Brassica juncea  at a slower pace. Double digest RAD (ddRAD) - a genome complexity reduction technique followed by NGS was used to generate a total of 23 million paired-end reads from three genotypes each of Indian  and Exotic  genepools.Narrow genetic base, complex allo-tetraploid genome and presence of repetitive elements have led the discovery of single nucleotide polymorphisms (SNPs) in B. juncea genotypes. SNP genotyping-based genetic diversity and population studies placed the genotypes into two distinct clusters based mostly on the place of origin. The genotypes were also characterized for six morphological traits, analysis of which revealed a significant difference in the mean values between Indian and Exotic genepools for six traits. The association analysis for six traits identified a total of 45 significant marker-trait associations on 11 chromosomes of A- and B- group of progenitor genomes.Sequence data analysis led to the identification of 10,399 SNPs in six genotypes at a read depth of 10x coverage among the genotypes of two genepools. A total of 44 hyper-variable regions (nucleotide variation hotspots) were also found in the genome, of which 93% were found to be a part of coding genes/regions. The functionality of the identified SNPs was estimated by genotyping a subset of SNPs on MassARRAY® platform among a diverse set of Brassica juncea.Despite narrow diversity, the ddRAD sequencing was able to identify large number of nucleotide polymorphisms between the two genepools. Association analysis led to the identification of common SNPs/genomic regions associated between flowering and maturity traits, thereby underscoring the possible role of common chromosomal regions-harboring genes controlling flowering and maturity in  Brassica juncea commonly known as Indian mustard is an important oilseed crop in Indian subcontinent, northern China and eastern European countries. It is widely and extensively grown for seeds which yield an essential oil and condiment; however its young leaves are also used as vegetables or mixed with other salad greens. Brassica juncea has two diverse genepools: the Indian and the east European genepool (exotic) [(exotic) . The eas(exotic) , 3. In s(exotic) . This na(exotic) .Brassica spp. (B. juncea and B. napus) different types of molecular markers were employed with a combination of various models  to figure out close relationship between various traits and markers. In most of the association mapping studies, the SSR markers were used for population studies due to their usefulness in population genetics inferences and these being highly informative when compared to biallelic markers [B. juncea. Various SSR-based genome-wide association mapping studies were conducted in B. juncea [B. napus [B. napus [B. juncea, mainly due to non-availability of SNP markers.The genetic enhancement can be achieved by the transfer of alleles between exotic (European) and Indian genepools using either traditional plant breeding approaches or marker-assisted selection (MAS). While MAS require the identification and use of closely and tightly linked molecular markers with the trait of interest, association mapping does not need prior molecular mapping information and serves as an important tool to identify marker-trait associations on the basis of linkage disequilibrium (LD) only. Association analysis infers significant marker-trait associations by accounting for co-segregated (or co-transmission) alleles at different locations in a genome across a diverse set of mapping population , allows  markers . However. juncea , 9 and BB. napus –12 for vB. napus –17. HoweB. juncea has proceeded with a slower pace mainly due to its narrow genetic base, complex allotetraploid genome and highly repetitive regions [The discovery of SNPs in  regions , 19. The regions . To reduB. juncea for SNPs identification and genotyping. A bioinformatics pipeline was developed using tools available within CLC Genomics workbench for the detection of SNPs  of The microfluidics-based electrophoresis analyses of pooled library revealed that majority of fragments were represented in the range of 300 to 400 bases following size selection during library preparation  using default parameters and about 92–94% reads were mapped to the reference genome. The mapping percentages for individual samples ranged from 92.56 (Zem 1) to 94.35 (Donskaja IV). Out of all the mapped reads, more than 80% reads mapped uniquely to a single locus , days to maturity (DTM), plant height (PH), siliqua length (SL), seeds per siliqua (SPS) and thousand seed weight (TSW). An analysis of chart correlation for various traits indicated that except DTF and DTM, all the traits were normally distributed in the diverse core set  and SL (− 0.43); however the same trait did not show any significant correlation with PH and a positive significant correlation with SL (0.36) and SPS (0.31). PH was significantly correlated with DTF and DTM in Indian genepool but not in European. SL was negatively correlated with both DTF and interestingly with DTM; and positively with TSW in Indian genepool. However, SL had significant but opposite correlation with DTM in European genepool. SPS was not correlated with any of the yield traits in Indian genepool, but was significantly correlated with SL and DTM. In both the genepools, TSW was not significantly correlated with any of the traits but SL in European. The t-test for means for two genepools indicated that the average values for two genepools were significantly different. The  and SL  values, the number of optimal groups was identified as two  of false discovery rate. The p-values for all the associations ranged from 1.26E-05 to 1.15E-18 and the phenotypic variance contribution (r2) ranged from 0.20 to 0.89.The association analysis to identify markers associated with six yield contributing traits was carried out using a set of 61 SNPs uniformly distributed across the all the chromosomes of Brassica species. SNP markers have been vital for the (fine) mapping of genes of agronomic importance with the goal of implementing marker-assisted breeding of elite crop cultivars. SNPs are distributed far more frequently in a genome and have been used to develop high-density molecular genetic maps and fine mapping of a region of interest. The abundance of SNPs in genome, low mutation rate and high heritability offsets the disadvantage of bi-allelism. SNPs are found randomly distributed throughout the genome in both repetitive and non-repetitive regions, however those present in the genic/non-repetitive regions are of keen importance. The presence of orthologous regions among the progenitors of allopolyploid genome adds an extra layer of genome complexity in addition to repetitive elements. However, recent advances in reducing the genome complexity coupled with NGS technologies have been highly successful to develop genome-wide SNPs in crops.A plethora of molecular marker-based studies have led to a greater understanding of the genetic make-up of MseI and SacI) was used for ddRAD sequencing of unique regions of B. juncea. The similar technique of genome complexity reduction has also been employed in several crops [EcoRI and BfaI restriction enzymes were used with a size selection between 450 and 600 bp [EcoRI and MspI with size selection around 200–400 bp [In the current study, a pair of restriction enzyme digestion  of SNPs in the coding regions of exchange , 33 or dexchange . The SNPexchange , 36.B. juncea genotypes into two major clusters- Indian and Exotic (European) genepool. The diversity and clustering results are in agreement with the previous studies based on SSR and other marker system. The SNP-based diversity analysis also concluded that a small subset of uniformly distributed SNPs would be highly useful for various genetic analyses.The high proportion (97%) of functional SNPs across a set of highly diverse genotypes indicated the accuracy of ddRAD technology to invariably target same locus across different individuals during the library preparation and partly due to the improved bioinformatics tools for sequence mapping and SNPs identification for complex and polyploidy crops. The SNPs identified through RAD-seq and its modifications in the previous studies have shown similar functionality levels in other crops as well , 38. TheThe morphological characterization of six traits revealed very interesting patterns on correlation matrix. The bimodal distribution for DTF and DTM upon combined analysis of all the genotypes indicated that these two traits are controlled by different set of genes in Indian and European genepools. The European genepool has traditionally been domesticated under low-temperature short-day conditions while the Indian genepool is more conducive for sowing in moderate to low temperature conditions found mostly in the north-western plains of Indian subcontinent. The hypothesis of different set of genes controlling DTF and DTM in Indian and European genepools got further strengthened upon getting a unimodal distribution for DTF and DTM in correlation matrices individually for Indian and European genepools. However, the detailed interaction between the genotype and phenotype could be studied by undertaking QTL analysis and other genetic analyses.In the present study, a common subset of 61 SNPs was used for diversity, population structure and association analyses. For diversity and population structure analyses, the subset of SNPs was able to group 80 genotypes into two distinct clusters, each over-represented by genotypes either from Indian and European (exotic) genepools; which indicated the usefulness of strategy involving the usage of sparse but uniformly localised SNPs for various genetic analyses.B. juncea was used to identify significant marker-trait associations. The association analysis using SNP subset was able to localize genes for various agro-morphological trait on different chromosomes, identifying genome regions for undertaking fine mapping of traits/genomic regions with large number of molecular markers. A majority of SNPs identified associations with multiple traits thus essentially indicating either the clustering of genes for multiple traits or involvement of same set of genes regulating multiple traits in the same genomic regions. Among these traits, DTF and DTM had invariably common SNP/genomic region associated with them, thus implying that the genes for these two traits are clustered together and/or likely have correlated/coordinated expression of genes. A recent study, using F2 mapping population, in Brassica napus has also identified the co-localisation of QTLs (and eQTLs) for flowering time and various growth-related morphological traits to a common genomic region of chromosome A10 [Brassica napus [Brassica rapa [A subset of SNPs representing all chromosomal regions of some A10 . In anotca napus . Such clica rapa . High coica rapa .B. juncea [The presence of a common ancestral genome between three polyploidy species led to the identification and comparison of association analysis results. In the current study, the associations for DTF were mapped to A- and B- genome chromosomes. Similarly genes for flowering time have been identified on both A- and B- subgenomes of . juncea . Two hig. juncea . The assBrassica juncea. The SNPs were developed initially from sequence comparison of six genotypes only; however the SNPs were found to be functional when tested on a diverse set of genotypes. The SNPs used for association analysis were also found to be significantly associated with six morphological traits. Given the fact that Brassica juncea has narrow genetic base, the SNPs identified in the current study would form an excellent source for various genetic studies including linkage mapping, fine mapping and association analysis.This is the first report of use of ddRAD-seq for the development of SNPs in B. juncea genotypes (three each from Indian and Exotic germplasm) were selected for use in ddRAD library preparation. Pusa Tarak (BJI-1), Urvashi (BJI-2) and RSPR-01 (BJI-3) were selected from Indian genepool and Zem 1 (BJE-1), Donskaja IV (BJE-2) and EC287711 (BJE-3) were selected from European (exotic) genepool. Seeds were procured from  University of Delhi (South Campus), India and National Bureau of Plant Genetic Resources, New Delhi, India. SNP genotyping was performed on 80 diverse B. juncea genotypes that were procured from Plant Gene Resources, Agriculture and Agri-Food, Canada and Genetics & Plant Breeding Department, SKUAST-Jammu, India , days to maturity (DTM- number of days from sowing to the date when pods on 75% of the plants in each plot were turned browned), plant height (PH- in meters), siliqua length (SL- in centimeters), seeds per siliqua (SPS- average number of the seeds present in single pod/siliqua) and thousand seed weight (TSW- weight in grams of the 1000 seeds collected in random). The traits value of each genotype was defined as an average of two replicates in the same location. The correlation coefficients between traits were determined using Student’s t-test and the variance components were also calculated.The phenotypic data of diverse core set of Brassica juncea genotypes. For ddRAD library preparation, ten microgram of purified DNA was digested to completion with Mse I and Sac I. The digested DNA was separated on 0.8% agarose gel; fragments between 300 and 400 bp were gel excised and eluted. The eluted and purified DNA was then end repaired, short dA-tail was attached and ligated with the adapters following manufacturer protocol. The ligated DNA was amplified using PCR to enrich and add the Illumina specific index and flow cell annealing sequences to the fragmented DNA. For each six genotypes, six different index sequences were used so at to facilitate the process of pooling. All six DNA samples were normalized to a final concentration of 50 ng/μl and pooled to reach a final volume of 300 μl to generate a reduced representation library. The pooled dd-RAD library was then sequenced using Illumina HiSeq 2000 to generate 100 bp paired-end reads.The ddRAD-seq protocol , 46 was Brassica juncea genome was used as a reference genome [The ddRAD-seq reads obtained after sequencing were bioinformatically analyzed using CLC Genomics Software in order to obtain a high quality SNP set. The paired end sequencing reads were subjected to a series of steps  through a pipeline. The following filtering scheme Fig.  was usede genome .Fig. 4WB. juncea. The sequences flanking each SNP were used to synthesize forward, reverse and iPLEX universal extension primer using Agena CXassay design suite V2.0 software. The forward and reverse PCR primers were diluted to the concentration of 100 μM, while iPLEX universal extension primers were diluted to the concentration of 500 μM. The experimental procedure included- (1) multiplex PCR using forward and reverse primer, (2) SAP  clean up reaction, (3) iPLEX extended reaction with the amplified product, (4) resin cleanup reaction to remove salts, (5) spotting of primer extended product on spectro-chip and (6) spectro-chip detection using MALDI-TOF mass spectrometry. The genotype calls were evaluated through MassARRAY TYPER 4.0 software.A subset of 61 SNP loci was selected with 3–4 SNPs from each chromosome and was validated across the diverse set of qK) were estimated with 10,000 burn-ins followed by 100,000 iterations. For structure analysis, the diverse population was assumed to be following an admixture model and correlated allele frequencies with no prior population information. The structure analysis was performed with 5 replicates for each K ranging from 1 to 5. The ΔK was calculated using Structure Harvester software [K. The membership coefficient with a threshold of 70% for each replicate of structure analysis was used to generate a Q matrix using the software CLUMPP [The SNP genotyping data were used for population structure and genetic diversity analyses  followinsoftware  to obtaie CLUMPP ; followee CLUMPP . The pole CLUMPP . The unwe CLUMPP  for consB. juncea database using BLASTX  to identify the corresponding sequences in the protein database [Flanking sequence of SNPs/ hyper-variable regions were compared against the database .Brassica genotypes and population structure data (Q matrix) by using TASSEL software [P < 0.01.Association analysis was performed by using the genotypic (SNPs) and phenotypic data of the diverse software . Marker–Additional file 1: Figure S1. Workflow design for SNPs/Variants detection in CLC Genomics Workbench . Figure S2. Bioanalyser analysis of the prepared library . Table S3. Summary of ddRAD-sequence data for six genotypes. Table S4. Details of Brassica juncea genotypes used for association analysis.


Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored  Scientific endeavors in human genetics, molecular biology, biochemistry, and bioinformatics have been progressively converging in order to more precisely describe how DNA variation explains differences in traits and diseases. Single base changes called single nucleotide polymorphisms or SNPs, along with changes where DNA has been inserted (e.g. insertions) or deleted (e.g. deletions), which are referred to as indels have been popular forms of genetic variation to investigate. Another form of variation is in terms of copy number variants (CNVs), where large portions of the genome are duplicated or deleted. These variants in our DNA differentiate us in terms of how we define our ancestry, the unique traits we all have, what diseases we inherit or sporadically develop, along with what medicinal therapies may be best suited for us. Therefore, being able to characterize how this portion of the genome influences life is of significant value to those trying to not only understand biology, but also developing tools and methods for improving health.Most of what we know about genetic variation on a genome-wide scale  has accumulated over the past 20 years, starting with the human genome project. As the name suggests, the human genome was sequenced as a part of the human genome project , 2. WhilSince we have two copies of each chromosome, we can be heterozygous or homozygous at any location in the genome, but these differences have significant implications with respect to the resulting phenotype. To identify alterations in haplotypes the HapMap project was started , 8. SincResearchers assumed that the human genome project, HapMap, 1000 Genomes project and other similar studies, would resolve many questions with respect to connecting a SNP to a trait or disease, however this was not the case. Rather, by sequencing the genome, scientists now had what is analogous to a book that contains the history of humans; they understood the alphabet, but weren’t sure how to translate the words into meaning. Even finding the “words”, which one might think of as genes is still an ongoing effort . The bigIn this review, we will describe some of the most commonly used methods along with new methods that innovatively annotate SNPs. Annotation methods can broadly be characterized by whether or not they annotate variants based on location (i.e. non-synonymous or 5′ untranslated region) or if they are more quantitative and intend to make inferences about pathogenicity or deleteriousness of the variant . We willOur understanding of how a protein coding gene is transcribed and translated into a protein along with how it performs its function has significantly influenced how SNPs are annotated. In parallel with those interested in identifying new SNPs, there have been many focused on the fundamental mechanisms by which gene expression is regulated and the eventual effect on the trait or disease. The cross pollination between these fields of study has led to an appreciation for characterizing how SNPs influence gene expression, and thus an interest in utilizing what is known on a molecular level to annotate SNPs . MoreoveAnnotating SNPs that cause a change in the amino acid sequence (e.g. non-synonymous variants) is critical to helping us understand the relationship that SNPs have with traits and diseases. Non-synonymous variants can be in the form of single amino acid changes, frameshifts (i.e. multiple base pairs added or subtracted from the gene), stop loss, and stop gained (e.g. non-sense) variants. These types of changes can impact enzymatic activity, protein interactions with other molecules, and interactions within the same protein molecule. Additionally, alternative splicing can be affected by these changes as well through non-synonymous changes to splice sites, thus impacting the amino acid and entire exons as well. Most genes have alternative splicing isoforms and this has also led to a subset of annotations that incorporate whether the SNP position is located at a splicing junction . VariantFor over a decade tools have been developed to annotate variants. In that time, a handful have become the most popular. Additionally, they utilize a combination of evolutionary conservation, published resources, expert knowledge, and computational predictions to characterize the importance of the variant on a molecular level, as well as its ability to contribute to a pathogenic phenotype. While some of them require amino acid information as the input, they are still commonly used to annotate non-synonymous variants. Sorting Intolerant From Tolerant (SIFT) uses evolutionary conservation to score whether or not a non-synonymous change could potentially affect the function of a protein and thus impact a phenotype . SIFT pePolyPhen is another annotation tool which primarily uses sequence conservation, however it includes information pertaining to 3-D structure and contacts with other residues as well . While Pp-values for each variant in addition to a score so that they can be aggregated for gene-level information [The use of machine learning (ML) has been a growing area of interest in the genetics community, and more specifically for those interested in characterizing SNP function and potential pathogenicity –39. ML iormation . Many oformation .Myvariant.info [Another category for SNP annotation strategies encompasses “meta-predictors”, which utilize multiple methods to generate a prediction of potential pathogenicity. REVEL (rare exome variant ensemble learner) was able to better characterize rare missense variants as neutral or potentially pathogenic compared to CADD by using random forest . While iant.info   index. It is calculated using the equation RSCU = SNc/Na, where S is the number of synonymous codons for an amino acid, Nc is the frequency of the codon within the genome and Na is frequency which the amino acid that Nc in the genome , 75. CalGNB5 which can cause autosomal-recessive multisystem syndrome with sinus bradycardia and cognitive disability [In addition to codon bias that affects translation, annotating SNPs that affect splicing, miRNA binding sites, and mRNA structure have also proved useful when dissecting synonymous variants. Splicing is one of the most common mechanisms by which annotations have been designed to characterize synonymous codons. These tools are generally interested in the effect on either 5′ or 3′ splice sites or splicing regulatory elements. Many of the tools used to investigate the effects of splicing on synonymous codons have been reviewed, however a number of new methods have recently been published. UMD-predictor is able to annotate cDNA substitutions by incorporating protein domain information, conservation across species, allele frequency in the human populations, and potential impact on splicing . Althougsability . On the sability . MoreoveCodon bias can also impact mRNA secondary structure, which in turn can alter mRNA stability and protein binding, thus impacting gene expression. The mechanisms by which mRNA secondary structure can influence gene expression is mediated through a variety of mechanisms including transcription, splicing, stability, translation, miRNA-mRNA interactions, mRNA localization, and protein expression . GeneticA predominant theme among those investigating the effects synonymous variants have on genes is by measuring or predicting the changes specifically to translation, protein structure, and folding. The rate of translation is important for protein fidelity and folding. There are multiple mechanisms and processes that affect translation including mRNA structure, mRNA stability, and sequence motifs, many of which have been proposed to be effected by codon bias . While sThe human genome project provoked many questions regarding the function of the non-coding regions of the genome and how gene expression was being regulated. One of the most important discoveries was that most of the genome did not code for proteins. Furthermore, genome-wide association studies (GWAS) have found that most disease associated SNPs are not within genes, suggesting the non-coding regions have medical relevance . HoweverThe Encyclopedia of DNA elements or ENCODE, was first launched in 2003 as a large group of scientists from multiple institutions that would generate new experimental and computational methods to characterize and annotate beyond the coding regions of the genome . Within Although ENCODE helped resolve the biochemical mechanisms by which genes are regulated on a genome-wide scale, it remained unclear how genetic variability affected gene expression. In other words, how do SNPs in non-coding regions impact RNA levels? And if a SNP does impact gene expression, what is the tissue context (i.e. brain)? To address this question, and others, the Gene-Tissue Expression consortia (GTEx) was started in 2013 to sample tissues from hundreds of individuals across many tissue types . Some ofEven though ENCODE did not investigate genetic variation, the transcription factor and chromatin annotations have been invaluable to those interested in annotating SNPs in non-coding regions. One of the reasons ENCODE was successful was that researchers hypothesized that regions of the genome that do not code for genes, but regulate the expression of genes, are likely to harbor disease causing mutations that affect protein-DNA interactions. Experimental methods such as ChIP-seq, DNase-seq, and bioinformatics methods that searched for protein binding motifs to detect these interactions throughout the genome were employed in ENCODE . The hyphttps://www.ebi.ac.uk/gwas/). Furthermore, it performs a statistical test that can provide insights as to what cell type the SNPs are likely to be most relevant. HaploReg has successfully characterized SNPs associated with cardiovascular disease, autoimmune disorders, cancer, diabetes, and neurological disorders [pubmed.gov).HaploReg is a tool that annotates variants with respect to ENCODE data either through its online interface, or by downloading the annotations that the authors collected . HaploReisorders , 106. Reisorders . For insisorders , 108, Alisorders , and Schisorders , just toACP2 as playing a novel role in human osteoblast development, even though it was not the nearest gene nor in the same LD block as the GWAS SNP [Another category of methods that can be used characterize non-coding variation is through enrichment analysis. These methods are often attempting to investigate the role of distant regions of the genome coming together in 3D space. These methods investigate chromatin architecture either through the use of ChIP-seq data on proteins that regulate chromatin looping or chromatin capture results like those from Hi-C . The tooGWAS SNP . Most reGWAS SNP . BEHEST GWAS SNP . While iA weakness of ENCODE, NIH Roadmap, and some of the Hi-C data discussed above was that most of the samples came from cell lines of the same individuals, which meant it would be very difficult to understand how SNPs had an effect on gene expression from those samples alone. To overcome this obstacle, the gene-tissue expression or GTEx consortium was established in 2010 to investigate how gene expression varies across individuals without disease . GTEx enUnlike many of the other methods discussed in this review that focused on individual SNPs in a trait-agnostic manner, the methods in the following section require GWAS data . While GWAS has been fruitful in finding associations between common variants and phenotypes, these SNPs often fall well outside of coding regions making it difficult to identify which genes these SNPs regulate . Therefocis-eQTLs and 673 trans-eQTLs, but more importantly it alluded to the difficulties in detecting SNPs that are most influential with respect to expression of a specific gene [The primary annotation that GTEx provides are eQTLs. A recent release from GTEx identified 24,886 fic gene . For insfic gene . Since ip-values and their minor allele frequencies or SNP effect estimates and standard errors) [Colocalization methods test for the co-occurrence of signals in both eQTL and GWAS datasets. Two of the earliest examples of that investigated colocalization utilized lymphoblastoid cells lines (LCL) from the HapMap project , 118. Th errors) . A conce errors)  was deve errors) . A more  errors)  can perf errors) .cis- gene expression from a small set of genotyped individuals into a much larger set of phenotyped individuals using their SNP information. TWAS has been effective at identifying new genes associated with complex traits that were unlikely to be detected through their proximity to the causal SNPs [Transcriptome-wide association methods evaluate the association between a SNP and a trait through measured or predicted expression . These msal SNPs . Howeversal SNPs ; approacsal SNPs , 128. Whsal SNPs . It thersal SNPs .http://gusevlab.org/projects/fusion/).Recently, the authors of PrediXcan developed S-PrediXcan, which is a version of PrediXcan that can use summary-level information . They decross-tissue expression imputation , then tests for associations between trait and imputed expression in each tissue, and finally uses a statistical test to combine all the single-tissue gene-trait associations. A conceptually similar approach called MultiXcan was developed by Barbeira et al. [independently from each of multiple tissues . Most recently, the fine-mapping approach, FOCUS , was released as a way to perform statistical fine-mapping over gene-trait association signals from TWAS [The current TWAS approaches train separate imputation models for each tissue, which means information across tissues can be lost and the burden of multiple tests reduces statistical power to identify significant associations. A new method, UTMOST (unified test for molecular signatures), combines multiple single-tissue associations from SNP and GTEx eQTL data to increase accuracy and quantity of identified gene-trait associations . This mea et al.  that userom TWAS . Finallyrom TWAS .Many contemporary methods for annotating non-coding regions have employed data from multiple sources, including but not limited to ENCODE and GTEx. A number of tools have been developed to identify where SNPs reside with respect to non-coding features but one of the most popular is ANNOVAR, which is both fast, flexible, and can even annotate coding regions as well . Two metWhile analysis of common variants that impact expression using TWAS and similar methods Table  have ideWhile GTEx, ENCODE, and other resources have well characterized genetic variants, there are still many avenues of annotating non-coding regions that have not been thoroughly investigated. As previously mentioned, the biggest weakness of ENCODE was the lack of genetic variability between samples. And while the strength of GTEx was that genetic variance could be analyzed, it only generated resources for genetic and expression data. In order to develop a resource that has DNA accessibility, epigenetic marks, expression, post-transcriptional information, and proteomic data across multiple individuals and tissue types the GTEx consortium has proposed eGTEx (Enhancing GTEx), the next phase in generating data for GTEx . In addiWhile bulk sequencing takes a sample or tissue and measures genetic, transcriptomic, and other forms of molecular variation on average across cell types, single-cell sequencing has enabled scientists to measure cell-type specific effects. Single-cell sequencing has been incredibly useful for those dissecting heterogeneity in cancer, as reviewed previously . HoweverDeep learning is a sub-category within machine-learning that has recently begun to show promise in many biomedical fields, including that of annotating SNPs. For instance, Xiong et al. were able to use deep learning algorithms to generate scores that predict how much a variant will impact splicing . To testhttps://genomeinterpretation.org/content/5-challenge).A rapidly evolving area of research that can be applied to SNP annotations are genetic screens. Genetic screens often test hundreds to thousands of genetic variants for some functional effect in a controlled manner . This prIn summary, our understanding of how genetic variation influences traits and diseases has rapidly evolved in recent years. A number of large studies that have pooled resources together have generated sequencing and phenotypic data. These studies have highlighted how we can traverse between characterizing how SNPs and phenotypes are related. SNP classification systems often differ depending on whether SNPs are non-synonymous, synonymous, or non-coding. Additionally, interpretation of the annotation results can vary not only because of the methodology, but also based on whether the results are used for research or clinical purposes. One of the biggest obstacles for the field for those doing more basic research is to find the sources of omics data that are most important and incorporating these into new methods, while those performing more applied or clinical studies need to more accurately identify SNPs most closely associated with disease in order to move them into clinical or pharmacogenomic use. In order to accomplish this goal, it will be important for researchers to incorporate more ethnically diverse annotations so that potential therapies are not limited to individuals of European ancestry . Finally


GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes ( The pig, which has abundant phenotypes, is a model animal that not only intimately resembles man in physiology, anatomy, and genetic architecture but alsoTo dissect the molecular basis of the divergent phenotypes of LMA and LMD among different individuals, researchers have selected various populations–14 usinghttp://www.animalgenome.org/cgi-bin/QTLdb/SS/index, Jul 24, 2018). Among them, 330 and 41 QTLs are associated with LMA and LMD, respectively. These discoveries have provided a certain number of molecular markers to the breeding for swine LMA and LMD. For instance, Edwards et al.[Sus scrofa chromosome (SSC) 11 that explains approximately 6% of the phenotypic variance of LMD. Although many QTLs have been detected, these findings are still deficient due to the inferior power of QTL mapping and poor resolution in the majority of QTLs[To date, 27,878 QTLs associated with 718 traits have been reported in the pig QTL database . The American and Canadian original populations comprised 3916 and 2127 individuals, respectively. Moreover, a meta-analysis combining the full GWAS results from the two populations was conducted and uncovered additional significant SNPs. This study aimed to identify genomic regions and plausible candidate genes that lead to the phenotypic diversity of LMA and LMD. This study can serve as a basis to elucidate the molecular architecture underlying muscle development in pigs, humans, and other mammals.All procedures involving animals in this study met the guidelines for the care and use of experimental animals established by the Ministry of Agriculture of China. The ethics committee of South China Agriculture University (SCAU)  approved this study .In this study, experimental animals, including American and Canadian origin Duroc populations, were raised in the Wens Foodstuff Group Co., Ltd.  following the same feeding standards. Briefly, a total of 6043 pigs of these two Duroc populations were used in the present study, including 3916 American origin pigs and 2127 Canadian origin pigs. The pigs of the American original population were born between 2013 and 2017, and the other was born between 2015 and 2017. All pigs of the two populations sustained uniform feeding conditions and fine fodder and consistent management during the fattening period from 30 to 100 kg live weight to minimize the impact of non-genetic factors. Phenotypic records included LMA and LMD. For American and Canadian populations, phenotypes of LMA and LMD were collected by practiced investigators from the 10th-rib to 11th-rib of pigs at the weight of 100 ± 5 kg by an Aloka 500V SSD B ultrasound , which employed diagnostic ultrasound system and transducers to acquire high resolution images, and computer software was used to ascertain the LMA[P > 10−6 for Hardy–Weinberg equilibrium test. SNPs with no position information and located on the sex chromosomes were also removed. Notably, the two populations follow the same quality control criteria. After QC, a final set of 38,790 and 35,845 informative SNPs and 3916 and 2127 individuals were retained for single-trait GWAS in the American and Canadian original Duroc populations, respectively. Furthermore, in the meta-analysis of GWAS, a common set of 40,030 differently eligible SNPs for LMA and LMD across the populations were later used.Genomic DNA was extracted from ear tissue following the traditional phenol/chloroform method. The quality and quantity of the DNA samples were measured with a NanoDropTM 2000  as described by Wang et al.. In brieIn consideration of the different genetic contexts of the two Duroc populations, principal component analysis (PCA) was performed to detect the possible population stratification using GCTA tool. We init−1K); and ε is the vector of random residuals, with ε ~ MVNn. τ−1 is the variance of the residual errors; λ refers to the ratio between the two variance components; K is a known n × n relatedness matrix calculated in previous step; and I refers to the identity matrix. MVNn denotes the n-dimensional multivariate normal distribution. Bonferroni correction was applied to determine the genome-wide and chromosome-wide significance thresholds, which were defined as 0.05/N and 1/N, respectively. N is the number of filtered SNPs for each trait of American and Canadian original Duroc populations.The entire processes in the single-trait GWAS were performed using the software of genome-wide efficient mixed-model Analysis (GEMMA) and genoA meta-analysis was performed across the different genetic contexts of Duroc pig populations for a trait on the strength of single-trait analysis using METAL software. METAL cThe GCTA software was used to estimate the variance explained by genome-wide SNPs (SNP-based heritability) for each trait in the two populations via the restricted maximum likelihood approach, relying on the genomic relationship matrix estimated from all the autosomal SNPs. To partition the phenotypic variance onto each of the autosomes, we also appointed a model to investigate the contributions by each chromosome as described by Yang et al.. FurtherMany SNPs, which were located on several narrow regions, were associated with LMA and LMD in the Canadian original Duroc population, and they were detected both by single-trait and meta-analysis GWAS. To further verify the linkage disequilibrium among these SNPs and detect candidate regions connected with the two traits, we employed PLINK v1.07 and Haplhttp://asia.ensembl.org/Sus_scrofa/Info/Index). Gene sets with the criteria of including or nearest the significant SNPs were selected to conduct KEGG pathways and Gene Ontology (GO) analysis. Pathway analysis and GO analysis was conducted using the DAVID bioinformatics resource . Fisher’s exact test was used to assess the significance of the enriched terms with P < 0.05 and detect the genes involved in biological processes[http://www.genecards.org/) and the NCBI (https://www.ncbi.nlm.nih.gov/) database were used to query gene functions and determine promising candidates.The functional genes were searched on the strength of Sscrofa 11.1 genome version  (SNP on SSC6: 47.35Mb) and fibroblast growth factor 21 (FGF21) (SNP on SSC6:54.07Mb). They both explained 1.18% of the phenotypic variance of LMA. Three SNPs were associated with LMD at the chromosome-wide significance level. Of these SNPs, the most significant was ALGA0065784 (SSC12: 26.37Mb) and was located close to gene ENSSSCG00000034682, and it explained 1.20% of the phenotypic variance of LMD.Five SNPs were significantly associated with LMA and LMD in the American original Duroc population, of which two common SNPs were associated with LMA and LMD at different significance levels Tables  and 3. TP < 2.79E-05), and five SNPs associated LMD reached the genome-wide significance level (P < 1.39E-06). The most significant SNP, ALGA0040260, was located at 30.34Mb and was near the gene Nudix hydrolase 3 (NUDT3) on SSC7. This SNP explained 1.77% of the phenotypic variance of LMA. As for LMD, the peak SNP in this region, ALGA0040263, was located at 30.35 Mb within the NUDT3 gene on SSC7 and contributed 2.48% of the phenotypic variance of LMD. The remaining 13 SNPs reached the chromosome-wide significance level, of which 10 were associated with LMA  and three with LMD . The most significant SNP of the 10 SNPs, WU_10.2_16_35829257 contributed 1.83% of the phenotypic variance of LMA. This SNP was not located within any gene; nonetheless, it was in close proximity to gene sorting nexin 18 (SNX18). In addition, the remaining SNPs, which were close to each other on SSC16 between 33.46 Mb and 34.37 Mb, were located within or near several genes, such as ADP ribosylation factor like GTPase 15 (ARL15), glutathione peroxidase 8 (putative) (GPX8), and multiciliate differentiation and DNA synthesis associated cell cycle protein (MCIDAS), and a not openly reported gene (ENSSSCG00000016898)  .P < 1.25E-06). In addition, 37 SNPs were associated with LMD, and 15 of these 37 SNPs reached the genome-wide significance level (P < 1.25E-06)  . Among t.25E-06) . AlthougAs mentioned above, among the 19 significant SNPs in the Canadian original population, we found eight significant SNPs on SSC16 in close proximity to be associated with LMA and detected a haplotype block that spanned 709 kb  containiThe phenotypic variance explained by significant SNPs, which were detected by single-trait association analysis, and autosomes were estimated for LMA and LMD in the two populations using GCTA tool Tables , Fig 6. The phenotypic variance explained by each chromosome for LMA and LMD is shown in GCLC), GPX8, death domain associated protein (DAXX), fibroblast growth factor 21 (FGF21), TATA-Box binding protein associated factor 11 (TAF11), SAM pointed domain containing ETS transcription factor (SPDEF), NUDT3, and protein kinase C and casein kinase substrate in neurons 1 (PACSIN1), with biological functions such as increasing available amino acids in skeletal muscle, mammalian growth, fat deposition, and body size of pig were selected as promising candidates for swine carcass traits and growth-related traits.A total of 41 functional genes that were within or nearest the significant SNPs were detected based on the Sus scrofa 11.1 genome assembly Tables . These gScientific and comprehensive determining indices are the basis of measuring the level of production performance and growth-related traits of swine. LMA and LMD are important traits influencing the production performance of commercial pigs. Thus, the genetic mechanisms of LMA and LMD should be elucidated to estimate the affecting factors and consider them for future pig breeding programs. In this study, we performed four single-trait GWAS in two Duroc pig populations containing 6043 individuals in total. To improve the power of detection for common SNPs, we conducted a meta-analysis combining the full GWAS results from the two populations and uncovered additional significant SNPs. To our knowledge, few studies used GWAS strategy to analyze LMA and LMD. The present study is the first GWAS for LMA and LMD focusing on Duroc breeding pigs. Based on a large sample size (n = 6043) and multiple statistical analysis strategies, a total of 75 SNPs associated with LMA and LMD were detected. Of these SNPs, only one third of the significant SNPs were found to be simultaneously associated with both LMA and LMD, although these two traits were highly positively correlated to each other in American (r = 0.94) and Canadian populations (r = 0.74). The partial overlap of significant SNPs between LMA and LMD may be because the muscle growth is not only depended on the muscle fiber density, but also the fiber diameter, 47. It Given their powerful detection capability, GWAS are widely adopted for studying the genetics of natural variation. However, the most basic problem of GWAS is the risk of false positive in structured populations that may cause spurious associations, 51. In GCLC and GPX8 are highlighted as important candidate genes that play roles in the pathway of glutathione metabolism. GCLC, the first rate-limiting enzyme of glutathione synthesis, is related to the biological processes of protein heterodimerization activity and coenzyme binding. Wei et al.[GCLC participates in amino acid metabolism and might upregulate the expression of glutathione in porcine skeletal muscle via feeding pigs with a linseed-enriched diet. GPX8 functions in glutathione metabolism and thyroid hormone synthesis pathways. Damon et al.[GPX8 is highly expressed in the longissimus muscle of Large White pigs with the function of response to oxidative stress and oxidation reduction. It was also identified by genome-wide linkage analysis as a candidate gene for growth and carcass merit in an F2 pig population because expression QTL (eQTL) analysis of loin muscle tissue demonstrated that GPX8 eQTL is part of a network relevant to cell cycle and lipid metabolism[DAXX, was related to LMD trait. DAXX participates in the MAPK signaling pathway and is also involved in the regulation of transcription and DNA templated. The DAXX gene encodes a multifunctional protein and interacts with various proteins, such as apoptosis antigen Fas. Yang et al.[DAXX, as a novel signaling protein, plays a critical role in Fas-binding domain, and the elevated expression of DAXX cements Fas-mediated apoptosis.Basing from the results of KEGG pathway and GO analysis, we obtained a number of functional genes and performed further gene annotations. Eight genes related to LMA and LMD were selected as promising candidates, of which, two were relevant to LMA, one was relevant to LMD, and five were involved in both traits. For LMA, ei et al. found thon et al. discoveretabolism. For LMDng et al. found thFGF21, TAF11, SPDEF, NUDT3, and PACSIN1. The FGF21 gene encodes a member of the fibroblast growth factor (FGF) family, which plays a critical role in lipid metabolism[FGF21 is relevant to adipose and muscle tissue development, and functions in lipid metabolism in purebred Iberian pigs and in cellular and muscle growth in Duroc-crossbred Iberian pigs. FGF21 also induces PGG-1α and regulates carbohydrate and fatty acid metabolism[TAF11 participates in RNA polymerase II transcription initiation and promoter clearance pathway and is related to the regulation of transcription and DNA templated. Robinson et al.[NUDT3 of the Nudix protein family and participates in insulin signaling and inositol phosphate metabolism pathway. Many studies proved that NUDT3 is the obesity-linked gene for human[SPDEF plays a role in DNA binding transcription factor activity, transcription, and DNA templated. Meanwhile, PACSIN1 is involved in phospholipid binding and cytoskeletal protein binding. Baranski et al.[SPDEF and PACSIN1 are associated with adiposity and the amount of fat/triglyceride in tissue-specific knocked down Drosophila. NUDT3, SPDEF, and PACSIN1 have been identified in a closely linked region that is associated with obesity or carcass traits, such as body growth and size, in human or pigs[For LMA and LMD, many common significant SNPs that are involved in both traits were found to be close to or within numerous genes. Consequently, five genes were determined as major functional candidates: etabolism, cell gretabolism. Ayuso eetabolism found thetabolism. TAF11 pon et al. showed tfor human, Drosophfor human, possiblfor human. SPDEF pki et al. found thn or pigs, 66, 67.In the present study, we performed four single-trait GWAS in American and Canadian original Duroc pig populations using the same linear mixed model and carried out a meta-analysis to improve the detection power. Comprehensive use of multiple methods improves the effectiveness of detection, but no common significant SNPs were identified between the two populations. For this case, some issues are worth discussing. First, significant SNPs detected by single-trait association analysis were significantly less than those detected by meta-analysis. The results suggest that the number of samples used in the experiment needs to be improved, although it far exceeds the number in a vast majority of previous studies; population specificity also possibly contributes to the occurrence of this event, 40. SomGCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1 with biological functions such as increasing available amino acids in skeletal muscle, mammalian growth, fat deposition, and body size of pig were selected as promising candidates for swine carcass and growth-related traits. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. These findings revealed the complexity of the genetic mechanism that forms the phenotypic diversity and provide essential insights into the future production of pigs in the context of marker-assisted selection.In sum, we detected a set of significant SNPs for LMA and LMD in 6043 Duroc pigs from two populations with different genetic contexts. One pleiotropic QTL located on SSC7 with a 283 kb interval was identified to affect multiple traits. Another genetic region on SSC16 (709 kb) was also discovered to play an important role in LMA. A series of bioinformatics analysis strategies revealed many functional genes for LMA and LMD. For instance, S1 FigQ–Q plots show the observed versus expected negative log10 P-values. The left panel of the figure shows Q–Q plots for LMA in (A) the American original population and (B) the Canadian original population, respectively. The right panel of the figure shows Q–Q plots for LMD in (C) the American original population and (D) the Canadian original population, respectively.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species, Elizabethkingia bruuniana and E. meningoseptica, were phenotypically tested to determine minimum inhibitory concentrations (MICs) for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies (ONT) and subsequent de novo assembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. bruuniana and 5,203 features in E. meningoseptica, where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75, and 0.54, respectively. By producing two sets of quality biological predictors, pan-genome genes and core-genome SNPs, from long-read sequence data and applying an ensemble of ML techniques, our results demonstrated that accurate phenotypic inference, at multiple AMR resolutions, can be achieved.The  Emerging antimicrobial resistance (AMR) is a global crisis. A recent report has predicted that by 2050 antimicrobial resistance will lead to 10 million deaths annually and cost the world’s economy upward of $100 trillion . CurrentStaphylococcus aureus isolates were predicted from whole-genome sequencing (WGS) annotation results which achieved an overall sensitivity and specificity of 97 and 99%, respectively (Neisseria gonorrhoeae isolates achieved an acceptable major error rate (93% accuracy within one MIC doubling dilution and 98% for two) by regressing MIC phenotypes on genetic mutations of known AMR genes  has led the way in cutting-edge prediction accuracy for vision tasks , time-seElizabethkingia demonstrates resistance to β-lactams and related antimicrobials due to the presence of multiple chromosomally-located β-lactamases  at 37°C under constant agitation. These cultures were then used to extract genomic DNA utilizing QIAGEN Genomic-tips DNA purification kits  according to the manufacturer’s protocol.Single colony isolates of Bacterial isolates were grown and maintained as described previously . MinimumElizabethkingia isolate following the procedures outlined for the SQK-LSK208 2D sequencing kit (Oxford Nanopore Technologies (ONT), United Kingdom) with the following protocol adjustments. A total of 1.5 μg of gDNA was sheared in g-tubes (Covaris) at 4200 RPM for a targeted fragment size of 20 kb. End-repair was performed following the manufacturer’s recommended protocol for Ultra II End-prep enzyme mix (NEB). Adapter ligation reaction incubations were increased to 15 min. All bead clean-ups used 0.4x AMPureXP beads  for additional size selection and elutions were performed at 37°C for 20 min. DNA concentration of the library was quantified using Quant-IT PicoGreen® dsDNA Assay Kit (Thermo Fisher Scientific), measured on Synergy H1, hybrid multi-mode microplate reader (BioTek). Final DNA library yields were above the recommended 200 ng.DNA libraries were prepared separately for each Two R9.4 flow cells were prepared for two corresponding MinIONs, each connected to a separate Windows PC using a USB 3.0 connection. MinKNOW GUI application 1.0.8.0 from ONT was used to validate the MinION connection and to monitor basic hardware details, like the number of active pores within each flow cell during sequencing runs. Pore count validation was completed beforehand, with the Platform QC command in MinKNOW. Flow cell priming was done according to the protocols provided by ONT for MinION use.Elizabethkingia DNA library were mixed to produce one loading library. The loading library mixture was carefully prepared for each species separately, to prevent fragmentation. The R9.4 flow cells received 75 μL of loading library via the SpotON port.In a microfuge tube, 37.5 μL of running buffer (RBF), 25.5 μL of library loading beads (LLB) and 12 μL of E. bruuniana was reloaded at the 24-h mark. E. meningoseptica KC1913 was only sequenced for 20 h, after which the flow cell provided no further sequencing capacity due to the depletion of nanopores.The sequencing runs were administered through the MinKNOW application, where each separate run was digitally labeled and the NC_48Hr_Sequencing_Run_FLO-MIN105_ SQK-LSK208.py option was used for 2D R9.4 chemistries. After running the sequencing script, the flow cells were allowed to sequence for 48 h, during which The sequencing output from MinKNOW exists as the ONT FAST5 format, and Albacore 1.3.25 (ONT) was used to base-call the sequencing data. This transcribes the signal-level data into FASTQ sequences embedded within FAST5 reads. Extraction of the FASTQ data was completed using poretools version 0.6.0 . ONT chaPer-read quality filtering consisted of a multi-step procedure to maximize read length and read quality for assembly. The reads, in FASTQ format, were subjected to a quality filter pass with a minimum Phred score of 12 using PRINSEQ  with theDe novo assembly of each organism’s reads was completed with Canu v1.5 , contained the Elizabethkingia species E. bruuniana, E. meningoseptica, E. miricola, E. occulta, and E. anophelis for use in core-genome construction.The vast extent of sequence data and available annotation information provide exciting opportunities for advances in biomedical sciences. To benefit from biological data available on cloud services and to enhance downstream analyses, the assemblies of 19 other strains of Elizabethkingia strain assemblies were also acquired from the NCBI along with matching MIC results for vancomycin, clindamycin, fusidic acid, ciprofloxacin, and rifampin , an “AMR group” was formed containing strains that had corresponding MICs for the corresponding antibiotic. This generates five groups, each with a different number of individuals . SeparatElizabethkingia genus .SNPs were called from within Mauve, using default settings for each alignment group. This did not include insertions or deletions. These SNP positions matched the regions listed within the “backbone” file that was produced prior to SNP calling. The resulting Mauve SNPs output is a tabular format file containing a row for each SNP site and a column for each individual in the alignment.X matrix was constructed by directly loading the data from the Mauve SNPs file. Genotypic information from SNPs was represented as the encoded additive value for all polymorphisms on that site. The smallest value (starting at zero) represented the major allele. For the minor alleles, the encoded value increases as the frequency of the allele at that polymorphic site decreases. Effectively, the first minor allele will be encoded as 1, and in the case of multi-allelic polymorphisms, the next most frequency allele will be encoded as 2. The matrix was then transposed to adhere to the traditional structure of an input X matrix, in which all rows represent individuals and columns contain additively encoded SNPs.To prepare the input data for downstream predictive algorithms, a predictor X predictor matrix was constructed using a presence/absence value. Each column represents the presence or absence of one putative pan-gene with a unique UniProt ID. A value of 1 is given for strains that contain the gene, while a value of 0 is given when the gene is absent. We consider this gene-centric approach appropriate in cases when genes, and not SNPs, are the true source of AMR, as seen in the relevant Tn1546 transposons that carry the vanA gene cluster conferring vancomycin resistant phenotypes  was generated. A second, separate enotypes  or fusB-sistance .X matrix.Each set of predictors (pan-genes and core-SNPs) was used in evaluating AMR prediction. A final hybrid method was also evaluated by appending both matrices (SNPs and genes) together to form a combined, third predictor Elizabethkingia genus have no standardized breakpoints so other species breakpoints are used for reference. Therefore, MIC values denoting resistance/susceptibility to the antibiotics vancomycin and clindamycin were based on the CLSI 2018 standards for Enterococcus spp. and Staphylococcus spp., respectively. For vancomycin resistance, any strain with a MIC value less than or equal to four was considered susceptible (CLSI 2018 M100 Enterococcus spp.), with the remaining strains being resistant. For clindamycin, the same protocol was used with a susceptibility label applied to MIC values of 0.5 or less (CLSI 2018 M100 Staphylococcus spp.). Fusidic acid was not considered for binary classification, as nearly every strain exhibited resistance to fusidic acid based on the reference breakpoints for Staphylococcus spp.In order to assess the predictability of AMR with gene/SNP predictors, strains were labeled either “resistant” or “susceptible” to a particular antibiotic. Members of the Y vector, where each row in the vector (yi) is the observed phenotypic value for the corresponding row in the X matrix (Xi). In the Y vector, a value of 0 represents a susceptible strain, while a value of 1 represents a strain that is labeled as resistant, with each respective group being assigned its own Y vector for that AMR category.In each of the two AMR groups, the susceptible or resistant values for all individuals were represented by a Higher resolution AMR prediction is possible by training models to predict a multiclass “resistance level” instead of a binary resistant/susceptible label. This was accomplished by assigning each strain a resistance level based on their MIC score. Resistance levels are therefore represented categorically as a sliding scale of AMR for the purposes of classification. Raw MIC results for each phenotype were collapsed into these resistance levels :k-Nearest Neighbor (k-NN) is a non-parametric algorithm that classifies test samples based on the Euclidean distance with training samples. These samples are represented in “feature space” as vectors, positioned in N-dimensional space, where N is the number of predictors for that AMR category. After populating the feature space with training data, new test data is classified based on the AMR category of the nearest samples (neighbors) in that space, using Euclidean distance as the metric, seen below /2 classifiers. At test time, all the classifiers are tested on the test samples, where each classification is a vote for that particular class. The class with the most votes is determined as the correct category for that sample.C, which acts within the soft margin cost function; this C term controls the tightness of the two margins used to produce the hyperplane. Larger C’s will produce tighter margins, resulting in less misclassified training samples. A smaller C will produce larger margins, allowing for the misclassification of some training samples. Smaller values may help deal with outliers and form a more generalizable hyperplane by trading error penalty for model robustness. In this study, the penalty hyper-parameter C term was tested with the following values: 0.01, 0.1, 1.0, 10, 20, 1000, and 10000, and the hyperplane was optimized as a dual optimization problem using the sklearn library . A second peak of 46 mb of DNA sequenced was observed during reloading at 24 h, followed again by a rapid decrease, converging to nearly zero bases sequenced per hour by 45 h. E. meningoseptica was sequenced for a total of 20 h (due to hardware failure), during which it received no library reloads. The highest hourly yield peaks (30 mb) occurred during hours 1 and 3, and yield rapidly diminished to 3 mb per-hour by hour 20.During 2D sequencing of E. bruuniana and E. meningoseptica has median 2D quality scores of 15.8 and 16.3, respectively  was 4,626,295 bp long, with a mean GC content of 35.9% and an average read coverage of 610x. The assembly of E. meningoseptica (KC1913) was 3,862,237 bp long, with a mean GC content of 36.5% and an average read coverage of 220x. The read coverage for both Elizabethkingia strains are shown in the E. meningoseptica increased slightly to 3,889,109 bp, primarily due to corrections in deletion errors at homopolymer regions. Deletions were the most common per-base assembly error, consisting of a total of 21,321 gap regions for E. meningoseptica. These occurred almost exclusively at homopolymer repeat regions. The majority of gap regions contained only a single deletion . A much smaller quantity of insertion errors (74 regions) that met the reporting criteria were also corrected by the polishing process. All of these reported regions contained only a single-base insertion error. Substitution errors were also minimal; only 1,273 substitutions were corrected within the E. meningoseptica assembly, and the majority of these substitutions  were made up of G to A and C to T corrections.After polishing, the assembly size of E. bruuniana, where deletions were again the largest source of corrected errors , resulting in a slightly larger genome sequence . A total of 279 insertion errors and 990 substitution errors were corrected. Unlike in E. meningoseptica, no particular substitution error dominated the corrections. The assemblies of the two newly assembled Elizabethkingia genomes are available on NCBI with accession IDs SUB4949836 (E. meningoseptica KC1913) and SUB4949835 (E. bruuniana ATCC 33958).Similar polishing results were also achieved with Elizabethkingia genomes have been deposited in a NCBI BioProject .The assemblies of the two newly assembled E. bruuniana ATCC 33958 and 4,887 putative features in E. meningoseptica KC1913. In E. bruuniana we found that RAST had putatively identified 19 ß-lactamases and 18 efflux pumps. In E. meningoseptica there were 14 putatively identified ß-lactamases and 19 efflux pumps.Annotation with RAST discovered 5,114 putative features in E. bruuniana genome, among which 5,426 were identified to be coding regions, 53 identified as tRNA genes and one feature identified as a tmRNA gene. In E. meningoseptica, there were 5,203 features found in total, 5,152 of these features matched coding regions, 50 matched tRNAs, one matched tmRNA and one was identified as a repeat region. Details of Prokka annotation of E. bruuniana genome can be seen in the E. meningoseptica.Annotation with Prokka produced 5,480 features in the Elizabethkingia strains contained a vanW gene, with E. meningoseptica KC1913 additionally containing a vanB gene  and dark red regions (for E. bruuniana) show sites with high frequencies of predicted cytosine methylation regions. In E. bruuniana, methylation-rich CpG-rich regions often appeared in non-core genomic regions of the strain  with gene predictors . Both thk = 1 performed with similar accuracy to the linear SVM, in the binary case, being very sensitive to the neighbor parameter: as shown in k = 1, with subsequent degradation in accuracy. Conversely, greater consistency can be achieved with larger k values. For example, k = 9 and k = 11 reduce the total f1 micro-score compared to k = 1, but have a smaller standard deviation when computing their mean f1 micro-score over many iterations (when k = 1 std. = 0.15 and when k = 11 std. = 0.11 for SNPs).Outside of support vector methods, k-NN with With SNP predictors, accurate binary AMR classification with a decision tree produced comparable results to gene-based SVMs and k-NN and required no hyper-parameter tuning. This method performed the best of all SNP predictor based methods.With genes, ensemble based methods are less accurate than SVMs and neighbor methods in binary classification. Ensemble techniques did not show significant improvements when increasing the number of trees; although, adding more trees improved f1-scores slightly .k = 1) with genes performed roughly the same. Multiclass classification with tree based methods (random forest and decision tree) showed a 0.06 – 0.07 improvement to the f1 micro-score by using SNP variables.In the case of the multiclass classification, the core-SNP approach with a radial SVM performed the best, with an f1-score of 0.71 . k-NN . When using SNP predictors, any k > 3 marked a consistent degradation in algorithm prediction performance. A similar trend was seen in the variation of the f1-scores during cross-validation; k = 3 yielded a lower standard deviation (std. = 0.10) when compared to most other k values.k-NN with SNPs produced an f1 micro-score of 0.92 with The random forest, with either genes or SNPs as predictors, performed competitively with SVM methods and neighbor methods in terms of f1 micro-score. The other ensemble method, AdaBoost, also generated similar performance between SNPs and genes as predictors but is significantly outperformed by random forests. Changing the number of estimators (trees) in either ensemble algorithm did not affect accuracy; although, having less than 20 trees in a random forest negatively impacted performance.Using SNPs, and a large alpha value, Naïve Bayes achieved a 0.93 f1 score for clindamycin AMR prediction . A largeIn the multiclass AMR classification, SVMs (with SNPs) again outperformed all other algorithms with a 0.71 f1 micro-score (for both kernels). Using the other described algorithms results in lower, and generally uniform, f1 scores, except for AdaBoost which underperforms with a score of 0.55 using both SNPs and genes.The fusidic acid group contained a total of 9,851 SNP predictors. With the gene-centric approach, the pan-genome consisted of 4,727 genes used as predictors.Multiclass classification of the fusidic acid group, with four possible AMR categories, showed that gene variables dominated as the best predictor across all tested algorithms with an average f1 score improvement of 0.06. Gene-centric k-NN performed the best, with a mean f1 micro-score of 0.70 and a standard deviation of 0.17 . Like wiWith a total of 29 individuals in the rifampin group, a total of 2,044 core SNPs were generated. The pan-genome consisted of 7,805 unique genes.Multiclass prediction of rifampin resistance levels produced the most successful results among the multiclass tests, with a mean f1 micro-score 0.75 (0.173) with a decision tree and SNPs. Radial kernel-based SVMs and random forests provided similar accuracy , SNP preThe ciprofloxacin group contained 28 individuals. The core-SNPs were 1,931 in total. The total number of pan-genes was 6,975.The results of ciprofloxacin multiclass prediction proved inferior to other phenotypes, despite the uniformity of the training and test set distribution. Although the decision tree with SNPs produced the top f1 micro-score of 0.54 , it alsoThe rapid evolvability of AMR systems and the subsequent surge of extended-spectrum resistance phenotypes  has dramElizabethkingia strains investigated in this study revealed some species-specific trends with regards to MICs and MBCs  and MBCs (1–8 mg/L) were consistently higher than all other species investigated. The E. meningoseptica MICs and MBCs for vancomycin were higher than most other strains investigated. When investigating the genetics underlying AMR, annotation-based discovery can only be effective when genetic annotation for the genotype responsible for the observable AMR is available and when homology exists. A successful case is seen in a recent study (vanA and vanH, genes belonging to the vanA operon, were identified in the extremely vancomycin resistant Enterococcus faecium strain 805447/07 (MIC value of 256 mg/L, vanA operon is a genetic element that provides resistance to vancomycin by facilitating the replacement of a dipeptide in peptidoglycan synthesis, making vancomycin less likely to bind to peptidoglycan precursors and inhibit cell wall synthesis (Susceptibility testing of and MBCs . For insnt study  that conynthesis . Also suynthesis . Such diElizabethkingia strain was annotated as the accessory gene vanW, believed to play a part in vancomycin resistance but with unknown function (vanW plays any role in the defense mechanism of Elizabethkingia, and this gram-negative genus is likely intrinsically resistant to vancomycin due to the outer membrane’s impermeability to large glycopeptides. However, these annotations failed to provide satisfactory support to the diverse MIC values observed in our Elizabethkingia strains (Annotation-based AMR detection can be complicated by the accumulation of mutations at these gene sites, reducing the effectiveness of alignment-based homology searches using a database . Similarfunction . Moreove strains , S2.In the post-NGS era, WGS provides a convenient means to explore genomic variants of entire chromosomes. Raw genomic data is often represented as genome assemblies or sequenced reads and has historically been difficult to extract genotype-phenotype relationships from. Producing genome-wide predictors, like SNPs, has become a conventional approach, but the SNPs generated by NGS techniques can be prone to noise ; this, ifusB-type fusidic acid resistance. In these genic AMR cases, the effect of mutational changes in the core genome might be too insignificant to be reliably explanatory for AMR phenotypes. Algorithmic identification of causative variants continues to be challenging, and expression and knock-out studies should be considered when aiming to expose differentially expressed genes, and related genetic networks responsible for AMR. Further, it must be stressed that genomic data cannot always be a determinant of the particular antibiotic susceptibility of an organism. Other types of informative biological predictors, like protein expression data, must accompany genomic data to fully understand the complex nature of AMR phenotypes.Prediction accuracy is data-dependent and relies on the quality of the predictor variables used as input. Also, some algorithms, like SVMs and k-NN, require careful hyper-parameter tuning to function properly . In thisA common problem seen when analyzing biological data from individuals that are not part of a controlled population is the relative lack of available samples compared to the wealth of genomic data. The curse of dimensionality characterizes this problem as requiring a tremendous amount of sample data to guarantee that each potential combination of SNP/gene predictor exists within the dataset. As shown in Elizabethkingia strains and also produced a more uniform phenotypic distribution which made prediction more feasible.Together with this research, we suggest that this sample size dilemma can be significantly lessened by capitalizing on the wealth of information stored on cloud services. There are currently more than 150,000 prokaryotic genome assemblies available on the NCBI. We used this community-driven “cloud knowledge” to increase our sample population by 57 and 33% for the vancomycin and clindamycin groups, respectively , comparea priori inference. New emerging technologies like shotgun proteomics and single-cell expression profiling, are expected to contribute to the mechanic viewpoint of AMR phenotypes (Decreasing sequencing costs and the subsequent increase in availability of genomic information stored on cloud services like NCBI and the European Nucleotide Archive means that “cloud knowledge” will be an effective means of improving sample sets for AMR prediction, provided proper phenotyping is performed. Practical use of a computational AMR prediction pipeline in a clinical or hospital setting will also depend on a computationally efficient predictor generation method. Also, given the large, diverse collection on the NCBI, constructing a core-genome could only be advantageous for species/strains that share common ancestry; the same approach can be problematic when including unrelated genera, owing to the decrease in conserved genomic regions, reduced numbers of predictors and possible losses of functional homology. Further, multiple-sequence alignment software must be able to align 1000’s of genomes in a time-efficient manner. Recently, alignment-free variant calling has become an effective alternative and does not demonstrate the excessive time-complexity of traditional methods . Our resenotypes , furtherBN and CC contributed to the conception and design of the study. BN and AL organized the database. KW, AL, NT, WJ, HW, and PH conducted experiments and data collection. BN carried out the statistical analysis. BN, AL, and CC wrote the first draft of the manuscript. BN, KW, AL, WJ, PH, JG, and CC wrote sections of the manuscript. All authors contributed to manuscript revision, and read, and approved the submitted version.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.


This review aims to summarize the current knowledge on the effects of n-3 PUFA on inflammation and its impact on CAD in COPD from a clinical perspective.Chronic obstructive pulmonary disease (COPD) is a growing healthcare concern and will represent the third leading cause of death worldwide within the next decade. COPD is the result of a complex interaction between environmental factors, especially cigarette smoking, air pollution, and genetic preconditions, which result in persistent inflammation of the airways. There is growing evidence that the chronic inflammatory state, measurable by increased levels of circulating cytokines, chemokines, and acute phase proteins, may not be confined to the lungs. Cardiovascular disease (CVD) and especially coronary artery disease (CAD) are common comorbidities of COPD, and low-grade systemic inflammation plays a decisive role in its pathogenesis. Omega-3 polyunsaturated fatty acids ( Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide . Data frCOPD is the result of a complex interaction between lifestyle factors, especially cigarette smoking and air pollution, and genetic preconditions, resulting in persistent inflammation of the airways even after smoking cessation, causing sustained peripheral airflow limitation ,6. The ln-3 PUFAs) exert multiple functions in humans and are crucially involved in limiting and resolving inflammatory processes [n-3 PUFA family, and seafood is the richest dietary source of these essential fatty acids, whereas their metabolic precursor, α-linolenic acid (ALA), is contained in substantial amounts only in vegetables [Omega-3 polyunsaturated fatty acids , serum amyloid A, and fibrinogen, as well as white blood cells (WBC), are detected at increased concentrations in the circulation of patients affected by COPD ,26. SimiCAD prevalence rates in COPD patients range from 7% to 34% ,29,30. CThe pathophysiological mechanism linking COPD to its comorbidities is not yet certain, but chronic exposure to low-grade systemic inflammation might play a central role. Inflammation is a key factor in the development and progression of atherosclerotic lesions, and numerous cytokines, mediators, and immune cells have been identified to promote vascular inflammation, cholesterol deposition in the arterial wall, and the formation of atherosclerotic plaques ,36.Most large epidemiologic studies identified C-reactive protein (CRP) as a key player and predictor of CAD ,37, as wHowever, even though persistent inflammation may constitute the pathophysiological link between COPD and CAD, alternative mechanistic approaches ought to be mentioned.Pulmonary emphysema, a phenotype of COPD, is a pathologic condition characterized by abnormal and permanent enlargement of the airspaces distal to the terminal bronchioles that leads to destruction of airspace walls and usually to progressive airflow limitation . StudiesStill, many other potential mechanisms, for instance, physical inactivity secondary to more advanced COPD stages, chronic hypoxia, genetic predisposition, as well as direct endothelial damage caused by cigarette smoking, may help to further explain the frequent association between COPD and CAD .n-3 PUFA exert multiple functions in humans and are crucially involved in limiting and resolving inflammatory processes. A recent American Heart Association science advisory recommends consuming nonfried seafood, especially species higher in long chain n-3 PUFA, one to two times per week for cardiovascular benefits [n-3 PUFA supplementation for influencing the risk of CVD are still ambiguous, underlined by recent findings from randomized controlled trials and meta-analyses, which led to a class III recommendation by the American Heart Association for dietary supplementation with n-3 PUFAs in populations at high risk of CVD [benefits . Howeverk of CVD . This isk of CVD . The hypk of CVD ,55,56.n-3 PUFAs, by means of EPA and DHA, change the balance between n-3 and n-6 PUFA with arachidonic acid (AA) as the major precursor of the latter, favoring the synthesis of anti-inflammatory eicosanoids [n-3 PUFA supplementation to human diet has been shown to result in decreased production of prostaglandin E2 (PGE2), thromboxane A2, Leukotriene B4 (LTB4), 5-hydroxyeicosatetraenoic acid, and leukotriene E4 by inflammatory cells [n-6/n-3 PUFA ratio and in a higher incorporation of n-3 PUFA precursors for the anti-inflammatory lipid mediators in plasma phospholipids. This further induced a significant improvement in the clinical status by improving joint inflammation, as well as reducing inflammatory parameters  [n-6/n-3 PUFA occurred solely through an increase of n-3 PUFA, while n-6 PUFA remained unchanged. In the literature, similar concerns with the n-6/n-3 PUFA ratio were identified and discussed. Depending on how the proportion of n-6 PUFA changes, the proportion of n-3 PUFA could decrease, remain unchanged, or increase and still lead to a lower n-6/n-3 PUFA ratio, although the active biochemical effects of a decrease, no change, or an increase in the proportion of n-3 PUFA are distinctly different. Moreover, the single aspect of an n-3/n-6 PUFA ratio neglects the source of n-3 PUFA, and it is well established that the biological effects of α-linolenic acid (ALA), EPA, and DHA are diverse [n-6 PUFA and lower levels of n-3 PUFA could increase cardiovascular risk. This has not been demonstrated, as both n-6 and n-3 fatty acids are associated with reduced cardiovascular risk in several studies [n-6/n-3 polyunsaturated fatty acids is of no value in modifying cardiovascular disease risk [Increased bioavailability of osanoids . The incry cells . Plenty ry cells . There ary cells . A recenon rate) . However diverse . In term studies . Similarase risk ,63,64.n-3 PUFA also expands on leukocyte recruitment by affecting the expression of adhesion molecules on endothelial cells [n-3 and n-6 PUFA  in smokers by decelerating the FEV1 decline. However, the effect was limited to subjects with a maximum inhalation of 30 cigarettes per day, indicating a dose-dependent limitation. Interestingly, an analysis of the same cohort also showed a decrease in the risk of CAD morbidity and mortality in smokers with increased fish intake (defined as >2 times/week), suggesting a relationship between airflow limitation, CAD, and n-3 PUFA [Investigations on n-3 PUFA . No infon-3 PUFA (assessment of EPA and DHA intake via questionnaire) on 8960 current or former smokers from the “Atherosclerosis Risk in Communities—ARIC” study cohort and studied its relation with COPD [n-3 PUFA. Moreover, higher intake of n-3 PUFA and fish consumption also predicted better lung function in smokers and former smokers. Many other investigations, including the same cohort, were subsequently published; n-3 PUFAs were associated with a modification of hemostatic factors—key components of CAD and acute myocardial infarction—resulting in a hypocoagulable profile [n-6 PUFA) among participants who developed CAD [n-3 PUFAs were associated with a decreased risk of heart failure in women in the ARIC cohort [Shahar et al. analyzed the dietary impact of ith COPD . COPD waith COPD . The aut profile . Data froped CAD . YamagisC cohort .n-3 PUFA (assessment of n-3 PUFA via questionnaire) consumption and ventilatory function (FEV1% and FVC%) was found in 1232 adults aged between 22 and 28 years [n-3 PUFA. The idea of analyzing a cohort of young adults was related to the fact that reduced maximal attained lung function (as measured by spirometry) may identify individuals who are at increased risk for the subsequent development of COPD [A positive association between 28 years . The ana of COPD .n-3 PUFAs, EPA, DHA, and DPA intake were positively associated with better FEV1 volumes, and DPA was associated with a reduced age-related FEV1 decline. No increased FEV1 decline of currently smoking participants was observed in one of the two cohorts (Lovelace Smoker’s cohort) if high amounts of DPA were consumed. The study only included approximately 25% COPD patients, and both cohorts showed a mean FEV1/FVC ratio of >0.7, limiting the disease to mild-to-moderate stages. No data about CVD prevalence in the two analyzed cohorts are provided.Leng et al. conducted a nutritional epidemiological study in two separate cohorts  intake via questionnaire) to identify nutrients associated with FEV1 and its decline . Of the n-3 PUFA (assessment via questionnaire) intake was not found to be associated with %FEV1 in the linear regression model, and no information is given about the relation between n-3 PUFA and IL-6. Overall, the patients included in the study had no absolute limitation of FEV1 (approximately 80% predicted) and obstructive ventilation, a typical feature of COPD, was not found (FEV1/FVC > 0.7.) Moreover, the assessed smoking status revealed non-smokers in 73% of female patients and 39% of male patients and, therefore, the odds of COPD patients being included in the study are low.Conflicting evidence came from another population-based cohort study showing that an increased proportion of dietary fat consumption was associated with increased IL-6 levels, which were significant predictors of FEV1 and FVC . Plasma n-3 PUFA between the two groups and showed lower levels of DHA and EPA in smokers. They hypothesized that PUFA might interfere with smoking habits, suggesting dietary supplementation as a potential therapeutic option for smoking cessation. Still, this observation was not confirmed in follow up studies thus far and urges further elucidation.In 2016, Scaglia et al. analyzed dietary habits related to PUFA consumption , EPA, DPA, and DHA) in smokers and lifetime non-smokers . They fon-3 PUFA in COPD patients have been studied [n-3 PUFA (ALA) or an n-6 PUFA (linoleic acid) supplementation diet for two years. Apart from clinical improvements , leukotriene B4 levels in serum and sputum, as well as TNFα and IL-8 levels in sputum, decreased significantly in patients consuming an n-3 PUFA rich diet, while there was no significant change in individuals receiving dietary supplementation of n-6 PUFA.The anti-inflammatory proprieties of  studied  in sixtyn-3  and n-6 PUFA [A cross-sectional study including 250 COPD patients analyzed inflammatory parameters related to dietary intake of n-6 PUFA . Higher n-3 PUFA, namely ALA, EPA, and DHA) on the outcome of pulmonary rehabilitation were studied in 80 COPD patients randomized to an intervention or a placebo group [n-3 PUFA supplementation. Thus, systemic inflammation is not a common feature of all COPD patients. Therefore, a more detailed analysis of the subgroup of “inflamed COPD” patients would be desirable. Still, it is unanswered as to how the biochemical phenotype of an “inflamed COPD” patient is defined.The effects of PUFA supplementation  Supplementation and the Prevention of Clinical Cardiovascular Disease”, in regard to included COPD patients ,54. Of tn-3 PUFA (850 to 882 mg of EPA and DHA) in the prevention of atrial fibrillation (AF) recurrence rates after electrical cardioversion in a prospective randomized controlled trial, including 199 patients [n-3 PUFAs-treated patients as compared with the placebo group. A small subgroup of approximately 10% COPD patients (n = 12 placebo vs. n = 13 intervention) was included in the study, however, no separate details on the subgroup were discussed.Nodari et al. analyzed the benefits of patients . After on-3 PUFA  if given on top of modern guideline-adjusted therapy in survivors of acute myocardial infarction [n = 122 placebo vs. n = 108 intervention group). Overall, guideline-adjusted treatment of acute myocardial infarction already resulted in a low rate of sudden cardiac death and other clinical events within one year of follow-up, and the application of n-3 PUFA could not further improve the outcome. No details are provided regarding such effects in the COPD subgroup.Rauch et al. analyzed the effect of farction . The stun-3 PUFA (850–882 mg EPA and DHA) in patients with chronic heart failure [n-3 PUFA intervention or placebo. A subpopulation of 205 COPD patients was included in the study (n = 793 placebo vs. n = 740 intervention). Primary endpoints were time to death from any cause or death/admission to hospital for a cardiovascular reason. A slight, yet significant reduction of death from any cause or death/admission to hospital for cardiovascular reasons was found in the PUFA group. No specific details are provided for the COPD subpopulation.The GISSI-HF investigators published a randomized, double-blind, placebo-controlled trial analyzing the effect of  failure . In totan-3 PUFA (EPA 465 mg and DHA 375 mg) in the prevention of AF in 1516 patients who underwent cardiac surgery [n = 90 vs. intervention n = 80) was included in the study. The authors concluded that perioperative supplementation with n-3 PUFA did not reduce the risk of postoperative AF and, similarly to the aforementioned studies, no details are provided about the COPD subgroup.Finally, Mozaffarin et al. analyzed the effect of  surgery . Patientn-3 PUFA supplementation in the setting of atrial fibrillation recurrence rate after electrical cardioversion and a slight reduction of death and hospital admission in patients with left heart failure, both diseases closely associated with CAD. No benefits were seen in survivors of acute myocardial infarction or patients with atrial fibrillation after cardiac surgery. The weak benefits seem to also apply to COPD patients, which were included in 5–20% of the discussed studies. However, as already mentioned earlier, the high number of undiagnosed COPD cases and low awareness of investigators towards this chronic disease in patients with cardiac pathologies led to neglect of this important aspect even in large and prospective trials. In our opinion, it seems plausible that other studies also included COPD patients at a rate of at least 5–10%. Thus, the results from the Cochrane systematic database review and the American Heart Association Science Advisory might also apply to COPD patients.In summary, these studies showed a weak benefit of n-3 PUFAs have been intensively studied for their ability to improve morbidity and mortality in patients with CAD. However, little evidence exists toward the beneficial anti-inflammatory aspects of n-3 PUFA in COPD patients, and even less evidence is available in regard to patients with COPD and associated CAD.COPD represents a growing healthcare concern worldwide. Pathophysiological aspects of COPD imply a local inflammatory reaction, initiated by inhalation of noxious substances, which, in some cases, led to the emergence of inflammatory mediators in the systemic circulation. Epidemiologic and mechanistic studies indicate that COPD is frequently associated with CAD, congestive heart failure, and cardiac arrhythmias, independent of shared risk factors. A possible pathway is highlighted as chronic low-grade systemic inflammation, an aspect that likely also contributes to coronary artery disease. n-3 PUFA in COPD patients related to clinical outcomes, and n-3 PUFA mediated borderline amelioration of local and systemic inflammation. To date, the specific interplay between n-3 PUFA, COPD, and CAD has not been investigated in prospective trials.Overall, the available literature suggests a weak benefit of n-3 PUFA in COPD and CAD revealed a relatively small number of registered trials on http://ClinicalTrials.gov. To date, only one trial investigating the effect of fish oil on endothelial function by FMD (flow-mediated dilation) in COPD has been registered (NCT00835289). Another trial has investigated the effect of both training and nutritional supplementation on body composition, activity, and cardiometabolic risk in COPD (NCT01344135). However, as nutritional supplementation has not occurred in all subjects and as an observational period of eight months may not be enough to reach clinically important cardiovascular and COPD endpoints, prospective, randomized controlled studies are warranted. Future studies ought to measure atherosclerotic disease progression, as well as establish relevant COPD inflammation parameters in order to determine the role of PUFA in inflammation-mediated diseases such as COPD and CAD. Moreover, standardized PUFA administration as well as a sufficient observation period may be crucial to understanding the role of PUFA in these diseases. Successful trials may help introduce a standardized supplementation of PUFA in patients with CAD or COPD or both to reduce disease progression or development.In-depth keyword guided research on ongoing trials on