The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures , besides the BWT.In , a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order github.com/nicolaprezza/ebwt2InDel.In this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of  to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at Results on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs. These percentages considerably improve the 71% of SNPs and 51% of INDELs found by the state-of-the art tool based on de Bruijn graphs. We furthermore report results on larger  Human whole-genome sequencing experiments. Also in these cases, our tool exhibits a much higher sensitivity than the state-of-the art tool. Variant Calling is meant the process of identifying variants associated with an individual, or trait, or population, from genomic data. The most typical workflow for variant calling downstream of a genome(s) or exome(s) sequencing, is to map the obtained reads onto a reference genome by means of some alignment tool, and then highlight loci where the reads differ from the reference. Such mapping, however, is time consuming, error prone, and it can even be unfeasible when a reference genome is not available . In this scenario, there has been a growing interest in reference-free (and assembly-free) variant calling methods, that perform their task directly on the raw reads data T is a suduced in –47. In ,s[2],…,s[|s|], where |s| is the length of s. A substring of s is denoted as s=s[i]⋯s[j], with s[..j]=s being called a prefix and s[i..]=s a suffix of s.Consider a string s of length n terminated by the end-marker $. If we imagine placing the suffixes of s in lexicographic order s [s) of s such that bwt(s)[j]=s[ij−1] if ij>1, and bwt(s)[j]=$ otherwise. Each symbol in the BWT is therefore associated with a suffix of the string. Note that, from this definition, the symbols in s preceding the same right context w∈Σ∗ (i.e. w is a common prefix of the suffixes starting after those symbols) are consecutive in the BWT. Perhaps, the two most important of its many interesting properties are that (i) the BWT is reversible, in the sense that s can be reconstructed from bwt(s) with no additional information  as a shorthand for The T1=CGT and T2=C means that the INDEL The problem we aim at solving in this paper is that of finding variants within a read set without mapping the reads onto the reference genome , and thus without actually needing a reference (reference-free). More formally, given as input the eBWT of a read set w (a suitable w will be defined later) will result in a contiguous substring of cluster. A similar concept has been used in  such that the suffixes in the lexicographic range  are prefixed by a string (context) w that appears only once in the genome. In particular, w is chosen to be the shortest such prefix. When such a cluster can be defined , this definition guarantees that characters in ebwt are the sequenced copies of a single genome position. At this point, one question arises naturally: given that the underlying genome is unknown, how can we compute the contexts w (and thus the clusters)? The following theorem, proven in  that do not contain local LCP minima, i.e. no index 1<i≤r≤j<n satisfies lcp[r−1]≥lcp[r]<lcp[r+1]. In order to filter out clusters corresponding to short random contexts w , also a minimum LCP is required: for some fixed kmin , the suffixes in the cluster must share at least k characters (i.e. lcp[r]≥kmin for i≤r≤j). Note that this characterization does not need the underlying reference genome to be known, and thus allows us to find clusters with a simple linear scan of the LCP array. Importantly, and differently from tools using a context of fixed length (e.g. those based on de Bruijn graphs), the length |w| of our contexts is variable-order , and can be up to the full read length for high-enough coverages and small-enough error rates.Based on the above observation, in this paper we describe a reference-free and alignment-free tool able to find SNPs and INDELs in an input read set by analyzing just its eBWT. More precisely, the key observation at the core of our tool is that, since variants share the same right-context, they are clustered when we suffix-sort all the reads’ suffixes. As a consequence, the string k-mer and adjacency of k-mers inside the input sequenced fragments, and (ii) are not able to disambiguate repetitive regions longer than k . One important drawback of our tool, however, was the need to compute the generalized Suffix Array of the collection (required to efficiently extract contexts from the eBWT), as well as its eBWT and LCP array. This resulted in huge memory demandings: letting n be the total number of bases in the collection, the input alone took 7n bytes of space on disk. In this paper, we employ recent algorithmic findings described in  can be computed from x, the algorithm induces the LCP values whose coordinates stand between the eBWT ranges of the children of x. At the end of the visit, all LCP values have been filled. Describing this algorithm is out of the scope of this article; the interested reader can find all the algorithmic details in  and localMin[1..n] defined as Kmin[i]=1 if and only if lcp[i]≥kmin and localMin[i]=1 if and only if lcp[i−1]≥lcp[i]<lcp[i+1]. Similarly, for a fixed parameter kright  we compute an additional bitvector Kright[1..n] defined as Kright[i]=1 if and only if lcp[i]≥kright. This bitvector will be used to know from which suffix to extract the kright characters that will form the right-context of our output events . It is easy to modify the algorithm of  such that Kmin[r]=1 and localMin[r]=0 for all i≤r≤j. For each eBWT cluster ebwt , then we discard it. Otherwise, we proceed with the next step. (ii) For each pair  of distinct letters occurring at least mincov times in the cluster we do the following:Lt, for values t=1,2, of length at most M=kleft+maxindel – where by default kleft=31 (this is a parameter that can be modified using option -L) and maxindel=10 (this is a parameter that can be modified using option -g) – among suffixes of strings preceding and including character ct in the eBWT . The consensus – which is defined to be the most frequent string preceding the variant – is computed using backward search: let ebwt be the range obtained by left-extending ebwt with letter ct. Starting from ebwt, for M−1 times we extend the current eBWT range ebwt . In the example reported in Fig. c1=T,c2=C and, by choosing kleft=2 and maxindel=2, we obtain L1=AT and L2=ATGC.We compute a consensus R of kright characters from the r-th smallest suffix for an arbitrary index i≤r≤j such that Kright[r]=1, where by default kright=30 (this is a parameter that can be modified using option -R). In Fig. kright=2; string R is extracted from array RIGHT  and corresponds to GC.Using function FL, we extract a prefix L1 and L2 that minimizes the variant of edit distance where at most one insertion or deletion is allowed at the right-end of the two strings (and nowhere else). Since we know where the INDEL ends, the optimal alignment can be found in linear time. If the optimal alignment does not include INDELs, then the event is a SNP. If the optimal alignment introduces too many edits , then the event is discarded. This is an additional filter that allows discarding left-borders of long repetitive regions, which differ in the contexts preceding the suffixes in the cluster and are therefore captured by the above strategy. In the example of Fig. L2=ATGC.We find the alignment between L1·R and L2·R, as well as information useful to localize the SNP/INDEL (i.e. length and position in the two fragments), and the coverage of the two sequences in the input dataset.We output the sequences The eBWT and arrays Note that, by definition of our procedure, INDELs necessarily end with two distinct characters. For example, in Fig. mentclass2pt{minimIf a reference genome is available, then the variants called by our tool can be aligned against it and converted to .vcf format. This can be useful, for example, to compare our output against a database of known variants and, in particular, to validate the output of the tool (see next section). In the next paragraph we describe a pipeline based on the aligner bwa-memFirst of all, variants can be filtered by minimum coverage using our executable filter_snp. In general, a higher minimum coverage will increase precision and decrease sensitivity see “” sectionhttps://github.com/Illumina/hap.py) to validate the variations output by the tools. This tool is among the gold standards solutions to compute sensitivityprecisionF1 scoreTP,FP,FN being true positives, false positives, and false negatives, respectively) of a given .vcf against a ground truth .vcf.We used hap.py . At the end, the union of the t outputs  will essentially be the same as the one produced by the sequential pipeline. We note that there could be some small differences between the output of the two  pipelines due to the fact that, on positions bordering the split points, the coverage is distributed between two fasta’s adjacent pieces. However, in practical applications t will be small (i.e. corresponding to the number of processors or servers), therefore we simply ignore this phenomenon in our heuristic. The most important source of noise in this strategy, instead, is that the reads’ order generated by the sorting tool  is only an approximation of their relative mapping position on the genome. In particular, we note that HARC measures similarity under the Hamming distance, that is, it does not take into account the existence of INDELs among reads. As we show in the next section, this has a strong impact on the sensitivity of INDEL detection of our parallelized tool. Our software repository includes a script, pebwt2InDel.sh, that automatically runs the parallel pipeline .We now describe a simple strategy that allows to effectively parallelize our tool, at the price of limiting the analysis to SNPs only. The idea is to sort the input reads by similarity using the tool HARCEBWT2INDEL with DISCOSNP++  and selected the value yielding the best average between the F1 scores of INDELs and SNPs. On synthetic data and coverages 10x, 20x, 30x, 40x, 50x, we obtained the best results for EBWT2INDEL with thresholds 3, 3, 4, 4, 6 and for DISCOSNP++ with thresholds 2, 2, 3, 3, 4, respectively. Similar settings were used on real data.Both tools allow the user to filter out variants covered less than a fixed threshold. In our case, this is done at post-processing time (using the tool filter_snp) by simply discarding low-covered variants from the output of All our experiments have been run on a 24-core machine with Intel(R) Xeon(R) CPU E5-2620 v3 at 2.40 GHz, and with 128 GB of shared memory. The system is Ubuntu 14.04.2 LTS.In the synthetic experiment (i), we generated two variants of human Chromosome 1 by applying to it the two alleles of each heterozygous variation contained in the .vcf file downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. The modified bi-allelic chromosome was used to simulate synthetic reads with the tool SimSeq , uniform1000 Genomes Project consortium starting from the raw read dataset that we also used for our experiment. It is therefore expected that the differences between this and the .vcfs created by EBWT2INDEL and DISCOSNP++ are larger with respect to those in the synthetic dataset, with consequent drops in precision for both tools. In Table EBWT2INDEL for two reasons: first, our tool assumes that the input is represented as a BWT, which is a lossless representation of the input fasta and can therefore replace it; optimizing the BWT-constructions step is a well-studied problem that does not fit the scope of this article. Second, the BWT needs to be built only once for each input dataset. If one wishes to run further analyses on the same dataset, possibly using different parameters, the same BWT can be used. This is not the case for DISCOSNP, whose de Bruijn graph is a lossy representation of the data and depends on the input parameters (k-mer length and minimum coverage). In Table In the real data experiment (ii) we ran the two tools on a reads dataset  downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/. We randomly sampled reads from the original dataset keeping a coverage ranging from 10x to the 48x full-dataset coverage. For the ground truth, we used again the .vcf file used in the synthetic dataset. Unlike in the first experiment, we note that in this case such .vcf was only an approximation of the real ground truth  since it had been created by the Comparison with our old tool ebwt2snpWe also tested the old version of our tool  on the 2Enabling ParallelismEBWT2INDEL and DISCOSNP++ on the 30x-covered real dataset allowing the two tools to use 24 cores, in order to establish the effectiveness of the parallelization strategies of both tools. The parallel pipeline using HARC followed by BCR and EBWT2INDEL  terminated in 33 minutes. This is approximately 10 times faster than the complete sequential pipeline .We run github.com/nicolaprezza/ebwt2InDeland www.internationalgenome.org/data-portal/sample/NA12878 . Note that, first, we have filtered the reads by removing those containing the symbol N. In total, the dataset’s size amounted to 65.3 Gbp, that is, a coverage of 10x per individual. We used the same corresponding parameters for the two tools: -m/-c = 3 controls the minimum read/k-mer coverage, -g/-D = 10 controls the maximum INDEL length, and -v/-P = 2 controls the maximum number of differences allowed (in the bubble/left-context of clusters) in addition to the main SNP/INDEL. In order to count the number of variations output by the tools, we converted their outputs  to .vcf files by aligning them against the hg38 Human assembly (using the pipeline described in “EBWT2INDEL terminated its execution in 33 hours (the whole pipeline took therefore 56 hours). The process returned 4,265,718 SNPs and 270,488 INDELs. Our parallel pipeline, on the other hand, processed the input in just 8 hours and reported 2,693,867 SNPs and 65,582 INDELs. DISCOSNP++ completed the analysis in about 2 hours and 45 minutes when running in parallel mode with 1947% of CPU utilization, and in 10 hours when using one core only. The pipeline based on DISCOSNP++ returned 787,256 SNPs and 104,090 INDELs.In the first whole genome experiment we reported variations contained in the union of the first 320 million 100-bp reads from both datasets ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/NA12878_HG001/latest/, and validated the calls using hap.py. As in the previous experiment, we used the same corresponding parameters for the two tools: -m/-c = 3, -g/-D = 10, and -v/-P = 2. In order to build a .vcf from the tools’ outputs, we aligned the called variations against the hg38 Human assembly, and removed duplicate entries from the resulting .vcf.We performed one more experiment whose goal was to find heterozygous sites from a 20x-covered dataset of Human individual NA12878. We selected 1.300.000.000 reads from the file ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR622/SRR622457/. The whole dataset, together with the reverse-complemented reads, totalled approximately 122 GBp. For the ground truth, we used the NCBI .vcfEBWT2INDEL terminated its execution in 24 hours and 34 minutes (the whole pipeline took therefore 46 hours). The whole pipeline found 55.56% of the INDELs and 85.53% of the SNPs present in the ground truth, with a precision of 47.07% and 35.16%, respectively. DISCOSNP++ completed the analysis in 32 hours and 44 minutes and found 41.68% of the INDELs and 72.18% of the SNPs, with a precision of 51.33% and 63.97%, respectively.BCR required 21 hours and 24 minutes to build the eBWT, while EBWT2INDEL finds considerably more SNPs and INDELs than DISCOSNP++, at the price of being slightly less precise. At 10x coverage, most variations are not found by both tools. However, already at 20x the sensitivity of the tools starts to stabilize, reaching stable values at 30x. The plots suggest that increasing the coverage above 30x does not bring significant advantages. At 30x, EBWT2INDEL is able to find nearly all SNPs (93%), and a large fraction of the INDELs (83%). DISCOSNP++, on the other hand, finds only 70% of the SNPs and 50% of the INDELs. The high sensitivity of our tool is moderately paid in terms of precision: at 30x coverage, 77% of the SNPs and 89% of the INDELs output by our tool are correct, versus 90% and 94% of DISCOSNP++. By mixing precision and sensitivity in the F1 metric, we obtain higher scores than DISCOSNP++ across all coverages.Experiments on synthetic data allowed us to assess how precisely the two tools were able to reconstruct the original .vcf file. The outcome, reported in Figs. DISCOSNP++, especially on INDELs: we are able to find 79.1% of the SNPs contained in the ground truth, as opposed to 64.1% of DISCOSNP++, and 54.7 of the INDELs, as opposed to 42.6% of DISCOSNP++. As expected, both tools exhibit a lower precision with respect to the synthetic experiment, due to the fact that in this case the ground truth was just an approximation of the underlying genotype. By enabling parallelism, EBWT2INDEL’s SNP sensitivity decreases by 7.4%, but precision increases by 8.9%, resulting in an increase of the F1 score by 2%. This is explainable by the fact that, by pre-processing the data with HARC, we separate in different chunks reads that share long sub-sequences (and thus create eBWT clusters) but align on different parts of the genome. As expected, our parallel pipeline finds very few INDELs compared to the sequential one. In a future development of our tool we plan to implement a read-sorting algorithm able to work under edit distance in order to improve this result.Also on real data our tool exhibits a much higher sensitivity than EBWT2INDEL  contained >5 times more SNPs and >2 times more INDELs than those output by DISCOSNP++. In parallel mode, EBWT2INDEL found less SNPs, though still >3 times more than DISCOSNP++. This depends on the sorting strategy of HARC, which in this case separated reads that, in the sequential pipeline, contained areas that clustered together and also yielded SNPs.The large processing times of our pipeline are paid off by a much larger number of reported variations: using comparable parameters for both tools, the fragments output by DISCOSNP++, since this tool does not “color” the paths of bubbles in the de Bruijn graph according to their provenience from the two input datasets . On the other hand, this analysis can easily be performed with EBWT2INDEL by just comparing the BWTs of the two datasets, or taking as input one single BWT (as we did in our experiment) and a bitvector  that tells apart eBWT characters of the two individuals.We note that, while the goal of this experiment was just to compare the raw amount of variations found by the two tools in the merged dataset, in a more realistic application one might be interested in discarding heterozygous sites within the same individual and keep only differences across the two individuals. This kind of analysis cannot be performed using EBWT2INDEL has been about 50% slower than DISCOSNP++ but found many more variations. As observed in the above real experiments, the precision of both tools is low due to the fact that the ground truth itself is an approximation : this is the reason why the ratio between ours and DISCOSNP++ running times increase with the coverage. If we take into account also BWT construction, this step accounted for 60% of the total processing time in experiments (i) and (ii), and 42% in experiment (iii). By enabling parallelism, our whole pipeline  runs in times comparable to those of DISCOSNP++, while also being more sensitive.Despite the complex data structures used, the running times of n bits per base; on the largest dataset (50x synthetic), this was equivalent to 21GB. On the  30x real dataset, our tool used 12.7 GB of RAM. The RAM usage of our tool was dominated by the variant calling phase; building the BWT required at most 3GB of RAM on the largest dataset.As far as the RAM usage of our tool is concerned, we observed that (as expected) it always amounted to 7EBWT2INDEL, a new algorithm that detects SNPs and INDELs. We also described a simple strategy for effectively parallelizing our tool for SNP detection only.In this work, we described EBWT2INDEL on both synthetic and real data. In particular, we simulated synthetic read collections with a range of values for the coverage, to investigate the effect of such parameter on both accuracy and computational performances. In this case the ground truth is known with certainty, and thus the experiment has also the goal to assess the precision of the tools. The experiment on real data aims at assessing the sensitivity and speed of the tools in a real-case scenario. On synthetic 100 bp reads simulated from the Human chromosome 1 at 30x coverage, our tool is able to find 83.5% of the SNPs with an accuracy of 77.8%, and 72.2% of the INDELS with an accuracy of 84.4%. This considerably improves the sensitivity of the state-of-the-art tool based on de Bruijn graphs, who finds 70.6% of the SNPs with an accuracy of 90% and 51.4% of the INDELs with an accuracy of 89%. Similar performance are observed on real data (with lower precision for both tools due to the use of an approximated ground truth). As far as the computational cost of our tool is concerned (excluding eBWT computation), when using only one core we report a slow-down of a factor of 1.4 with respect to the state-of-the-art tool. When including eBWT computation, our whole pipeline is 4 times slower than the state-of-the-art (run on one core only). We then describe a simple way to parallelize our strategy for SNPs detection only. When enabling multi-threading on 24 cores for both tools, our complete pipeline runs in times comparable to those of the state-of-the-art competitor while at the same time also exhibiting a higher SNP precision. As a limitation, our parallel pipeline at the moment works for SNPs only, due to the use of an external pre-processing tool that does not take into account the presence of INDELs in the read set. We plan to overcome this limitation in a forthcoming update of our tool. We furthermore report results on a larger  whole-genome sequencing experiment whose input consisted of two 10x-covered Human datasets from the 1000 Genomes Project. Also in this case, our tool exhibits a much higher sensitivity than the state-of-the art tool, finding >5 times more SNPs and >2 times more INDELs.We validated


The early death and illness of newborn calves result in enormous economic losses in the dairy industry. As the immune system has not been fully developed in neonates, the adequate intake of nutrients and immune substances in colostrum is essential for protecting neonates from infections in their early life. The term albumin refers to a group of multifunctional proteins that are important in anti-inflammatory and anti-oxidative reactions and can help calves against various infections. Albumin of bovine whey is derived from the serum; hence, the concentration of albumin in colostrum and serum could be important traits for the breeding of potential natural disease resistance in dairy cattle. Herein, genome-wide association studies (GWASs) were performed to identify the candidate genes associated with albumin concentrations in colostrum and serum to provide useful molecular information for the genetic improvement of disease resistance traits in dairy cattle.RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips . After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs  at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are  Improved animal health and resistance to pathogens is an increasingly important breeding objective in the dairy industry ,2. As thThe concentration of albumin is a typical quantitative trait . PreviouA genome-wide association study (GWAS) is a practical approach for the high-resolution mapping of loci controlling quantitative traits and has been widely applied in domestic animals . In dairBlood, colostrum and hair follicle samples were collected in the first milking within 24 h after calving from 572 Chinese Holstein cows . All of the cows were from 10 dairy farms of the Beijing Dairy Cattle Center and the Beijing Sunlon Livestock Development Company Limited . Cows were in parity of 1 to 4 and were the offspring of 44 sires. Cows were 23–72 months old at the time of calving. The whole procedure for collecting the blood, colostrum and hair follicle samples was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (permit number: DK996).10-transformed to follow a normal distribution.The concentration of albumin of every colostrum and serum sample was measured by commercial ELISA kits . Concentrations were log® DNA Mini Kit . Then the extracted DNA was genotyped by a GeneSeek GGP_HDv3 chip . The extraction of genomic DNA from the hair follicles of the 572 cows was carried out using a QIAampp-values < 10−6 were discarded [Quality control was conducted on PLINK 1.90 software and the filtering processes were as follows: Firstly, samples with genotyping <95% of the SNPs were deleted; then, SNPs with call rates <90%, minor allele frequencies (MAF) <0.1 and Hardy–Weinberg equilibrium (HWE) iscarded ,43. Thusy is the vector of the phenotype of the log-transformed corrected concentration of albumin for 563 cows; μ is the vector of the overall mean; b refers to fixed effects, including herd, parity, time from calving and season of calving; X is the incidence matrix of b to y; f is the vector of the additive effect of the candidate SNP to be tested for association; M is the vector of the genotypes for the SNP, coded as 0 = BB, 1 = AB and 2 = AA; g is the vector of the polygenic effect with g ~N , where G is the genomic relationship matrix between pairs of individuals from all of the SNPs’ 30 chromosomes, including the X chromosome [g2 is the additive variance; and e is the vector of the residual effects with e~N , where σe2 is the residual error variance.The association analysis for each SNP was implemented independently for the albumin concentration of colostrum and serum with the following mixed linear model:romosome ; σg2 is p-value threshold [p < 0.01 in the GWAS results for the concentration of colostrum or serum albumin, and l is total number of SNPs analyzed. Then, we calculated the genomic inflation factor λ value to evaluate the extent of the population stratification by GenABEL packages [http://www.R-project.org/) [The GWASs were implemented with GCTA v1.90.2 software, which estimates the variance explained by all of the SNPs on a chromosome or on the whole genome for a complex trait . As Bonfhreshold ,47. The packages  in R 3.6ct.org/) . Quantilct.org/) .Bos taurus UMD3.1 database (http://www.ensembl.org/index.html). The extracted genes were then submitted into DAVID Bioinformatics Resources (https://david.ncifcrf.gov) for the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to identify the biological processes that these genes are involved in. Simultaneously, we compared the physical position of these genes that contained or were adjacent to significant SNPs with the known QTLs for inflammation and immune-related traits in the Cattle QTL database .To further identify the positional candidate genes for albumin, the genes that contained or were close to (±1 Mbp) the significant SNPs were selected based on the Ensembl 10-transformed to accomplish normality  was 0.983 and 1.004 for the concentration of colostrum and serum albumin, respectively, and the QQ plots (Bos taurus autosome (BTA) 1: 149,246,858 bp (BovineHD0100043239); BTA 3: 118,061,120 bp ; BTA 17: 68,421,115 bp ; and BTA 20: 59,137,600—69,916,426 bp  (According to the QQ plots  showed t-114933) .For the albumin concentration in serum, seven genome-wide significant SNPs were detected (FDR at 1%) on BTA 4: 10,737,673 bp (Hapmap39425-BTA-70290); BTA 7: 93,597,405 bp (BovineHD0700027327); BTA 7: 111,481,071 bp (BovineHD0700032536); BTA 14: 1,463,676 bp (Hapmap30381-BTC-005750); BTA 16: 30,440,171 bp (BovineHD1600008636); BTA 20: 39,761,822 bp (ARS-BFGL-NGS-73590); and BTA 28: 45,702,356 bp (BovineHD2800013250) . The ManRUNX1), carbonyl reductase 1 (CBR1) and OTU deubiquitinase with linear linkage specificity (OTULIN) were observed to be involved in the positive regulation of interleukin-2 production, oxidation-reduction process, negative regulation of nuclear factor (NF)-kappaB transcription factor activity and negative regulation of inflammatory response (RUNX1 (BTA 1: ~148.73 Mbp), CBR1 (BTA 1: ~150.06 Mbp) and OTULIN (BTA 20: ~58.58 Mbp) were eventually selected as the candidate genes for the colostrum albumin concentration , SHANK-associated RH domain interactor , cytochrome c1 , exosome component 4 , poly (ADP-ribose) polymerase family member 10 , nuclear receptor binding protein 2 , GDP-L-fucose synthase , pyrroline-5-carboxylate reductase 3 , eukaryotic translation elongation factor 1 delta , gasdermin D , pyrroline-5-carboxylate reductase 2  and C-X-C motif chemokine ligand 12 .A total of 206 genes were obtained 1 Mbp from the significant SNPs for the serum albumin concentration, containing 155 protein-coding genes, 5 miRNA genes and 46 pseudogenes . Of thesIn this study, we identified nine and seven genome-wide significant SNPs (FDR at 1%) associated with the albumin concentration in colostrum and serum in Chinese Holstein, respectively, using GWASs. For the significant SNPs captured in these two traits, no common genomic regions were shared. Hence, we calculated the correlations between these two traits using GCTA software and the phenotypic and genetic correlations were 0.011 and 0.0996, respectively, suggesting a weak correlation between the colostrum and serum albumin concentration. This is likely the reason that no common genomic regions were identified in the GWAS for these two traits. Besides, albumin is produced by hepatocytes and the majority of the protein is immediately released into the blood circulation  and onlyPopulation stratification and family structure can cause a number of false positive results in GWASs . After aIn this study, the MAF of each significant SNP ranged from 0.107 to 0.463 for the colostrum and serum albumin concentrations. This implied these SNPs could be used for marker-assisted selection or a genomic selection program through selecting the advantageous alleles with positive effects to accelerate the albumin concentration in colostrum and serum in dairy cattle, thereby increasing the disease-resistance ability of calves. The contribution to genetic variance of the SNPs were relatively high with a range of 2.55–4.091% for the colostrum and serum albumin concentrations. Previous studies indicated that the SNPs identified by GWASs generally explain only a small fraction of the heritability, while the SNP effects might be magnified when applying the mixed-model association (MLMA) methods by GCTA ,54,55. DCBR1 and CYC1 were reported to be associated with the oxidation–reduction process. CBR1 encodes the protein belonging to the short-chain dehydrogenases/reductases (SDR) family that acts as an NADP-dependent oxidoreductase in the oxidation–reduction process [CYC1 gene encodes a subunit of the cytochrome bc1 complex whose catalytic activity is required for the release of pro-apoptotic factors from mitochondria and the execution of the subsequent apoptotic steps [After the functional analysis of genes in the regions within a 1 Mbp distance of the significant SNPs, 3 and 12 genes were identified as promising candidates for the concentration of colostrum and serum albumin in dairy cattle, respectively. Of these,  process . The CYCic steps ,58,59. IRUNX1, OTULIN, CDK6, SHARPIN, NRBP2, PYCR3, GSDMD, PYCR2 and CXCL12, were involved in immunity and inflammation. It has been proven that the mutations of RUNX1 initiate the hyperactivation of inflammatory and innate immunity, including the IL-6, TLR, NF-kappaB, IFN and TREM1 signaling pathways [OTULIN is critical for restraining life-threatening spontaneous inflammation, maintaining immune homeostasis and activating NF-kappaB to promote the secretion of pro-inflammatory cytokines and restricts bacterial proliferation in infection [CDK6 is required for the expression of inflammatory genes and is a critical regulator in the NF-kappaB signaling pathway, as well as contributing to cytokine production while inhibiting apoptosis [SHARPIN regulates TLR3-mediated innate immunity, auto inflammation and the development of immunodeficiency [PYCR2 and PYCR3 encode a protein that belongs to the pyrroline-5-carboxylate reductase family of enzymes that responds to genotoxic, inflammatory, nutrient and oxidative stress [GSDMD encodes a member of the adermin family of pore-forming proteins implicated in the immune response [CXCL12 encodes a stromal cell-derived alpha chemokine member of the intercrine family and is involved in many diverse cellular functions, such as immune surveillance, inflammation response and tissue homeostasis [The other set of candidate genes, i.e., pathways . OTULIN nfection ,62. CDK6poptosis ,64. SHARficiency ,66. The ficiency . PYCR2 ae stress ,69. GSDMresponse , which cresponse ,72. CXCLeostasis ,74,75. GPARP10, EXOSC4, GFUS and EEF1D, involved in various viral infections, were identified. PARP10, a member of the poly (ADP-ribose) polymerases (PARPs) family, is related to immunity, metabolism, apoptosis and DNA damage repair [EXOSC4 participated in the regulation of anti-viral responses to decrease human papillomavirus infectivity of keratinocytes [GFUS activated the immune-network to enhance the regulation of apoptosis, T cell homeostasis, neutrophil-mediated immunity, neutrophil chemotaxis, interleukin-8 production, inflammatory response, immune response, B-cell activation and MAPK activity activation during hepatitis C virus infection [EEF1D interacted with HIV-1 transcription protein, resulting in the inhibition of the translation of host cell proteins but in an increase in the translation of viral proteins [Resistance to viral infection is also a crucial capacity for the health and survival of calves. Herein, the candidate genes e repair , and coue repair . EXOSC4 inocytes . GFUS acnfection . During proteins . These sMoreover, from a breeding perspective, the concentration of albumin in serum or milk is influenced by pathological and genetic factors, which highlights the possibility of albumin as a new trait to improve dairy cattle disease resistance. Nowadays, with the comprehensive implication of genomic selection in dairy cattle breeding, where high-density SNP chips, such as Illumina 50 K and GeneSeek 150 K chips, are widely used, most of SNPs are collected from the current SNP database and almost evenly distributed across the whole genome. Therefore, the significant SNPs and genes associated with albumin concentration could be put into such chips to improve the health and resistance to pathogens of newborns in dairy cattle.RUNX1, CBR1 and OTULIN were selected as the candidate genes for the albumin concentration in colostrum. Meanwhile, 12 promising candidate genes were suggested for the albumin concentration in colostrum and serum of dairy cattle, including CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our results provided a genetic view on the regulation of albumin and fundamental information for the genetic improvement program on health and resistance traits in dairy cattle.In summary, our GWAS detected nine and seven genome-wide significant SNPs associated with the concentration of albumin in colostrum and serum, respectively. By integrated analysis of the biological functions of the genes that contain or close to (±1 Mbp) such significant SNPs and the known QTLs for inflammation and immunity capacity,


Hordeum vulgare L.) is one of the major grain crops worldwide and considered as a model plant for temperate cereals. One of the barley row-type groups, named intermedium-barley, was used in our previous study where we reported that other genetic loci rather than vrs1 and Int-c could play a role in lateral spikelet development and even in setting grains. To continue this work, we used phenotypic and genotypic data of 254 intermedium-spike barley accessions aimed at dissecting the genetic basis of development and grain traits of lateral and central spikelet using genome wide association (GWAS) analysis. After genotypic data filtering, 8,653 single-nucleotide polymorphism (SNPs) were used for GWAS analysis. A total of 169 significant associations were identified and we focused only on the subset of associations that exceeded the p < 10−4 threshold. Thirty-three highly significant marker-trait-associations (MTAs), represented in 28 different SNPs on all seven chromosomes for the central and/or lateral spikelet traits; such as kernel length, width, area, weight, unfilled spikelet and 1000-kernel weight, were detected. Highly significant associated markers were anchored physically using barley genome sequencing to identify candidate genes to either contain the SNPs or the closest gene to the SNP position. The results showed that 12 MTAs were specific for lateral spikelet traits, nine MTAs were specific for central spikelet traits and seven MTAs for both central and lateral traits. All together, the GWAS and candidate gene results support our hypothesis that lateral spikelet development could be regulated by loci different from those regulating central spikelet development.Barley ( Hordeum vulgare L.) is one of the most highly produced and consumed grains in the world, classified as the fourth major cereal crop globally [intermedium-barley might be a transition form from two- to six-rowed barley.Barley  Mansf. (syn. H. intermedium Körn.) that exhibits various degrees of lateral spikelet fertility and grain development, an intermediate form between two- and six-rowed types [There are more than 400,000 accessions of barley in worldwide gene banks  that areed types . It mighSix-rowed spike 1 (Vrs1) encodes a homeodomain leucine zipper class I transcriptional activator [VRS2, VRS3, VRS4 and VRS5/INTERMEDIUM-SPIKE C (INT-C) encode homologues of SHORT INTERNODES (SHI), putative Jumonji C-type H3K9me2/me3 demethylase, LATERAL ORGAN BOUNDARY (LOB) and TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR 1 (TB1), respectively [int-c locus, located on chromosome 4HS [There are five row-type genes identified and characterised in barley, ctivator ,10 that ectively ,14,15,16some 4HS ,17, has Vrs1, Vrs4 and Int-c genes in the intermedium barley collection. The results showed that some of the intermedium accessions showed a complete six-rowed phenotype while carrying the wildtype two-rowed allele of Vrs1 [int-c that is considered as a modifier of lateral spikelet development [Int-c.a allele was not able to overpower the suppression of lateral florets in the genomic background of wild barleys and two-rowed-like forms of intermedium barley accessions that carried known functional Vrs4 alleles [Vrs1.b allele [In our previous work , we re-s of Vrs1  in combielopment . Additio alleles  and the b allele . These rThe rapid development of the genomic tools in DNA sequencing revolutionized the way to genetically improve important traits  to bridge the gap between the world production and demands. One of the recent applications of next generation sequencing (NGS) is the genotyping by sequencing (GBS) approach that provides a huge amount of high-quality genome-wide genetic markers . These SNPs are suitable for studying diversity and marker-trait associations ,20 and aintermedium barley collection. Association mapping analyses have been used to identify SNP markers associated with heading and flowering time in barley  better understand the genetic basis controlling spikelet development and growth. Moreover, we sought to (2) identify novel gene(s)/loci that could play a role in lateral spikelet development and even setting grains in the e, e.g., ,25). Howintermedium-spike barley accessions for all traits. Frequency distributions of all accessions for the investigated traits are presented in Phenotypic variation was observed based on the average of the four biological replicates of each accession among the 254 intermedium-spike barley collection showed different fertility degrees for lateral spikelets, with a range from 0%, i.e., completely infertile (two-rowed like), to 100% fertile (six-rowed like), with a mean value of 36%. However, only an average of 18% infertility was noticed for central spikelets. As expected, a higher number of kernels (KN) and lower 1000-kernel weight (TKW) of lateral spikelets were observed. The mean value for KN per spike of central spikelets was 19.03, while it was 34.75 for lateral spikelets. The mean value for TKW of central spikelets was about 50 g, while for lateral spikelets it was 34.32 g  was smaller by about 27.6%, with a range from 9.7 to 33.78 mm2. The mean values of kernel width (KWd) and length (KLn) were 3.59 and 9.83 mm2, respectively, of central spikelets. However, KWd and KLn of lateral spikelets were smaller by about 12.8% and 22.4%, respectively (The average of kernel area (KA) of central spikelets was 25.41 mmectively .A total of 8,653 high quality SNPs distributed on the seven chromosomes  were selintermedium barley was analysed using 8653 SNPs and a model-based approach in STRUCTURE program. The number of subpopulation (K) was plotted against the ΔK calculated and the peak of the broken line graph was observed at K = 6  was detected on chromosome 1H, at 556,903,593 pb (m1_556903593) for KW and KN of central spikelets only. Interestingly we found that this SNP is located in the Early flowering 3 (ELF3) gene, in the exon at 45 bp from the start codon . AnotherOn chromosome 2H, the SNPs m2_727927744, m2_727927750 and m2_727927781 representing the same genomic region were detected for TKW and KWd of central and lateral spikelets and KA of lateral spikelets. However, another region was found on chromosome 3H , associated with TKW and KA, but only for central spikelets, while m3_208358229 was detected for TKW, KA, and KWd, but only for lateral spikelets. On chromosome 4H at 26,351,423 bp, another MTA was detected for TKW and KWd for lateral spikelets. On chromosome 7H, marker (m7_548419008) was detected for KA of both central and lateral spikelets, and KLn of lateral spikelets only.In this study, we focused on a better understanding of the spikelet development and its effects on barley grain production. Yield is a complex agronomic trait, kernel number per spike and kernel weight and size are key factors determining final barley yield. Although some QTLs related to yield traits have been identified, such as grain length ,28,29,30intermedium-spike barleys for morphological and fertility-related traits along the barley inflorescence using GWAS. We phenotyped mature spikes from 254 intermedium-spike barley accessions and found 169 significant MTAs position on all the seven chromosomes that showed associations with lateral and/or central spikelets traits. Here we focused our attention only on the 28 highest significant ones . Tsuda et al. (2004) [multiprotein bridging factor 1 (MBF1) in Arabidopsis functions as a transcriptional co-activator that mediates transcriptional activation by bridging between an activator and a TATA-box binding protein (TBP). Annotations of genes located on the other positions on chromosome 2H encode three different proteins; Expansin B3 at m2_647258179, Elongation factor G (EF-G) at m2_651372029 and Aldehyde dehydrogenase family 3member F1 at m2_653986096. All of these genes have unknown effects on the lateral spikelet development of barley. For lateral spikelets, an MTA at nucleotide position  on chromosome 3H was detected for KWd, KA and TKW, and another MTAs at nucleotide position  on chromosome 4H was detected but only for the KWd and TKW. Another MTAs on chromosome 2H (positions: m2_727927744 and m2_727927750) were detected for the KWd and KA for lateral spikelets   reportedpikelets  as well pikelets . These rome 2 HL  and Int-ome 4 HS  at a posOn chromosome 3H, we detected highly significant MTAs for KA and TKW but only for the central spikelet, suggesting that there are different loci in central spikelets playing a role in controlling these traits. Interestingly, the four detected SNPs  are located in a single gene, a uridine-diphosphate (UDP)-glycosyltransferase superfamily protein. We found that three of these SNPs are causing changes in the amino acid sequence of this gene; m3_661788539 caused a serine (S) to arginine (R) change and m3_661788540 and m3_661788542 caused arginine (R) to glycine (G) changes at amino acid positions three and four, respectively. UDP-glycosyltransferases are enzymes that work on a large and diverse group of highly complex and diverse substrates such as different phytohormones  and flavonoids as well as many others substrates to regulate plant growth and development, including biotic and abiotic resistance [ELF3. The ELF3 gene is responsible for suppressing flowering under non-inductive photoperiods by regulating gibberellin production and FLOWERING LOCUS T 1 (FT1) expression [Interestingly, we detected an MTA for central kernel number and kernel weight at 556,903,593 bp position on chromosome 1H. The annotation data showed that this SNP is located at pression . These rpression . We alsoAll together, the GWAS and candidate gene results support our hypothesis that lateral spikelet development could be regulated by loci different from those regulating central spikelet development. Nevertheless, future experiments may have to reveal the nature of these insights in more detail.intermedium-spike barley accessions, classified based on lateral spikelet size as intermedium-spike barley [Hordeum vulgare L. convar. intermedium (Körn.) Mansf.) from the German Federal ex situ Gene bank hosted at IPK-Gatersleben , were used in this study  and temperature 12 ± 2 °C day and night. Afterwards, each plant was transplanted into 14 cm diameter pot and grown in potting substrate  in the greenhouse under long-day conditions (16 h/8 h) and temperature 20 ± 2 °C during the day and 16 ± 2 °C during the night, and labelled as the main culm. The pots were randomly arranged and periodically moved around to avoid positional effects. The plants were watered when needed.Four plants from each accession of the Briefly, the data were scored from the spike of all labelled main culm plants after harvest for central/lateral spikelet traits, separately as follows: (1) the percentage of unfilled spikelets “UF” (%) were evaluated. Then central and lateral kernels were separated and the following grain traits were scored using a MARVIN grain analyser : (2) kernel number “KN”, (3) kernel length “KLn”, (4) kernel width “KWd”, (5) kernel area “KA” (6) kernel weight “KW” per main spike in gram (g), and (7) thousand kernel weight “TKW”. Four labelled spikes from the main culm of each accession were checked to obtain the average for all traits used for GWAS analysis.intermedium-spike barley accessions. All statistical analyses in this study were conducted using R 3.5.3  per accession. The frequency distribution of all traits was visualized using a histogram. Analysis of variance (ANOVA) of the phenotypic data was performed, which included 254 eam 2018 , Vienna,intermedium barley accessions were genotyped using genotyping-by-sequencing (GBS). DNA was extracted according to Doyle and Doyle protocol [http://www.novocraft.com/). Multi-sample variant calling was done with SAMtools [https://bitbucket.org/ipk_dg_public/vcf_filtering. Only biallelic SNPs were considered. SNPs with minor allele frequency (MAF) of less than 5% and missing data of more than 20% were filtered out. Finally, we used 8653 SNPs for GWAS analysis of 254 intermedium-spike barley accessions.All protocol . GBS wasprotocol . Adapterprotocol   . Severalp-values comparing the uniform distribution of the expected −log10 (p) to the observed −log10 (p) of all the evaluated traits  were considered highly significant SNPs for phenotypic variation. We calculated the false discovery rate (FDR) at p value < 0.05 to determine the significance level of the SNP and exclude false-positive MTAs. Loci with a physical distance less than 100 bp were considered the same locus. Visualization of the significant SNPs was reported using Manhattan plots generated using the R package qqman [p) < 0.05) or highly significant (FDR (p) < 0.01) identified associated markers were anchored physically using the recently published barley genome sequence [http://apex.ipk-gatersleben.de/apex/f?p=284:10).The optimal model (P + K) was selected based on the quantile-quantile (Q-Q) plots of d traits  and genoge qqman . The sigsequence . Based o


Bubalus bubalis bubalis) and swamp buffalo . The buffalo genome has been recently sequenced, and thus a new 90 K single nucleotide polymorphism (SNP) bead chip has been developed. In this study, we investigated the genomic population structure and the level of inbreeding of 185 river and 153 swamp buffaloes using runs of homozygosity (ROH). Analyses were carried out jointly and separately for the two buffalo types.Water buffalo is one of the most important livestock species in the world. Two types of water buffalo exist: river buffalo , and 16,251 of these were unique. ROH were present in both buffalo types mostly detected (~ 59%) in swamp buffalo. The number of ROH per animal was larger and genomic inbreeding was higher in swamp than river buffalo. In the separated datasets , 19,760 and 10,581 ROH were found in river and swamp, respectively. The genes that map to the ROH islands are associated with the adaptation to the environment, fitness traits and reproduction.Analysis of ROH features in the genome of the two water buffalo types allowed their genomic characterization and highlighted differences between buffalo types and between breeds. A large ROH island on chromosome 2 was shared between river and swamp buffaloes and contained genes that are involved in environmental adaptation and reproduction.The online version contains supplementary material available at 10.1186/s12711-021-00616-3. The domestic water buffalo represents a fundamental livestock resource for rural populations in many areas of the world, providing milk, meat and traction power. This species is also farmed in intensive dairy systems , 2 and iBubalus bubalis bubalis) and the swamp buffalo . However, it is still under discussion if they should be considered as two distinct species, subspecies, or ecotypes. The two buffalo types have a different number of chromosomes due to a tandem fusion between chromosomes 4 and 9 in the swamp buffalo, which results in 50 and 48 chromosomes in the river and swamp buffalo, respectively [Two types of water buffalo exist: the river buffalo .The water buffalo genome was recently sequenced by an International Consortium  and a hiBos taurus reference sequence [During the sequencing project, a medium-density 90 K SNP bead chip was developed in collaboration with Affymetrix (Axiom) using single nucleotide polymorphism (SNP) positions that were based on the sequence . This 90sequence , 13–15, sequence , and invsequence . Most ofClassical population genetic approaches that are used to study biodiversity, demography, population structure, and inbreeding, can be extended by investigating the distribution of runs of homozygosity (ROH). ROH are continuous stretches of homozygous genotypes that are found along the genome . The proHere, we report the distribution and features of ROH in river and swamp buffalo populations, and use the information for an in-depth genomic investigation of domestic water buffalo to elucidate the genomic structure of different breeds and populations.The dataset consisted of genotypes for 185 river and 153 swamp buffaloes from 15 river and 13 swamp breeds or populations . The array was originally developed based on the Bos taurus sequence [All the animals were genotyped with the 90 K Affymetrix Axiomsequence . In the sequence . Since tsequence . For thiInitially, all SNPs and animals were quality-controlled as a single set. An animal was discarded if its individual call rate was lower than 0.95 and a SNP was discarded if the call rate was lower than 0.95, and the minor allele frequency (MAF) was lower than 0.01. Additional filtering steps were carried out separately for each of the three datasets. For the ALL_DATA set, only SNPs that were polymorphic in the swamp buffaloes were retained, according to the approach followed by  to reducDefining the settings for the detection of ROH is a crucial point because of their effect on the results. The detection of ROH was carried out separately for each of the three datasets using the Zanardi pipeline . In the The following statistics were calculated: number of ROH per animal (n_ROH), average ROH length (l_ROH), ROH distribution across five classes of length that are usually considered for cattle and sheep . The total number of ROH could include exactly the same chromosomal segment repeated in different animals. Thus, a unique ROH was defined as a homozygous segment that starts and finishes at exactly the same precise chromosomal positions . We alsoROH), i.e., the number of animals that have a given SNP in a ROH was also calculated [ROH value in the top 1% of the distribution was considered as significant [ROH, genomic regions that contained uninterrupted sequences of significant SNPs were defined as ROH islands.The ROH count per SNP  for each animal was calculated as the ratio between the length of the genome covered by ROH and the total genome length [GROH) by coding SNP genotypes as follows: 0 = heterozygous; 1 = homozygous not included in a ROH; and 2 = homozygous included in a ROH. SNP coding was based on their presence/absence in ROH longer than 4 Mb. GROH was then calculated according to [The ROH-based inbreeding coefficient  databases.The functions of the genes included in ROH islands were investigated, with particular attention to regions where significant markers according to their SNPST) for the ALL_DATA set using the equation proposed by [ST interpretation was improved by removing noise from the raw signals with a locally weighed scatterplot smoothing (LOWESS) procedure [ST distribution was adopted [In order to compare the results of ROH detection, particularly their chromosomal location and the length of ROH islands, with more widely used parameters, we calculated the Wright fixation index  was analyzed with a generalized linear model that included the fixed effects of type and breed nested within type.In the RIVER_DATA and SWAMP_DATA sets, ROH length was analyzed with the following generalized mixed linear model:The relationship between the occurrence of an SNP in a ROH and the type of buffalo was assessed in the ALL_DATA set using a logistic regression model. For each individual, the SNP was coded as 1 if included or 0 if not included in a run. This binary variable was analyzed with the following logistic regression model:ROH were found in all breeds and populations for both water buffalo types, with 18,116 ROH detected in the ALL_DATA dataset Table . The larSwamp populations had more ROH per individual compared to river breeds Fig. . The swaBubalus bubalis chromosome (BBU) 10 in an Indonesian Nusa Tenngara individual. The chromosomes with the largest average l_ROH were BBU18 in river (5.8 Mb) and BBU13 in swamp (4.1) buffalo, respectively . The average values of l_ROH were largest for river buffaloes (LSmean and SE Mb 2.87 ± 0.05) compared to swamp buffaloes (2.67 ± 0.04). Significant differences were also observed among chromosomes, breeds or populations and between types (data not shown). LSmeans was largest on BBU18 (3.32 ± 0.11) and smallest on BBU19 (2.33 ± 0.05). The repeatability for this trait was 0.08.Average l_ROH was generally larger in river buffalo breeds Fig. , althougin swamp .1 buffalROH values were obtained for the two buffalo types when all homozygous segments were considered  was detected on BBU2 at about 50.8 Mb. In total, 176 SNPs, located on six chromosomes, exceeded the 99th percentile threshold of the SNPROH distribution and were considered as significant  for 1876 of 17,784 SNPs. The overall average odds ratio (OR) was 1.73 (± 1.37), which indicates that the PRSNP_ROH increases by about 70% from river to swamp buffalo. If only the 176 significant SNPs exceeding the 99th percentile of the SNPROH distribution are considered, the average OR increases to 4.16 (± 1.37), with a fourfold increase of PRSNP_ROH in swamp compared to river. Although for most of the 176 significant SNPs the OR was higher than 2, which means that PRSNP_ROH is more than twice as high in swamp than in river buffalo, 42 of these SNPs exhibited values lower than 1.0, which indicates that, for these markers, the PRSNP_ROH is higher in river than in swamp buffalo. Finally, all ROH islands except one showed average OR values higher than 1 (Table The results of the logistic regression highlighted significant differences (P < 0.01) between river and swamp buffaloes in terms of the probability of a SNP to be included within a ROH  were unique Table . The disP < 0.001), with the largest value found for the Brazilian Murrah breed (LSmean and SE Mb 2.07 ± 0.05) and the smallest for the Pakistani Aza-Kheli breed (1.40 ± 0.12). Pairwise comparisons highlighted significant differences between the Brazilian Murrah and all of the other breeds, except the Iranian Mazandarani and Mozambique breeds. BBU5 had the largest l_ROH LSmean (1.82 ± 0.04) and BBU21 the smallest (1.56 ± 0.04). BBU2 was statistically different from BBU12, 17, and 19. The repeatability for this ROH characteristics was 0.05.The longest average l_ROH was found in the Brazilian Murrah breed (3.22 Mb) and the shortest was in the Pakistani Aza-Kheli breed (1.53 Mb). The general pattern was similar to that observed in the ALL_DATA set. l_ROH was significantly affected by breed and chromosome  belonged to the Mediterranean Mozambique breed and that with the smallest number (37) belonged to the Pakistani Aza-Kheli breed. The longest (66.47 Mb) and shortest (1 Mb) ROH were both found in Italian Mediterranean buffaloes, on BBU10 and BBU7, respectively. The most frequently detected ROH, i.e. in nine animals of the Italian and Mozambique Mediterranean breeds, was located on BBU9 . In total, 432 SNPs exceeded the threshold of the 99th percentile of the SNPROH distribution. They were located on 16 chromosomes . The swamp buffalo population of Thailand had the largest LSmean, which was statistically different from that of most of other groups (Mb 3.55 ± 0.21), and the Chinese Hunan swamp buffalo (Mb 2.35 ± 0.10) had the smallest LSmean. The repeatability of this ROH characteristic was 0.08. n_ROH was affected by population, with the largest (118.23 ± 8.64) and smallest (28.57 ± 2.48) LS means found for Indonesian Nusa Tenggara and Thailand buffaloes, respectively. Pairwise comparisons mostly highlighted significant differences between Indonesian buffaloes and the other populations.ROH (0.32) was a Nusa Tenggara buffalo.The Indonesian Nusa Tenggara population had the largest average inbreeding coefficient (0.16 ± 0.07) and the Chinese Yangzou population had the smallest (0.04 ± 0.01). The individual with the largest FROH in the SWAMP_DATA set was 15 (± 7.8). One hundred and sixty-nine SNPs exceeded the threshold of the 99th percentile of the SNPROH distribution and were located on six chromosomes  [RCAN1) [ARHGEF10) [ARHGEF10, CLN8 and DGAP2, that were associated with hairless phenotype in pigs [IFNGR2, IFNAR1, and SYNJ1.The largest ROH island in the RIVER_DATA set (about 10.8 Mb and 109 SNPs) was detected on BBU3  , iron co [RCAN1) , and fatRHGEF10) . Further in pigs . This RO in pigs , 65 and  in pigs : IFNGR2,LGR5 on BBU4, previously proposed as a candidate gene for supernumerary teats in cattle [UBE2H on BBU8 that is associated with feed efficiency in cattle [PCDHB7, which has been suggested as a candidate gene for milk protein composition [Streptococcus agalactiae [CDK10 on BBU18 at ~ 14–14.6 Mb, which has been reported to be present in a selection signature in African local cattle breeds [Several other interesting genes were identified within the ROH islands detected in river buffaloes: n cattle ; UBE2H on cattle ; two genposition , and CD1alactiae ; and CDKe breeds .GROH calculated by using the SNPs of the ALL_DATA set, explained 4 and 1% of the total variance, respectively. The plot of eigenvector coefficients  than in river buffaloes when they were analyzed jointly using the same set of SNPs. The average ROH frequency per animal detected in the two buffalo types  is comparable with figures that have been previously reported for buffalo, e.g., Aza-Kheli and Khuzestani Iranian river buffalo breeds that have an average n_ROH of 21 and 33, respectively . The aves cattle , 75 and s cattle . HoweverThe occurrence of ROH in a population mirrors its genetic and demographic history. The present study highlighted a difference in ROH number between river and swamp buffaloes, especially for the shortest ROH (< 4 Mb) Fig. . Swamp bThe observed higher frequency of short ROH in swamp buffalo individuals is likely to be related to their genetic history, in particular to the lack of strong anthropogenic selection and to geographic differentiation . The rivGROH for genomic prediction purposes based on the coalescence theory and was able to account for mutation and recombination [GROH based on the recoding of SNP genotypes according to their location within a ROH is proposed. This method estimates a matrix that could be calculated following the method of [. The separation between types and breeds using the eigenvalue decomposition of the GROH matrix that is calculated by using ROH longer than 4 Mb  gave an average F Mb gave ROH observed for swamp buffaloes in this case was basically due to a larger n_ROH, and particularly of short length (< 4 Mb)  Table ; Fig. 1.g et al.  pointed Genomic regions characterized by a high level of homozygosity have been detected in many livestock species , 74, 76.ROH exceed a certain threshold) is a more flexible way of comparing homozygous regions between individuals. The largest ROH island detected in the two buffalo types, using the three datasets, was located on BBU2, between 47 and 58 Mb. A ROH island located on BBU2 between 52.8 and 53.8 Mb was recently reported in Murrah buffaloes [Bos indicus [Ovis aries chromosome OAR2 has been detected in three sheep breeds  [myostatin (MSTN) gene, which is involved in muscle development and most likely is under selection. A possible interpretation for the occurrence of the same ROH island in different species could be due to evolutionary convergence caused by the selection on the same group of genes. It is noteworthy that two of the genes present in the ROH island on BBU2 (ARHGEF4 and PLEKHB2) which are associated with milk yield and feed efficiency in ruminants, are also present in a ROH hotspot on chromosome 18 of the horse. Although shorter ROH islands represent a weak signal that needs to be carefully validated with other studies and approaches, the consistency between the results on the ROH located on BBU2 with the three datasets of the present study and those from other studies on buffalo and other species suggests that the ROH approach is useful to study the major features of genomes.The use of unique ROH to study the genomic similarity between individuals is restrictive when they share the core of a region of homozygosity that does not start or finish exactly at the same position. Since a ROH roughly reflects an IBS haplotype, the precise location of the beginning and end of a ROH may not be important. The use of ROH islands in our study  . This ROShort ROH are ancient and may derive from environmental adaptation, whereas longer ROH are more recent and are more likely to result from artificial selection or population bottleneck events. We found that ROH were longer in river than swamp buffalo breeds, which reflects the different histories and usages of each buffalo type worldwide. In fact, river buffaloes are subdivided in a number of well-recognizable breeds, based on geographical location and morphological traits . They are mainly farmed and selected for milk production, and secondarily for meat. Conversely, swamp buffaloes are phenotypically homogeneous throughout their distribution area and no breeds are formally recognized. Swamp buffaloes, which are farmed primarily as draught animals for ploughing and transport, are not subjected to intense human-mediated selective pressures and also provide meat as a secondary product.ARHGEF4, which is known to be associated to dairy traits, was detected in the ROH on BBU2 that is shared between both buffalo types. All the other genes related to production traits were identified in the analyses of the RIVER_DATA set but not of the SWAMP_DATA set: e.g., PCDHB7 and UBE2H, which are related to dairy traits, and TRNAG, MYOM2, AADAT, and RCAN1, which are related to beef traits. MYOM2 is present in a signature of selection that was identified in a comparison between Atzeri and Khuzestani Iranian buffaloes using FST metrics [A few genes related to production traits were located in the ROH islands detected on the buffalo genome.  metrics . The smaADAM32, IQCG, and DST), female fertility  and embryogenesis . The second largest group of genes identified in ROH are involved in disease resistance such as KALRN, ITGB5, MUC13, HEG1, and LHCR3 in the ALL_DATA set, and HLF, MMD, SH3RF1, STXBP4, CD14, and CDK10, in the RIVER_DATA set.Our results suggest that the main driving force in shaping the genome of the domestic water buffalo is adaptation to the environment. This is supported by most of the genes that map to the ROH islands detected in the ALL_DATA set and that are related to fitness traits. In particular, the largest group consists of genes that are associated with reproduction traits in livestock: male fertility , and breathing rate (LIMS2). Another interesting group of genes that were identified within ROH is associated with the hairless phenotype  [UMPS, TPN18, and PLEKHB2). The efficiency of the process of digestion is extremely important for surviving in extreme arid areas [Surviving and producing in arid areas represents a strong environmental challenge for livestock. The buffalo is a species that has a reduced tolerance to heat stress due to the poor distribution of its sweat glands, its very short and sparse hairs, and the dark color of its body . Howeverd DGAP2) . Finallyid areas . Our resid areas .ST values were calculated for the ALL_DATA set. Some SNPs that distinguished the two buffalo types also frequently occurred in homozygous regions. Moreover, average FST values of SNPs included in the ROH islands were larger than the average values for the corresponding chromosome. These results further confirm that different metrics, although calculated from the same data, can offer complementary perspectives to analyze genome features and we recommend their combined use.Selection footprints identified from ROH analyses should be corroborated by other well-proven techniques. Although one should keep in mind that most metrics are based on comparisons between breeds or populations, whereas the rationale behind the search for selection sweeps using ROH is the sharing of homozygous regions by a large number of animals. In our paper, FIn the present study, runs of homozygosity were used to investigate the genomic structure of the two buffalo types and their breeds, and to compare populations of domestic water buffalo. In particular, the dissection of the ROH-based genomic relationship matrix suggested that the two buffalo types have undergone environmental adaptation. The geographical isolation of populations together with genetic drift, have played a substantial role in shaping the genome of buffalos, especially of the swamp type. Analysis of the distribution of ROH islands on BBU2 identified a region that is shared with other ruminant species. The evolutionary significance of this region merits further investigation.Additional file 1:Table S1. Minor allele frequency in river and swamp buffalos.Additional file 2:Figure S1. Plot of linkage disequilibrium (r2) according to the distance between markers in river and swamp.Additional file 3:Figure S2. Frequency distribution of ROH across chromosomes in river (black bars) and swamp (white bars) buffalo.Additional file 4:Figure S3. Distribution of the average ROH length per chromosome in river (white bars) and swamp (black bars) buffalo populations.Additional file 5:Table S2. Top ten most frequently detected ROH in the ALL_DATA set.Additional file 6:Figure S4. Stacked bar graph of ROH distribution on BBU4 in river (a) and swamp (b) buffalo.Additional file 7: Figure S5. Stacked bar graph of ROH distribution on BBU17 in river (a) and swamp (b) buffalo.Additional file 8:Figure S6. Manhattan plot of smoothed FST values for chromosomes 1 to 24.Additional file 9: Table S3. Top five most frequently detected ROH in RIVER_DATA and SWAMP_DATA sets.Additional file 10:Table S4. Chromosomal location of significant SNPs  in the RIVER_DATA and SWAMP_DATA sets.Additional file 11:Table S5. Genes mapped in the ROH islands highlighted in this study [is study –112.Additional file 12:Figure S7. Relationship between number of ROH and total length of the genome covered by ROH .Additional file 13:Figure S8. Relationship between number of ROH and total length of the genome covered by ROH (using ROH with length ≥ 4 Mb detected in the ALL_DATA set).


ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc  and Landrace  across the population and for low feed efficient  and high feed efficient group  for FCR and RFI were identified. Gene and pathway analyses identified  Genetic or epistatic interactions between SNPs, genes or QTLs is a topic of interest in molecular and quantitative genetics  as it ocEpistatic interactions between genes for some quantitative traits, such as meat quality , carcassGWAS being a single-step approach tests each SNP with the trait of interest, which gives rise to multiple testing problems. Additionally, due to the high stringent cutoffs, some of the most biologically relevant SNPs are missed. Thus, searching for the missing heritability of the traits has led us to include the genetic or epistatic interactions, as it would help us to identify markers explaining a higher proportion of heritable variance. Furthermore, if all the epistatic SNPs are identified, it would help to establish a framework to explicitly include epistatic variance in future methods of genomic prediction in addition to additive variance. This approach would increase the accuracy of genomic prediction for FE and help the pig breeding industry.Systems genomics and network biology methods focus on detecting interactions among genes and relate them to phenotype or disease manifestation . WISH W has beenad libitum fed and free water supplied. The authors of this study were not responsible for animal husbandry, diet, and care as the testing station is a facility within the Danish breeding program run by SEGES.A general overview of the study design and the main analyses steps is represented in Blood samples at Bøgildgård were collected from jugular venipuncture from each pig into tubes containing EDTA and immediately placed on ice. The pigs were purebred uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace breeds (n = 50), amounting to a total of 109 animals. The breeds have been mentioned as Duroc and Landrace further in the paper.For the phenotypic traits, the weight of feed consumed (FC) and FE for each pig in the testing phase were measured, beginning with an initial weight of around 28 kg for each animal. The testing phase ranged from 41 to 70 days based on the viability of each pig. Bodyweight during the beginning and end of the test were recorded from the standard test procedure of the testing station, and their difference was referred to as delta weight (DW). The feed conversion ratio (FCR) was calculated as the ratio between the amount of FC and DW. Likewise, the residual feed intake (RFI) was estimated as the difference between the observed daily feed intake (DFI) and the predicted feed intake (pDFI) . A summahttps://www.neogen.com/uk/). Genotyping was conducted using GGP Porcine HD array , featuring over 68,516 SNPs across 18 autosomes and two sex chromosomes.DNA isolation was carried out from the collected blood, and SNP genotyping was outsourced to GeneSeek  was carried out using Plink software . Samplesassoc” function. For this, two approaches were adopted as follows: we first carried a breed-specific analysis, considering the whole population (Duroc—59 samples and Landrace—50 samples). Then, the association was also performed within groups for each breed to identify group specific SNPs. The computed p-values were used in the later steps as a filter.To identify meaningful SNPs related to FCR, both among and within breeds (LFE and HFE groups), we carried an association analysis using Plink version 1.07 based on the “LD_blocks” function, with a maximum block size of 1000, and threshold = 0.9 from WISH-R package , 1 (heterozygote) or 0  based on the function “generate.genotype” in WISH-R which uses file in the PED and TPED file format from Plink  estimated by WISH-R were visualized using the function “genome.interaction” with a quantile size of 0.9. The genome-wide interaction overview was calculated with the quantile values of the significance of the interaction between chromosomes. It indicated the chromosomal hotspots for the interaction of a given phenotype. WISH-R default settings were used to calculate chromosomal interactions.The epistatic interactions , and related the MSNP with the traits of interest. We constructed a signed weighted network, using “To unravel biologically meaningful modules, we used the module eigenSNP to examine the relationship between the FCR and the modules, resulting in a genome-wide module association test. We also included the RFI in the association analysis to identify epistatic SNPs for both the traits.adjP. The highest interacting SNPs were identified considering the SNP connectivity score estimated from the “softConnectivity” function (The top 3 significant modules (p ≤ 0.05 and r > 0.4) were selected for downstream analysis to unravel the biological pathways underlying the traits. The P-values used were adjusted using Bonferroni approach referred as function . The SNPEnsembl database, Sscrofa11.1. The unidentified SNP annotation from Biomart was done through g:Convert in g:Profiler (https://biit.cs.ut.ee/gprofiler/convert)  (The SNP annotation was done using BiomaRt (R package)  from theconvert) . Gene onconvert)  based onpe.org/) . A heatmAfter quality control (QC), 48,444 and 49,876 loci in Duroc and Landrace on 18 porcine autosomes were available for association analysis with FCR. Likewise, 51,852 and 51,335 SNPs from Duroc and Landrace, respectively, passed QC in LFE and HFE groups.From the association analyses, the top 7,000 SNPs were selected, and based on WISH-R package , we calcadj <0.01) with both FCR and RFI traits , MEblue (637 SNPs) and MEred (313 SNPs) were significantly associated  and RFI   was significantly correlated with FCR. No significant associations were identified for RFI  and Landrace , only 64 SNPs were found common, while 1,183 SNPs were unique to Duroc and 1,151 were unique to Landrace. In DLFE and DHFE groups, with 80 and 198 hub-SNPs, respectively, four SNPs were common pointing towards the difference in SNP interactions in both breeds. In LLFE and LHFE groups, among 146 and 232 hub-SNPs in each group, respectively, only six SNPs were found common between them. The annotated hub-SNPs Table 2 Erb signaling pathway, Ras signaling pathway, and Rap1 signaling pathway. For DLFE group, SNP-SNP interaction pointed out to thiamine metabolism and phenylalanine, tyrosine, and tryptophan metabolism. In DHFE group, the pathways resulting from the interaction were the hippo signaling pathway and glycosaminoglycan biosynthesis. In Landrace, the major pathways involved were cGMP-PKG signaling pathway, cAMP-signaling pathway, MAPK signaling pathway, aldosterone synthesis, and secretion, and thyroid hormone synthesis. Lysine degradation was found to be the most significant pathway underlying LFE group. Additionally, the over-represented pathways included arginine and proline metabolism, and fatty acid biosynthesis as well. Pathways like fatty acid elongation and degradation, valine, leucine, and isoleucine degradation, lysine degradation, and tryptophan metabolism were identified for the LHFE group -anchor biosynthesis, and nitrogen metabolism. In Landrace, the major pathways involved were cGMP-PKG signaling pathway, cAMP-signaling pathway, MAPK signaling pathway, aldosterone synthesis and secretion, and thyroid hormone synthesis. Furthermore, lysine degradation was found to be the most significant pathway underlying LLFE group, followed by arginine and proline metabolism and fatty acid biosynthesis as some other pathways. Pathways like fatty acid elongation and degradation, valine, leucine and isoleucine degradation, lysine degradation, and tryptophan metabolism were identified for the LHFE group.Based on the over-representation pathway analysis, NRG4 gene as given in our study. The ErbB receptor tyrosine kinases include the epidermal growth factor receptor (EGFR), ErbB2, ErbB3, and ErbB4. These are important for intestinal tract homeostatic maintenance (NRG4) induces apoptosis (PDK1) (PTEN), which acts to oppose PI3K activity. The serine/threonine kinase AKT, also known as protein kinase B (PKB), possesses a PH domain and is recruited to the plasma membrane along with PDK1. Phosphorylation of amino acid residues by PDK1 and mTORC2 (a subunit of mTOR from P13K-related kinase family), respectively, is essential for full AKT activation  . The sigtivation . Previou in pigs .ABL1, MAP3K4, MAP3K5 (chr 1), SEMA6A (chr 2), KITLG (chr 5), and KAT2B (chr 13), identified from highly interconnected epistatic SNPs also pointed out towards ErbB, Ras, Rap1, thyroid hormone, axon guidance signaling pathways in Duroc. Mitogen-activated protein kinase (MAPK) cascades have been shown to play a key role in transduction extracellular signals to cellular responses   . Some otd intake . The MAPd intake . KAT2B w in pigs . MAPKs r in pigs . Genes Pin Duroc . P14KB wean pigs .TPK1 (chr 9) was significantly involved with thiamine metabolism. The most active form of vitamin B1 is thiamin pyrophosphate; its synthesis in eukaryotes requires thiamine pyrophosphokinase, which catalyzes pyrophosphate group transfer from ATP to thiamine , DLG2 (chr 9), PARD6G (chr 6), and PPP1CB (chr 3) pointed towards the hippo signaling pathway. This pathway is an evolutionarily conserved and it controls organ size by regulating cell proliferation, apoptosis, and stem cell self-renewal (CHST4 (chr 6) gene, was also significant in the HFE group. This pathway was identified in a GWAS study of beef cattle for growth and intake components for feed efficiency  are hydrolyzing enzymes that terminate the intracellular effects of cyclic nucleotides by their hydrolysis and prevents continuous activation of the downstream effector proteins  and 3′,5′-cyclic guanosine monophosphate (cGMP) are ubiquitous intracellular second messengers that regulate multiple physiological functions . The higproteins . cGMP-reoss-talk . Aldosteoss-talk . The ATP in pigs  and was e cattle  while CAchickens . MAPK paal tract .SETD7 (chr 8) and PLOD1 (chr 6) were involved with lysine degradation in our study. Lysine is a limiting amino acid, and its deficiency impairs the animal’s immunity and growth performance  was involved in most of the pathways, which are also found to affect lipid metabolism in feed efficiency in pigs (LPCAT3 involved with ferroptosis incorporates arachidonic acid (lipid) into the membrane of the intestine and liver cells, which enables triacylglycerols to be assembled into lipoproteins , were identified in Duroc and Landrace across the whole population for FCR and RFI traits. The approach followed here, provided many interesting genes and interactions with significant p-values. While some of the identified hub-SNPs were linked to genes well known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. The main over-represented pathways in Duroc were the mTOR and MAPK pathways. In Landrace, the main pathways were cAMP, cGMP-PKG pathway, aldosterone synthesis and secretion, and purine metabolism. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction that includes epistasis, increasing the accuracy of estimated genetic merit for FE, as well as helping the pig breeding industry.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144064.We have made our genotype data along with metadata publicly available in NCBI-GEO data repository with the accession number: GSE144064 and the link: The feed efficiency experiment were approved and carried out in accordance with the Ministry of Environment and Food of Denmark, Animal Experiments Inspectorate under the license number (tilladelsesnummer) 2016-15-0201-01123, and C-permit granted to the principal investigator/senior author (HK).HK conceived and designed this “FeedOMICS” project, obtained funding as the main applicant. VC and HK designed the blood sampling experiments, phenotype data collection, and biostatistical/bioinformatics analyses. PB and VC carried out biostatistical and bioinformatic data analyses. All authors collaborated in the interpretation of results, discussion, and write up of the manuscript. All authors have read, reviewed, and approved the final manuscript.This study was funded by the Independent Research Fund Denmark (DFF) – Technology and Production (FTP) grant (grant number: 4184-00268B).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.


Thermoproteus and the genus Sulfolobus, respectively. These findings provide new insights into the phylogenetic and genomic diversity for archaea species that inhabit the El Tatio geysers field and expand the understanding of the Crenarchaeota phylum diversity.A phylogenomic and functional analysis of the first two Crenarchaeota MAGs belonging to El Tatio geysers fields in Chile is reported. A soil sample contiguous to a geothermal activity exposed lagoon of El Tatio was used for shotgun sequencing. Afterwards, contigs were binned into individual population-specific genomes data. A phylogenetic placement was carried out for both MAG 9-5TAT and MAG 47-5TAT. Then functional comparisons and metabolic reconstruction were carried out. Results showed that both MAG 9-5TAT and MAG 47-5TAT likely represent new species in the genus  El Tatio  is the largest geyser field in South America, it is located in the Andes mountain at 4300 m above sea level in the Antofagasta Region, Chile. This geothermal field presents several extreme conditions such as high temperatures, high salinity, presence of heavy metals, among the main abiotic factors . All afoMethanococcus, Thermoprotei, Thermococcus and Crenarchaeota, where Crenarchaeota has been described as the most predominant [Most microbial diversity studies carried out in El Tatio have been mainly focused on bacteria domain. Thus, the archaea domain remains poorly studied in this environment even when there is evidence about their predominance and functionalities mainly associated to sulphur and methane metabolism. Some of the most abundant archaea inhabiting El Tatio are dominant ,7,8. Credominant . Most ofdominant ,11. HencSamples used in this study  were col® Kit . Briefly, samples were heated at 70°C for 10 min and immediately frozen at −80°C for 10 min. Then, enzymatic lysis was carried out with Lysozyme (1:100) and proteinase K (20 mg/mL) (Thermo Fisher Scientific). From this step, the DNA extraction process was carried out according to the DNeasy PowerSoil® Kit  manufacturer indications. Afterwards, a random amplification protocol was carried out with the GenomiPhiTM DNA Amplification  to increase the concentration of extracted metagenomic DNA. Finally, DNA quantification was carried out using the One DNA QuantiFluor® ONE dsDNA System  on a Quantus fluorimeter. Metagenomic DNA was sequenced under a metagenomic approach on a Novaseq6000 sequencer using a LITE library 150 bp PE at Earlham Institute .Genomic DNA was extracted from soil samples by a modified method using the DNeasy PowerSoilMetagenomic reads were quality trimmed using TrimGalore v0.6.0  followinFor phylogenetic placement of archaeal MAGs, a taxonomic genome assignment was performed with Centrifuge 10.3  using thGene prediction was carried out with Prodigal v2.6.3  and thenThermoproteus uzoniensis representative genome available on NCBI and MAG 47-5TAT was compared to Sulfolobus tokodaii representative genome available on NCBI.To characterize and compare functions between our MAGs and their closest neighbors, we identified shared and unique gene clusters and visualized them in Anvi’o using the pangenomic workflow. For this purpose, MAG 9-5TAT was compared to The temperature of the sampling site (22°22’00 S 67°59’22.0 W) was 89°C with a pH of 5.4, and it is important to note that the soil sample showed clay characteristics. These temperatures are common for this environment ; howeverThermoproteus and MAG 47-5TAT is affiliated to the Sulfolobus genus  respiration is common; however, many other electron acceptors are used by various species and studies have already reported and demonstrated that Crenarchaeota plays essential roles in sulphate reduction ,33. All oniensis .Additionally, several resistance mechanisms to toxic compounds were detected in MAG 9-5TAT, most relevant are cooper, cobalt, zinc, cadmium and mercury resistance pathways. Metabolic machinery for the response to oxidative stress and carbon starvation was also predicted. All aforementioned is completely related to the environmental conditions of El Tatio, where oxidative stress and the presence of heavy metals are a common characteristics . HoweverSulfolobus genus [2 as the main carbon source. This MAG showed methanogen capability, in this regard, among methanogenic archaea previously detected in the El Tatio, genera Methanospirilum and Methanobrevibacter have been reported as the most prevalent [Functional annotation of MAG 47-5TAT showed a genome length of 1,265,490 bp. Overall, 746 COGs were detected from 1673 predicted ORFs. Metabolic reconstruction indicates that MAG 47-5TAT possess the capacity of carbon fixation and it uses methane and sulphur for energy metabolism. Carbon fixation and sulphur metabolism have been described as core functionalities in the us genus . All therevalent . In addiSulfolobus species such as Sulfolobus solfataricus [Central carbon metabolism of MAG 47-5TAT showed a metabolic potential to degrade glucose, galactose, and also possesses metabolic pathways to degrade polysaccharides such as glycogen. The aforementioned has been reported as a common characteristic for ataricus ,38Sulfolobus species have gained the ability to utilize additional nitrogen sources such as nitrate and S. tokodaii is one of them [Sulfolobus genus and probably is the distinctive functionality linking MAG 47-5TAT with this Sulfolobus species. In addition, several oxidative stress pathways such as protection from reactive oxygen species, CoA disulfide thiol-disulfide redox system and rubrerythrin were also identified. Some Crenarchaeotas are considered a source of metabolites of biotechnological interest. Within this context, archaea capable of inhabiting environment with high temperatures and acidic pH are expected to produce temperature and acid-stable enzymes, biomaterials and metabolites [Nitrate/nitrite ammonification and ammonia assimilation pathways were identified in this MAG. This metabolism is interesting for MAG 47-5TAT since only a few  of them ,39. Therabolites . In consFinally, because the two MAGs that have been analysed in this work likely represent new species in the phylum Crenarchaeota, we studied the metabolic differences between both MAG 9-5TAT and 47-5TAT and their respective closest neighbours . Thermoproteus. Similar findings were obtained through the pangenomic functional analysis for MAG 47-5TAT. Results showed that, compared with its closest phylogenetic neighbour, MAG 47-5TAT possess eight metabolic pathways (When analysing the genome metabolic profiles in the genomic reconstruction, six metabolic pathways not detected in the closest species were found in MAG 9-5TAT, which are related to energy metabolism . Among tpathways  not deteThermoproteus and the genus Sulfolobus, respectively. These findings provide new insights into the phylogenetic and genomic diversity for archaea species that inhabit the El Tatio geysers field and expand the understanding of the Crenarchaeota phylum diversity.In summary, both MAG 9-5TAT and MAG 47-5TAT likely represent new species in the genus