GWAS, phenotypes were corrected for some systemic environmental factors using the following linear model,ith QTNs for i = 1, …, N, where N is the number of markers in the genome. The “Q + K” model isy* is the vector of phenotypic values adjusted by non-genetic effects as described early, bi is additive genetic effect of the ith SNP, X is a vector of genotype indicator variables whose value is defined as 0, 1, or 2 for the three possible genotypes, G11, G12 and G22, respectively, v is vector of population structure effects, Q is the principal component matrix calculated from part of the SNPs, u is a vector of polygenetic effects assumed to be Z is a design matrix for the random effects (identity matrix in this case), and e is the vector of residual errors following a To take into account the effects of population stratification and unequal relatedness among individuals, the “Q+K” model of Yu et al. (2011) was used to detect the Essentially, the “Q+K” model is a mixed linear model (MLM), where population stratification and SNP effects are treated as fixed effects and the polygenic effects are considered random. GWAS with this model in its original form is computationally very expensive because the mixed model needs to be solved for each SNP tested. By compressing the genomic relationship matrix, MLM has been transformed into a “sire model” that contains only a few effects of individual groups and such a sire model can greatly improve the computational efficiency. The GAPIT software package  implemenith SNP, where N, where N is the total number of SNPs. The positive false discovery rate (FDR) across association tests between a SNP and the trait of interest was estimated using the Benjamini-Hochberg method [http://www.animalgenome.org).After SNP effects were estimated and their standard deviations calculated by GAPIT, a student-t test statistic was calculated usingg method . Finally−7, respectively. As a result, only 588,150 SNPs on 1141 individuals were used for the GWAS analysis.Means (standard deviations), coefficients of variations, heritabilities and pairwise phenotypic correlations between traits  are presented in Based on the kinship matrix calculated from 50,000 randomly sampled SNPs, the principal component analysis (PCA) showed that the first two principal components (PC1 and PC2) contributed more than 85% of the marker variation. The population was clustered into four groups, as displayed in p-value was p = 0.05/588150 = 8.50×10−8 and–log10(p) = 7.07 after Bonferroni correction. p) against genome locations of the SNP markers. Significant QTNs were claimed at the peaks that passed over the critical value in the Manhattan plot. Based on this criterion, a total of 9 SNPs were significantly associated with the three traits, of which one for knuckle, six for biceps and two for shank. The GWAS results are listed in p-values were close to the threshold p-value were also considered as candidate QTNs. The FDRs of these QTNs were all < 0.05, except for four QTNs occupying the following four positions: BovineHD0600009630, BTB-00622352, BovineHD1400007161 and BovineHD1400007161.With 588,150 markers, the threshold for the Hapmap26308-BTC-057761 and BovineHD0600009630 on BTA6, at BovineHD1400007259 on BTA14 and at BTB-00622352 on BTA15. Only BovineHD1400007259 was identified as a QTN that is 25 Mb away from the initial marker on BTA14, because the p-value exceeded the corrected threshold. The most significant QTN resides within gene PLAG1. The two SNPs on BTA6 are candidate QTNs belonging to genes LAP3 and CCSER1, respectively. BTB-00622352 is located in the region where genes LOC100848982 and LOC514367 overlapp. The largest QTN at BovineHD1400007259 only explained 2.9% phenotypic variation.A total of 4 QTNs were mapped at BovineHD0600010952, BovineHD0600010950, BovineHD0600010951, BovineHD0600010953 and BovineHD0600010956. The last one overlaps with a peak on BTA6. Notably, we found BovineHD1400007259 and BTA-75902-no-rs overlapped with genes PLAG1 and KCNIP4. The phenotypic variance explained by the 10 detected SNPs ranged from 0.009 to 0.086.A total of 10 SNPs were associated with biceps, representing the most SNPs associated with a single trait. Five SNPs on BTA6 and one SNP on BTA14 were identified as the QTNs, while four SNPs as QTN candidates on BTA6. As displayed in the middle panel of BovineHD1400007259 resides within gene PLAG1. Two other QTNs are 24 Mb and 26 Mb away from the initial marker on BTA 14 and very close to gene PLAG1.A QTN mapped at BovineHD1400007259 was found to be simultaneously associated with knuckle, biceps and shank, showing a typical pleiotropic effect of of the QTN in gene PLAG1. All detected QTNs contributed relatively low proportions of the phenotypic variation, but QTN BovineHD1400007259 explained over 8.61% of the phenotypic variance for biceps.In summary, a QTN at In this study, we performed a GWAS for body compositions including knuckle, biceps and shank using high density Bovine HD BeadChip in Chinese Simmental cattle. Previous studies using PLINK  have locPLAG1 locus. Actually, several QTNs have been found to be located in the well-known PLAG1-CHCHD7 region [PLAG1 is related to the pleiomorphic adenoma gene [PLAG1 knock-out mice are 30% smaller than their wild-type litter mates at birth [PLAG1-CHCHD7 intergenic region. Carcass weights have been proven to be associated with gene PLAG1 by GWAS in Japanese Black cattle [PLAG1, some QTNs were found to be responsible for changes in knuckle, biceps and shank in Chinese Simmental cattle, which carry gene LAP3, CCSER1 and KCNIP4. As an aminopeptidase that catalyzes the exclusion of amino acids from intracellular proteins and peptides, LAP3 (leucine aminopeptidase 3) is highly articulated in the bovine pineal gland, kidney, skin, intestine, mammary, and adipose tissues . LAP3 has been confirmed to be also associated with milk production and body size in Charolais [LCORL and NCAPG have been detected, but two QTN candidates at Hapmap30134-BTC-034283 and BovineHD4100004565 for biceps are in high LD with some SNPs within gene NCAPG.Importantly, the most significant SNP for knuckle, biceps and shank weights is located within the 7 region , 16, 17 oma gene –34 and roma gene . Additioat birth . Also inat birth  reportedharolais –39. For S1 FileThe GWAS result of the SNPs that are nominally significant (P<0.05) for biceps, including: SNP names, chromosome, position, p-value and FDR.(CSV)Click here for additional data file.S2 FileThe GWAS result of the SNPs that are nominally significant (P<0.05) for biceps, including: SNP names, chromosome, position, p-value and FDR.(CSV)Click here for additional data file.S3 FileThe GWAS result of the SNPs that are nominally significant (P<0.05) for biceps, including: SNP names, chromosome, position, p-value and FDR.(CSV)Click here for additional data file.