Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa—along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas.Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient—experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in  k – the so-called k-mers. These k-mers provide a versatile descriptor, allowing to capture genetic variants ranging from local polymorphisms to insertions of large mobile genetic elements. Unfortunately, they are also redundant and difficult to interpret. We rely on the compacted De Bruijn graph (cDBG), which represents the overlaps between k-mers. A single cDBG is built across all genomes, automatically removing the redundancy among consecutive k-mers, and allowing for a visualisation of the genomic context of the significant ones. We provide a computationally efficient and user-friendly implementation, enabling non-bioinformaticians to carry out GWAS on thousands of isolates in a few hours. This approach was effective in catching the dynamics of mobile genetic elements in Staphylococcus aureus and Pseudomonas aeruginosa genomes, and retrieved known local polymorphisms in Mycobacterium tuberculosis genomes.Genome-wide association studies (GWAS) help explore the genetic bases of phenotype variation in a population. Our objective is to make GWAS amenable to bacterial genomes. These genomes can be too different to be aligned against a reference, even within a single species, making the description of their genetic variation challenging. We test the association between the phenotype and the presence in the genomes of DNA subsequences of length  The aim of Genome-Wide Association Studies (GWAS) is to identify associations between genetic variants and a phenotype observed in a population. They have recently emerged as an important tool in the study of bacteria, given the availability of large panels of bacterial genomes combined with phenotypic data –7.GWAS rely on a representation of the genomic variation as numerical factors. The most common approaches are based on single nucleotide polymorphisms (SNPs), defined by aligning all genomes of the studied panel against a reference genome , 3, 4 ork found in the genomes [To circumvent these issues and make bacterial genomes amenable to GWAS, recent studies have relied on k-mers: all nucleotide substrings of length  genomes , 5, 6. T genomes .While k-mers can reflect any genomic variation in a panel, they do not themselves represent biological entities. Translating the result of a k-mer-based GWAS into meaningful genetic variants typically requires mapping a large and redundant set of short sequences , 6, 13. De Bruijn Graph GWAS, bridges the gap between, on the one hand, SNP- and gene-based representations lacking the right level of flexibility to cover complete genomic variation, and, on the other hand, k-mer-based representations which are flexible but not readily interpretable. We rely on De Bruijn graphs [de novo genome assembly [Our approach, coined DBGWAS, for n graphs  (DBGs), assembly , 18 and assembly , 19. Thetopology of the subgraph, reflecting the nature of the genetic variant, 2) the metadata represented by node size and colour, allowing us to identify which unitigs in the subgraph are associated to a particular phenotype status, and 3) an optional sequence annotation helping to detect unitig mapping to—or near—a known gene.DBGWAS relies on the ability of compacted DBGs (cDBGs) to eliminate local redundancy, reflect genomic variations, and characterise the genomic environment of a k-mer at the population level. More precisely, we build a single cDBG from all the genomes included in the association study . The graph nodes—called unitigs—represent, by construction, sequences of variable length and are at the right level of resolution for the set of genomes considered, taking into account adaptively the genomic variation. The unitigs are individually tested for association with the phenotype, while controlling for population structure. The unitigs found to be phenotype-associated are then localised in the cDBG. Subgraphs induced by their genomic environment are extracted. They often provide a direct interpretation in terms of genetic events which results from the integration of three types of information: 1) the Mycobacterium tuberculosis, Staphylococcus aureus and Pseudomonas aeruginosa. The subgraphs built from significant unitigs described SNPs or insertions/deletions in both core and accessory regions, and were consistent with results obtained with a resistome-based association study. In addition, novel genotype-to-phenotype associations were also suggested.We benchmarked our novel method using several antibiotic resistance phenotypes within three bacterial species of various degrees of genome plasticity: https://gitlab.com/leoisl/dbgwas, and validated it on panels for several bacterial species for which genome sequences and antibiotic resistance phenotypes were available. DBGWAS comprises three main steps: it first builds a variant matrix, where each variant is a pattern of presence/absence of unitigs in each genome. Each variant is then tested for association with the phenotype using a linear mixed model, adjusting for the population structure. Finally, it uses the cDBG neighbourhood of significantly associated unitigs as a proxy for their genomic environment. DBGWAS outputs a set of such subgraphs ordered by minq, which is the smallest q-value observed over unitigs in each subgraph. The top subgraphs therefore represent the genomic environment of the unitigs most significantly associated with the tested phenotype. We developed DBGWAS, available at Here we rely on a few experiments to illustrate how the subgraphs output by DBGWAS can be read as genetic events. We then benchmark DBGWAS against two other k-mer-based approaches and one resistome-based approach. DBGWAS recovers known variants, while suggesting novel candidates out of the range of the resistome-based approach. We also find it to be more computationally efficient and to provide more interpretable outputs than the other k-mer-based methods.http://pbil.univ-lyon1.fr/datasets/DBGWAS_support/experiments/#DBGWAS_all_results.A synthetic description of the discussed subgraphs is provided in P. aeruginosa levofloxacin resistance, the subgraph obtained with the lowest minq highlighted a polymorphic region in a core gene , S. aureus ciprofloxacin , and ofloxacin resistance in M. tuberculosis, whose genome does not contain the parC gene [gyrA: minq = 9.66 × 10−144).For ore gene . Indeed,sistance , 21. In arC gene   highlighted a SNP in an accessory gene (aac(6’), a gene coding for an aminoglycoside 6-acetyltransferase, an enzyme capable of inactivating aminoglycosides, such as amikacin, by acetylation [Pseudomonas fluorescens [aac(6’), making its association to amikacin resistance more significant than the gene presence itself.For ory gene . As in Ftylation . Most untylation  revealedorescens . It appeM. tuberculosis ethionamide resistance, the top subgraph , a core gene previously shown to be involved in ethionamide and isoniazid resistance [fabG1 gene, but their close neighbours did , shown in mecA gene, extensively described in this context and known to be carried by the Staphylococcal Cassette Chromosome mec (SCCmec) [SFF) parameter value, which defines the unitigs used to build the subgraphs. By default, the unitigs corresponding to the 100 lowest q-values are retained (SFF = 100). Increasing the SFF value to 150  allowed us to reconstruct the entire SCCmec cassette, as shown in For (SCCmec) , 29, 30.S. aureus erythromycin resistance, a unique subgraph was generated (minq = 2.69 × 10−100). As shown in ermC gene together with a replication and maintenance protein in strong linkage disequilibrium with ermC [For ith ermC , 31.P. aeruginosa amikacin resistance, the third subgraph (minq = 2.21 × 10−6) represented a 10,000 bp plasmid acquisition. Using the NCBI nucleotide database [P. aeruginosa HS87 strain [SFF default value and tuning.For database , most of7 strain . S4 and gyrA and parC alteration in fluoroquinolone resistance in P. aeruginosa [rpsL) and the 16S rRNA (coded by rrs) in M. tuberculosis [Although resistance determinants are not perfectly or exhaustively known for all species, some resistance mechanisms are well described. This is the case of ruginosa , and of rculosis . Here werculosis , 35 , among which were several unknown, potentially new candidate markers.For sistance , both DBM. tuberculosis streptomycin resistance  gathered unitigs mapping to the 3’ region of a DEAD/DEAH box helicase, known to be involved in stress tolerance in P. aeruginosa [P. aeruginosa levofloxacin resistance, the third subgraph (minq = 1.87 × 10−2) represented a L650M amino-acid change in a hybrid sensor histidine kinase/response regulator. Such two-components regulatory systems play important roles in the adaptation of organisms to their environment, for instance in the regulation of biofilm formation in P. aeruginosa [In our ruginosa . The uniruginosa , and as S. aureus, polymorphisms within genes not known to be related to resistance were identified for several antibiotics: purN (minq = 2.02 × 10−22) for fusidic acid, odhB (minq = 1.49 × 10−33) for gentamicin, ybaK and mqo1  for trimethoprim. None of these genes have been associated with antibiotic resistance before, to the best of our knowledge.In M. tuberculosis, polymorphisms in two genes encoding proteins involved in cell wall and cell processes, espG1 and espA, were found associated with streptomycin  and XDR phenotype , respectively. Again, these genes have never been reported in association with antibiotic resistance before.In Although experimental validation would be required to tell whether these hypotheses are false positive  or actual resistance mechanisms not yet documented, DBGWAS is a valuable tool to screen for novel candidate markers. Moreover it provides a first level of variant description  which can directly drive the biological validation.M. tuberculosis streptomycin resistance experiment for example, they reported several thousands of features, while DBGWAS reported only 24 annotated subgraphs without missing any expected determinant  contained 36 significant unitigs, distinguishing between susceptible (blue) and resistant (red) strains. Instead of a single point mutation, this subgraph represented a polymorphic region known as the rifampicin resistance-determining region (RRDR) of the rpoB gene. The unitig with the lowest q-value covered several mutant positions, defining a particular haplotype strongly associated with rifampicin susceptibility. Where DBGWAS reported in this case only one subgraph, pyseer, for instance, reported 470 k-mers with the rpoB annotation, and the resistome-based association study reported in this case 4 distinct SNPs in rpoB  contains a path of three red (positively-associated) nodes lying in a noncoding region between variable accessory genes. Consequently, their neighbour unitigs branch to various other unitigs, making the structure complex and hard to interpret. Complex subgraphs also arise when several associated variants have overlapping neighbourhoods (as defined in the Graph neighbourhoods subsection in the nh parameter) in at least one strain. This is the case for the subgraph with the smallest minq which aggregates aac(6′) acetyltransferase and the CML efflux pump.Admittedly, some subgraphs output by DBGWAS are not readily interpretable: they are neither coloured bubbles highlighting SNPs, nor long single-coloured paths denoting MGE insertions. This was the case of several subgraphs produced for nh and SFF parameters to break large subgraphs into a set of smaller ones, as discussed in the discussed in the aac(6′) subgraph, where nearby variants are aggregated into a large subgraph, reducing the SFF value to 15 provided a much smaller and easier-to-interpret subgraph focusing on the aac(6′) mutation  extracted from the input sequences. Edges represent (k − 1)-exact-overlaps between k-mers: an edge connects a node n1 to a node n2 if and only if the (k − 1)-length-suffix of n1 equals the (k − 1)-length-prefix of n2  into a single node referred to as a unitig –50   to retriU:The unitig description on all the input genomes is stored into a matrix U into Z, which represents the minor allele description, in terms of presence [Z is identical to U except for columns with a mean larger than 0.5, which are complemented: Zj = 1 − Uj for these columns.We then transform the matrix presence : Z is idZ to its set of unique columns. If several unitigs have the same minor allele presence pattern, then they will be represented by a single column. Keeping duplicates would lead to performing the same statistical test several times. Finally, we filter out columns whose average is below 0.01—the user can specify this threshold using the -maf option. We denote the de-duplicated, filtered matrix of patterns by X.We then restrict Importantly, both k-mers and unitigs lead to the same set of distinct patterns across the genomes. Indeed, every unitig represents (at least) one k-mer, and conversely every k-mer is represented by one (single) unitig. When de-duplicated, the two representations therefore lead to the same set of patterns to be tested for association with the phenotype.Human GWAS literature extensively discusses how testing procedures can result in spurious associations if the effect of the population structure is not taken into account –55. Popunewick option. An example of the generated figures is available at http://pbil.univ-lyon1.fr/datasets/DBGWAS_support/full_dataset_visualization/.We thus rely on the bugwas method , which uYi, given the j-th minor allele pattern Xij and the population structure represented by a set of factors β is the fixed effect of the tested candidate on the phenotype, σ2 > 0. W is estimated from the Z matrix, which includes duplicate columns representing both core and accessory genome. More precisely, denoting Z = USV⊤ the singular value decomposition of Z, we use W = US.Formally, the LMM represents the distribution of the binarized phenotype H0: β = 0 versus H1: β ≠ 0 in We test te (FDR) .The LMM is used to identify de-duplicated minor allele presence patterns significantly associated with the phenotype at a chosen FDR level. While the testing step is done at the pattern level, the interpretation of the selected features is done at the unitig level. As a result of the de-duplication procedure, a given pattern may correspond to several distinct unitigs. To faithfully interpret the results, all the unitigs corresponding to the significant patterns are retrieved and are assigned the q-value of their pattern. We now show how the initial cDBG can be used in the interpretation step.The interpretation step focuses on the unitigs with the lowest q-values. These unitigs are indeed used to build the resulting annotated subgraphs. The unitig selection can be either based on the FDR  or on a number of presence/absence patterns ordered by increasing q-values. Practically, this is done in DBGWAS using a Significant Features Filter (SFF). For a selection based on a FDR threshold, the SFF value is set between 0 and 1, while any integer value > 1 defines the number of patterns to consider.S. aureus ciprofloxacin) as shown in In our experiments, we choose not to apply a fixed FDR threshold, even though DBGWAS offers this option. Different datasets lead to different q-values, even by several orders of magnitude, and a single FDR threshold would lead to selecting a large number of unitigs generating more than 1,000 subgraphs on some of them (e.g. u (defined by the SFF) as the set of unitigs whose shortest path to u has at most ne = 5 edges. Users can modify the ne value using the -nh option. The objects returned by DBGWAS are the connected components of the graph induced by the neighbourhoods of all significant unitigs in the cDBG. As illustrated in subgraphs in the We define the neighbourhood of each significant unitig SFF value can be tuned to optimise the number and size of the output subgraphs. It has no impact on subgraphs describing SNPs in core sequences  or nucleic acid (-nt-db option) sequence databases. We annotate the subgraphs of interest by blasting each unitig sequence to the available databases. Users can then easily retrieve the annotations which are the most supported by the nodes in the subgraph, or with the lowest E-value. Importantly, DBGWAS works with any nucleotide or protein Fasta files as annotation databases straight away. However, users can customize the annotation databases by changing the Fasta sequences headers to make DBGWAS results more interpretable. A common example is compacting the annotation in the summary page by using abbreviations or gene class names, and expanding them to full names in the subgraph page. Other custom fields can also be included in the annotation table by adding specific tags to the headers. A detailed explanation on how to customize annotation databases for DBGWAS can be found in https://gitlab.com/leoisl/dbgwas/wikis/Customizing-annotation-databases. We also provide on the DBGWAS website a resistance determinant database built by merging the ResFinder, MEGARes, and ARG-ANNOT databases [DBGWAS can optionally integrate an automated annotation step using the Blast suite  , or based on the annotations. A complete description of the DBGWAS interactive interface is available in https://gitlab.com/leoisl/dbgwas/wikis/DBGWAS-web-based-interactive-visualization.DBGWAS produces an interactive view of the enriched and annotated subgraphs, allowing the user to explore the graph topology together with information on each node: allele and phenotype frequencies, q-value, estimated effect, and annotation. The view is built using HTML, CSS, and several Javascript libraries, the main one being Cytoscape.js . Resultsskip1 option allows to re-run from the second step, for instance to compute the lineage effects (adding the -newick option). It is also possible to re-run only the third step by using the -skip2 option, for instance when the default SFF and nh values generated highly connected graphs, or if the annotation was incomplete.It is possible to re-run a part of the analysis if a first run with the default values was unsatisfactory. The -M. tuberculosis, S. aureus, and P. aeruginosa. We also built large datasets with random phenotypes for these 3 species, and used them only for time performance and memory usage assessment. All panels are summarised in We used in our experiments genome sequences from three bacterial species with various degrees of genome plasticity, from more clonal to more plastic: M. tuberculosis (TB) is a human pathogen causing 1.7 million deaths each year [ach year . This spach year  in core ach year . We use ach year , 68. We ach year . XDR pheS. aureus is a human pathogen causing life-threatening infections. It is subject to HGT and many plasmids, mobile elements, and phage sequences have been described in its genome. However, this does not affect the species’ genome size, which is always close to 3 Mbp [to 3 Mbp . Most anto 3 Mbp . This stP. aeruginosa is a ubiquitous bacterial species responsible for various types of infections. It is highly adaptable thanks to its ability to exchange genetic material within and between species [ species . The spe species , 65, 71. species  (n = 63)Most available phenotypes are categorical, with S, I and R levels, respectively, for susceptible, intermediary, and resistant. We binarise them by assigning a zero value to susceptible strains (S) and one to others (I and R).M. tuberculosis , S. aureus , and P. aeruginosa  available on the NCBI RefSeq bacterial genome repository [We built large panels for the three species, in order to analyse the computational performance at a comprehensive scale. To do so, we gathered all genome assemblies of pository , and remWe benchmarked DBGWAS against a targeted approach to ensure its ability to retrieve all expected resistance determinants. We thus performed association studies under the same model, using as input a collection of known causal resistance SNPs and genes, defining the resistome.W, so the resistome-based analyses and DBGWAS only differ by their input variant matrix (unitigs versus SNPs or genes presence/absence).In this validation study, we used bugwas with the same phenotypes and population structure matrix P. aeruginosa resistome, we use a variant matrix previously described [M. tuberculosis resistome, we built the variant matrix using the same approach as for P. aeruginosa [For escribed , which iruginosa : we callruginosa , 67, 73.P. aeruginosa levofloxacin and M. tuberculosis streptomycin resistance, respectively.We sort the annotated features by q-values. d17602500a4530b0e68a679ed675fdb12942f56f (9 commits ahead of pyseer v1.1.1). pyseer pipeline is composed of four steps: 1) k-mer counting; 2) population structure estimation; 3) running pyseer; 4) downstream analysis. To use the correct parameters, we followed the pyseer tutorial . For k-mer counting, we used fsm-lite , filtering out all k-mers with a minor allele frequency smaller than 1%. For population structure estimation, we used Mash v2.0 [scripts/count_patterns.py script), sorting them by LRT p-value, blasting them against the two databases presented in the Interpretation of significant unitigs (step 3) subsection, and keeping the best hit for each k-mer. For reproducibility purposes, the scripts we used to run pyseer can be found at https://gitlab.com/leoisl/DBGWAS_support/tree/master/scripts/pySEER.We installed pyseer , 36 commash v2.0 . To run P. aeruginosa levofloxacin and M. tuberculosis streptomycin resistances by other methods (see Tables jellyfish dump (parameter -L), since we are working with contigs and not reads. The last step was performed similarly as the one described for pyseer. For reproducibility purposes, the scripts we used to run HAWK v0.8.3-beta can be found at https://gitlab.com/leoisl/DBGWAS_support/tree/master/scripts/HAWK_0_8_3_beta.We firstly ran HAWK  v0.9.8-bS1 FigIn the first case, the 3 loci represented as polymorphic in the alignment lead to 3 bubble patterns in the cDBG, and numerous redundant k-mers. In the second case, genomes are so polymorphic that an alignment is not possible. The cDBG summarizes well the common regions and the links between them, while the collection of unique k-mers still contains redundancy.(PDF)Click here for additional data file.S2 FigparC, green for gyrA, yellow for norA promoter region, blue for noncoding between glmM and fmtB and violet for transposase flanking regions).Annotation of the first subgraphs is strictly conserved (red for (PDF)Click here for additional data file.S3 FigmecA gene (highlighted in red) is generated for lower SFF values. Then several regions of the SCCmec cassette appear for SFF = 70, and are aggregated into a single subgraph for SFF ≥ 150. Green subgraphs do not concern the mecA MGE.Only one subgraph, containing the (PDF)Click here for additional data file.S4 FigblaZ MGE. Annotations are ordered by number of nodes carrying it. Yellow, orange and pink highlight blaZ, blaR1 and blaI, respectively.Green subgraphs do not concern the (PDF)Click here for additional data file.S5 FigermC and its plasmid is outputted when SFF < 200. Green subgraphs do not concern the ermC MGE.Only one subgraph, describing the (PDF)Click here for additional data file.S6 Figaac(6’) gene are shown in a blue frame. When the SFF parameter increases, these nodes aggregate to others genes found at least once close to aac(6’). The annotation of the following subgraphs are well conserved (same color legend as in Nodes corresponding to (PDF)Click here for additional data file.S7 Figk value varying between 21 and 41, the first 3 subgraphs always have the same ordering, shape and annotation, as well as comparable q-values, although smaller q-values are observed for lower values of k. The number of significant unitigs per subgraph is also well conserved. The fourth top-rated subgraphs are not always the same: the gyrA mutation appears at a lower rank when k is smaller.With a (PDF)Click here for additional data file.S8 Figk varies, the plasmid (yellow) and the mercury reductase and transposase (blue) remain among the five top-rated subgraphs. However, k has an effect on the aggregation of subgraphs corresponding to different genetic events: the mutation on aac(6’) gene (blue frame) always appears in the first subgraph but is merged with the large mercury reductase and transposase subgraph for k = 27, 39 and 41. The order of the subgraphs also varies with k: up to four ranks for some subgraphs, and others leave the top-5 list.When (PDF)Click here for additional data file.S9 FigThis figure describes how DBGWAS scales in terms of time and memory usage for large datasets, containing up to 9,000 genomes. The large panels used here are described in the Large panels subsection of the (PDF)Click here for additional data file.S1 TableIntel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz core. The datasets are described in the Datasets subsection of the All runs presented in this table were executed with the default parameters, without optional steps , on a single (PDF)Click here for additional data file.S2 TableIntel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz cores, 315 GB of RAM and 1 TB of disk space. Each execution used all the 8 available cores. The datasets are described in the Datasets subsection of the Timeout. The runs that exceeded 1 TB of disk space were interrupted and are shown as DQE (Disk Quota Exceeded).The total execution time is presented with the maximal memory consumption in parenthesis, in order of GBs. For pyseer and HAWK, the time and memory for each step is also detailed. All tools were ran on a same machine with 8 (PDF)Click here for additional data file.S3 Tableq), the corresponding estimated effect (estimated β of the linear model) and the number of susceptible (resp. resistant) strains harbouring this unitig (count per phenotype). The type of event represented by the subgraph, its annotation and some comments and references on this annotation were also provided. Comments were coloured if the annotation was previously described in antibiotic resistance literature: in green if this description concerned the tested antibiotic, in orange otherwise.For each antibiotic, top subgraphs were reported with their rank, the q-value of the unitig with the lowest q-value (min(XLS)Click here for additional data file.S4 Tableq), the corresponding estimated effect (estimated β of the linear model) and the number of susceptible (resp. resistant) strains harbouring this unitig (count per phenotype). The type of event represented by the subgraph, its annotation and some comments and references on this annotation were also provided. Comments were coloured if the annotation was previously described in antibiotic resistance literature: in green if this description concerned the tested antibiotic, in orange otherwise.For each antibiotic, top subgraphs were reported with their rank, the q-value of the unitig with the lowest q-value (min(XLS)Click here for additional data file.S5 Tableq), the corresponding estimated effect (estimated β of the linear model) and the number of susceptible (resp. resistant) strains harbouring this unitig (count per phenotype). The type of event represented by the subgraph, its annotation and some comments and references on this annotation were also provided. Comments were coloured if the annotation was previously described in antibiotic resistance literature: in green if this description concerned the tested antibiotic, in orange otherwise.For each antibiotic, top subgraphs were reported with their rank, the q-value of the unitig with the lowest q-value (min(XLS)Click here for additional data file.S6 Tableβ of the linear model). The type of targeted variant, with its gene annotation were also provided. Comments were coloured if the annotation was previously described in antibiotic resistance literature: in green if this description concerned the tested antibiotic, in orange otherwise. The last column presents the corresponding subgraphs found by DBGWAS, with their rank and minq.For each antibiotic, the 10 first features most associated to the phenotype were reported, with their rank, q-value, and estimated effect (estimated (XLS)Click here for additional data file.S7 Tableβ of the linear model). The type of targeted variant, with its gene annotation were also provided. Comments were coloured if the annotation was previously described in antibiotic resistance literature: in green if this description concerned the tested antibiotic, in orange otherwise. The last column presents the corresponding subgraphs found by DBGWAS, with their minq.For each antibiotic, the 10 first features most associated to the phenotype were reported, with their rank, q-value, and estimated effect (estimated (XLS)Click here for additional data file.S8 TableSFF = 100, ‘top 100’) or when using a 5% false discovery rate (FDR) threshold . Different datasets lead to different q-values, even by several orders of magnitude. For instance, a single FDR threshold leads to selecting a large number of unitigs generating several hundreds subgraphs for SA (S. aureus) panel.This table shows the number of subgraphs generated when defining the significant unitigs as the ones with the 100 lowest q-values (default (PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


Background: Twin studies indicate that genetic and environmental factors contribute to both psychological resilience and coping style, but estimates of their relative molecular and shared environmental contributions are limited. The degree of overlap in the genetic architectures of these traits is also unclear.Methods: Using data from a large population- and family-based cohort Generation Scotland , we estimated the genetic and shared environmental variance components for resilience, task-, emotion-, and avoidance-oriented coping style in a linear mixed model (LMM). Bivariate LMM analyses were used to estimate the genetic correlations between these traits. Resilience and coping style were measured using the Brief Resilience Scale and Coping Inventory for Stressful Situations, respectively.Results: The greatest proportion of the phenotypic variance in resilience remained unexplained, although significant contributions from common genetic variants and family-shared environment were found. Both task- and avoidance-oriented coping had significant contributions from common genetic variants, sibling- and couple-shared environments, variance in emotion-oriented coping was attributable to common genetic variants, family- and couple-shared environments. The estimated correlation between resilience and emotion-oriented coping was high for both common-variant-associated genetic effects , and for the additional genetic effects from the pedigree . Genetic correlations between resilience and task- and avoidance-oriented coping did not meet statistical significance.Conclusions: Both genetics and shared environmental effects were major contributing factors to coping style, whilst the variance in resilience remains largely unexplained. Strong genetic overlap between resilience and emotion-oriented coping suggests a relationship whereby genetic factors that increase negative emotionality also lead to decreased resilience. We suggest that genome-wide family-based studies of resilience and coping may help to elucidate tractable methodologies to identify genetic architectures and modifiable environmental risk factors to protect against psychiatric illness, although further work with larger sample sizes is needed. Resilience has increasingly become a focus of behavioural and medical research6, promoting positive mental health and offering an alternative to ‘deficit’ models of psychopathology7. Whereas resilience refers to positive adaptation in the face of adversity, coping style encompasses cognitive and behavioural strategies used to manage adversity8. Although often used interchangeably, there is a growing body of evidence to suggest that resilience and coping style are conceptually distinct constructs12. However, the underlying biological mechanisms of and genetic similarities between resilience and coping are not well understood.Despite significant risk for psychopathology, many individuals exhibit better than expected adjustment. This ability to ‘bounce back’ and maintain or regain mental health despite significant risk is referred to as13. Such estimates have been applied to the study of both resilience and coping style in family-based twin studies. To illustrate, in defining resilience as the residual for positive affect after controlling for social and interpersonal stressors, a twin study by Boardman and colleagues14 found resilience was significantly more heritable among men (52%) than women (38%). Similarly, another twin study found modest heritability estimates (24–49%) on measures of well-being and mental health which were indirectly related to resilience15. A longitudinal twin study4 estimated resilience as the residual between actual and predicted psychiatric symptoms, based on the total number of stressful life events an individual has experienced, and found moderate heritability estimates at both waves of assessment approximately five years apart (~31%). However, it is important to note that wide discrepancies in both the definition and measurement of resilience preclude accurate comparison of these results5.A starting point from which to address whether the observed variation in resilience and coping style is due to environmental or biological factors is to investigate heritability. Heritability is broadly defined as the proportion of phenotypic variation that can be attributed to genetic effects18 which focus on task-, emotion-, and avoidance-oriented coping styles. Emotion-oriented coping is characterised by the regulation of distressing emotions, whereas task-oriented coping denotes purposeful efforts aimed at problem solving19. Avoidance-oriented coping is defined by behaviours aimed at avoiding difficult circumstances16. Evidence from twin studies suggests that coping style may be geneticallyand environmentally mediated. For example, a twin study20 found that whereas task-and emotion-oriented coping were modestly heritable (17–20%), avoidance-oriented coping was entirely determined by environmental factors. The majority of the variance in coping style was attributable to non-shared environmental influences. Furthermore, Kozaket al.21 found modest genetic influences to the variation seen in each coping style (33–39%), in addition to substantial environmental variance. Kendleret al.22 found that approximately 30% of the total variance in the coping styles of ‘turning to others’ and ‘problem solving’ was attributable to genetic effects. Interestingly, however, the variability in the use of ‘denial’ coping styles was entirely accounted for by environmental influences.Several well-validated questionnaires have been developed to measure coping style24. However, recent methodological developments in Genome-Wide Association Studies (GWAS) offer an alternative to pedigree-based heritability estimates through molecular estimates in unrelated samples via Single Nucleotide Polymorphisms (SNPs)26. Specifically, this method decomposes the phenotypic variance of a trait into that attributable to common genetic variants (SNPs) and that attributable to the entire genome to estimate narrow-sense heritability (27. Yang and colleagues28 have subsequently found that variation in complex traits (such as resilience and coping) are likely resultant from a large number of common variants with effect sizes too small to pass the stringent threshold of GWAS, indicating they are the result of polygenic inheritance. Elaborating on these findings, Zaitlen and colleagues29 have developed a genomic relatedness restricted maximum likelihood (GREML) variance component method that estimates heritability between both related and unrelated individuals, simultaneously. Using genome-wide data this method yields more accurate heritability estimates than other methods because no assumptions are made on the extent of genetic similarity, dominance effects, or epistasis30. Specifically, using the GREML method, heritability estimates have been found to lie between those estimated from pedigree- and GWAS-based studies, and have been considered as a lower limit for the former and an upper limit for the latter31. Furthermore, GREML methods enable researchers to disentangle the differential contributions of molecular and non-additive genetic effects to phenotypic variance whilst simultaneously modelling environmental effects29 which overcomes the caveats of both pedigree- and GWAS-based heritability estimates.Pedigree-based estimates such as those described above measure heritability by comparing the observed phenotypic similarity to the expected genetic resemblance between relatives, and as such, do not require information on the inheritance of individual genetic loci33, we extended our methods to bivariate analysis to investigate whether these traits have significant overlapping genetic architectures using genetic correlation.Studies seeking to identify the genetic and environmental contributions to resilience and coping style are an important starting point from which to build an understanding of their aetiology in addition to identifying treatment strategies focussing on primary prevention which may have significant impacts on mental health conditions. In this study, we sought to partition the phenotypic variation of resilience and coping style into their genetic and shared environment components using GREML methods in a family-based genotyped cohort; Generation Scotland: Scottish Family Health Study (GS:SFHS). We drew on the diverse familial relationships within the sample to estimate both molecular and pedigree genetic effects and the contribution of early shared family environment and recent shared environment by analysing family members/siblings and couples respectively. Furthermore, as resilience has been found to be positively correlated with task-oriented coping, and negatively with indices of emotion- and avoidance-oriented coping34 is a family-based population cohort recruited from General Practitioners’ practices throughout Scotland between 2006 and 2011. Individuals were eligible for participation if they were aged above 18 years and had at least one first-degree relative also willing to participate. A total of 5,628 families  spanning up to three generations were recruited. In 2014, GS:SFHS participants were re-contacted and asked to take part in a follow-up study of mental health and resilience35. A total of 9,618 participants provided useable re-contact data – a retention rate of 45% - and represent the participants included in the current study. Full cohort details and recruitment procedures for baseline and re-contact are described elsewhere36. All components of GS:SFHS, including its protocol and written study materials have received national ethical approval from the NHS Tayside Committee on Research Ethics (reference 05/s1401/89).The Generation Scotland: Scottish Family Health Study (GS:SHFS)Wellcome Trust Clinical Research Facility, Edinburgh. Genome-wide genotype data were generated using the Illumina HumanOmniExpressExome-8 v1.0 DNA Analysis BeadChip  and Infinium chemistry37. The details and procedures for DNA extraction and genotyping have been reported extensively elsewhere39. Population outliers were removed from the sample40. Quality control of genotyped SNPs used inclusion thresholds: call rate ≥98%, missing SNPs per individual ≤2%, Hardy-Weinberg equilibrium p>1×10-6, and minor allele frequency >1%. In total, 561,125 autosomal SNPs for 8,734 individuals remained and were used in subsequent analysis. Multidimensional scaling (MDS) components were created according to the ENIGMA 1000 genomes protocol41 in the software packagePLINK42 (version 1.9).At baseline, blood and salivary DNA samples were collected, stored, and genotyped at the1, a self-report questionnaire assessing an individual’s ability to ‘bounce back’ or recover from stress. The BRS consists of six statements  answered on a five-point scale from “Strongly Disagree” to “Strongly Agree”. After reverse coding of even-numbered questions, a total resilience score was calculated by computing the mean of six questions. The BRS has been found to have a one-factor structure, demonstrating good internal consistency  and test-retest reliability of 0.69 for one month and 0.62 for three months1.Psychological resilience was assessed at re-contact using the Brief Resilience Scale (BRS)43 was completed at re-contact. The CISS is a 48-item self-report questionnaire in which responders indicate how much they engage in various coping activities “when under stress”, on a five-point scale from (1) ‘Not at all’ to (5) ‘Very much’. Scores are summed over three 16-item sub-scales scales measuring task-oriented (e.g. “when under stress I focus on the problem and see how I can solve it”), emotion-oriented  and avoidance-oriented  coping styles. The CISS has proven a robust measure of assessing situation-specific coping strategies, with a stable factor structure, high internal reliability and construct validity16.The Coping Inventory for Stressful Situations (CISS)et al.29, we utilised a new method introduced by Xiaet al.44 to simultaneously estimate29. Using genome-wide complex trait analyses (GCTA: version 1.22)30 the genetic and shared environmental contributions to each trait were measured by partitioning the phenotypic variance using linear mixed modelling (LMM) techniques. For each trait, two genomic relationship matrices;G (genomic relationship matrix) andK (kinship matrix created by modifying G using a threshold of 0.05 for pairwise relatedness)44, and three environment relationship matrices,F ,S  andC 44, were fitted separately or simultaneously in LMM. The corresponding variance componentsG),K),F),S) andC) were estimated using LMM and their significance tested using likelihood ratio tests (LRT). Age, sex and four MDS components were included in each LMM as fixed effects. Details on the construction of the variance-covariance matrices can be found in theUsing a recently developed variance component methodology based on the GREML framework, we calculated the contributions of genetic and shared environmental components to the phenotypic variance in resilience, task-, emotion-, and avoidance-oriented coping. Building on the work of Zaitlen45 suggest that variance estimates may be confounded due to correlations between components. To overcome this issue, a backward stepwise model selection was employed. LRT tests were conducted to test the significance of each variance component, which were removed sequentially if they failed to obtain significance (α = 5%) and had the highestp-value. This process was repeated until all the remaining components were significant. This method is described in more detail elsewhere45.The initial model was a full model comprising of all genetic and environmental components. However, previous studiesGKFSC’ denotes a full model whereby all five matrices were fitted as random effects simultaneously whereas ‘GFC’ represents a model in which only the genomic relationship matrix, the environmental matrix representing nuclear-family-member relationships and the environmental matrix representing couple relationships were fitted simultaneously.To simplify model descriptions, the following codes were used: - e.g. ‘46 was conducted to estimate the correlation between pairs of traits for common genetic variants (Gr), and the pedigree associated genetics (Kr) simultaneously. These models were controlled for age, sex, and four MDS components. The significance of each genetic correlation was estimated using the LRT.Genetic correlations between resilience and each coping style were calculated to examine potential overlapping genetic architectures. Specifically, bivariate GREML analysis in GCTAKFSC matrices for whom genotypic and phenotypic information are available for each trait are shown in theAmong the 8,734 participants with genome-wide genotyped data, we recognised 655 couple pairs, 1,925 full sibling pairs and 4,508 nuclear families . The number of non-zero elements of ther = -0.52 to 0.36), their covariance is sufficiently modest to consider them partially independent variables in our analyses.To determine if the traits in this study had sufficient discriminant validity to warrant independent investigation, phenotypic correlations were calculated between resilience and each coping style. The results from age-, and sex-adjusted Pearson correlations are presented inG andK) alongside three environmental components representing the family, sibling, and couple effects . Specifically, we modelled the effects of additive genetic effects from common variants (GK model), the full model obtained lower estimates of genetic variance which suggests that the full model effectively reduced confounding environmental effects when calculating heritability estimates  of the phenotypic variance was attributable to common genetic variants (G). The pedigree-associated genetic component (K) of this model was not significant, and so the proportion of total additive genetic determination (narrow-sense heritability:G). Of the three shared-environmental components, both sibling- (p = 0.019) and couple-shared (p < 0.001) environmental effects met statistical significance. For emotion-oriented coping, 14%  of its phenotypic variance was determined by common genetic variants, and 14%  was resultant from couple-shared environmental effects. The environmental effects shared between nuclear family members and full-siblings were not significant. For avoidance-oriented coping, 12%  of its phenotypic variance was attributable to common genetic variants (G). Significant effects from sibling- (p = 0.049) and couple-shared (p < 0.001) environment were also found. These results are illustrated inFor task-oriented coping, the full model estimated that 11%  explained 6%  of the phenotypic variation in resilience and family-shared environmental (F) effects explained 5% . Using the same methodology, 14% of the variance in task-oriented coping was explained by common-variant associated genetics . Furthermore, 10% of the variance was explained by sibling-shared environmental effects , and a further 18% of the variance was explained by couple-shared environmental effects . Similar patterns were found in avoidance-oriented coping with 15% of the variance explained by common-variant associated genetics , 7% explained by sibling-shared environmental effects , and 18% of the variance explained by couple-shared environmental effects . In examining emotion-oriented coping, it was found that common genetic , family-shared  and couple-shared environmental effects  were most likely to account for phenotypic variance  and pedigree associated (rK) genetic correlations are reported inK in theGK models captured a mixture of both pedigree associated genetic and environmental effects, we have estimated the genetic correlations forG andK only. As each trait still has contributions fromK , we will examine this effect to prevent inflation in ourG estimates45. The genetic correlations between the four traits are illustrated inG) between resilience and task-oriented coping was .51 . The correlation between resilience and task-oriented coping explained by additional genetic variation associated with pedigree (rK) was 0.20 , although this estimate is potentially influenced by the effects of shared-environment. High genetic overlap was found between resilience and emotion-oriented coping: rG = -0.79 , rK = -0.94 ; and moderate genetic overlap was found between resilience and avoidance-oriented coping: rG = -0.24 , rK = 0.36 .Estimates of common-variant associated  and shared environmental (20–30%) contributions. Furthermore, we found large genetic correlations between resilience and emotion-oriented coping for both common-variant associated (rG = -0.79) and pedigree-associated (rK = -0.94) genetic effects, which suggests that genetic effects have a shared influence on both traits but in opposite directions. Such findings indicate that genetic factors that increase negative emotionality lead to reduced psychological resilience, which mirror previous reports that which suggest resilience and traits characterised by negative emotionality provide partially separate mechanisms to reduce and increase susceptibility to psychopathology, respectively47.Here, we report a novel study examining the genetic and environmental contributions to resilience and coping style in GS:SFHS, a population- and family-based cohort comprising close and distant relatives with genome-wide genotyped data. Using a recently developed variance component methodology48, whereas positive family relationships have been found to prevent negative mental health outcomes in ‘at-risk’ children49, which supports our finding that family-shared environment influences resilience. Behavioural genetics studies suggest that positive familial relationships enable an individual to regulate their behaviour and emotions to perceive their environment as manageable, no matter how challenging51. Furthermore, previous studies have demonstrated that strong familial attachments in childhood have long-lasting impacts on resilience and general well-being in later life52, which is important within the context of this study which examined adults who may no longer be living within the ‘nuclear’ family environment, but whose effects are still apparent.Although the majority of phenotypic variance in resilience remained unexplained in this study,  significant contributions from common variant associated genetic and family-shared environmental effects were found. Within our study, the family effect represents the ‘nuclear’ family, an early environmental influence associated with living in the same family group. It has been found that children with poor familial relationships are more likely to develop psychopathology in later life53. The major contribution of couple-shared environment to coping could potentially capture the effects of assortative mating54, and other factors leading to spousal similarity. However, this effect may also be explained by couples learning from each other and adapting their coping styles to better face the adversity at hand55. Comparatively less variance was accounted for by sibling- and family-shared environmental effects which may be due to the high correlation between the matrices which could potentially impede model fit and estimation45. Previous simulation of these models44 suggest that true components are detected approximately 80% of the time and so the small sibling- and family-shared environmental effects found could be due to false positives in the model. However, without a larger sample size, it would be difficult to have the power to fully discriminate between these components44, and so we advocate further replication in independent samples.In examining both genetic and environmental effects simultaneously, we also detected an almost equal contribution from common-variant associated genetic and couple-shared environment effects for all three coping styles. The couple effect reflects the current environment shared between spouses in adulthood, which contrasts with both the full sibling and nuclear family effect which reflects the influence of earlier shared environments. During stressful circumstances, the support of a spouse (living in the same household) is more likely to be sought than support from closely related family members (living in a different household)32. We must note, however, that correlations for pedigree-associated genetic components are likely biased due to the influence of shared-environmental effects which may be contained within the pedigree component, or vice versa. Unfortunately, due to a lack of power and model non-convergence, we were unable to report the environmental correlations between these traits. It would be of benefit to further investigate the genetic and environmental correlations between these traits in a larger sample to underpin important differences between the traits. For example, in further investigating the environmental correlations between resilience and coping style, we may be able to determine if having a resilient spouse is associated with a particular coping style.We also examined the genetic correlations between resilience and each coping style. Our results revealed very high negative correlations between resilience and emotion-oriented coping for both common-variant associated genetic and pedigree-associated genetic components. These findings suggest that there is a strong shared genetic architecture between resilience and emotion-oriented coping whereby genetic factors that increase negative emotionality also lead to decreased resilience. The direction of these findings supports previous research which suggests that individuals high in negative emotionality, and low in resilience are at a greater risk for psychopathology15. This is unsurprising given that previous reports suggest that GREML-based estimates provide a lower limit for pedigree-based estimates, and an upper limit for GWAS-based estimates31, although without any existing GWAS data on resilience, it difficult to know this for certain. However, the narrow-sense heritability estimates of task- and emotion-oriented coping in this study were in line with previous reports22. Furthermore, genetic estimates of avoidance-oriented coping were at odds with previously reported heritability estimates which found no genetic effects in avoidance-oriented coping styles21. This may be due to our sample being better powered to detect genetic components of avoidance-oriented coping in comparison to previous twin studies22 which found conflicting results with much smaller samples . In our analyses, pedigree-associated genetics (which include rarer genetic variants and mutations) showed no significant contribution to any of our four traits. This is a novel finding as previous estimates suggest that for most complex traits over 50% of narrow-sense heritability is attributable to pedigree-associated genetic effects44. This could be because our sample may not have had sufficient power to separate out pedigree effects from shared environmental effects, indicated by our study failing to detect any significant pedigree effects. Alternatively, this study may have been confounded by correlations between components45.The narrow-sense heritability estimate  or between two independent datasets (using methods such as LD-score regression60). Finally, there may be other major shared and non-shared environmental effects of each of our traits that are not specifically captured in our analysis. For example, research suggests that resilience may be associated with stressful life events, growing up in adversity, or being raised in care65.A number of limitations to this study deserve mention. Firstly, we employed a quantitative trait-based measure of resilience, whereas other behavioural genetic studies have found larger genetic effects with both outcome- and process-based approachesHere, we provide evidence that psychological resilience , is a heritable trait with a relatively small proportion of its variance explained by genetic factors. Early childhood environment such as that shared by the nuclear family was also found to have a small association with resilience. Task-, emotion- and avoidance-oriented coping styles were found to be moderately heritable, although substantial environmental effects also contributed to their phenotypic variance. Approximately one fifth of the variance in each coping style was attributable to recent environment shared by couples. These results indicate that both genetic and environmental contributors to resilience and coping style need to be considered in future research. Finally, high negative genetic correlations between resilience and emotion-oriented coping suggests that the traits share an overlapping genetic architecture in which genetic factors that increase negative emotionality lead to reduced resilience. This is the first study to date which aimed to disentangle the molecular and environmental components in resilience and coping style, and represents a valuable starting point from which to further elucidate the underlying mechanisms of these traits. We argue that further work with larger samples sizes is necessary to fully delineate the genetic and environmental contributions of these traits, and the relationships between them to identify modifiable protective factors against psychological distress and illness.downloaded. Non-identifiable information from the GS:SFHS cohort is available to researchers in the UK and to international collaborators through application to the GS Access Committee:access@generationscotland.org, with further information available fromhttp://www.ed.ac.uk/generation-scotland. Each application requires the completion of a data and materials transfer agreement, the conditions of which are determined on a case by case basis. GS has Research Tissue Bank status, and the GS Access Committee reviews applications to ensure that they comply with legal requirements, ethics and participant consent.Due to the confidential nature of the genetic and phenotypic data provided by participants in this study, it is not possible to publically share the data on which our analyses were based. A phenotype data dictionary from Generation Scotland (GS) is available and can beEthics approval for the Generation Scotland study was given by the NHS Tayside committee on research ethics (reference 05/S1401/8). In this paper, the authors estimate genetic and shared environmental contributions to resilience and coping, and subsequently the genetic correlations between these phenotypes. They use the Generation Scotland: Scottish Family Health Study, comprised of sibling, couple, and nuclear family dyads, from whom DNA samples have been collected. They simultaneously estimate heritability based on common genetic variants and additional additive genetic variance based on family relatedness, as well as early shared environment based on nuclear family and sibling relationships and recent shared environment based on couple relationships. Finally, a bivariate model simultaneously calculated genetic correlations between pairs of traits based on genetic variance from common genetic variants and estimated from relatedness. The statistical modelling, building on GREML, represents an elegant advance in disentangling genetic and shared environmental contributions where there are a variety of degrees of relatedness. The authors measured psychological resilience with the Brief Resilience Scale that assesses self-reported ability to bounce back from stress. Coping was measured with the Coping Inventory for Stressful Situations (CISS) that provides scores for task-oriented, emotion-oriented, and avoidance-oriented coping. For resilience, estimated narrow sense heritability based on additive genetic effects was 14%, which is considerably lower than prior reports using a twin design. However, the twin studies used quite different measures of resilience. Estimated heritability for the three coping scales was 23% to 24%. This result is not substantially at odds with two prior reports using a twin design and the same measure. Estimates of genetic effects based on common variants were not statistically significant for resilience and ranged from 11% to 14% for the three coping scales. Shared environmental effects were not statistically significant for resilience. For task-oriented coping, 27% of the variance was explained by shared environment, especially couple and to a lesser extent sibling effects. Similarly, for avoidance-oriented coping, 22% of the variance was explained by shared environment, again attributable to couple and sibling effects. For emotion-focused coping, 18% of the variance was explained by shared environment, mainly reflecting couple effects that represent current shared environment . The potential of the statistical modelling method is nicely illustrated by these results. The estimated genetic correlation between resilience and emotion-oriented coping was high and negative, whether based on common variants (-.79) or pedigree (-.94). There were positive but not statistically significant genetic correlations between resilience and task-oriented coping. The correlation between resilience and avoidance-oriented coping was negative but not statistically significant when based on common variants and positive but not statistically significant when based on pedigree. Unfortunately it was not possible to estimate shared environmental correlations. The authors conclude that the genetic factors that are associated with increased negative emotionality are also associated with lower resilience. With respect to the implications of this study for understanding coping and resilience, a key issue not introduced by the authors is their choice of a measure of coping. Coping styles are strategies that people use to manage stress and adversity. Typically, problem-focused coping refers to practical ways to address a problem or situation that is causing stress, whereas emotion-focused coping refers to strategies reduce and regulate the distressing emotions arising from the problem or situation. Classic measures of coping include Carver’s COPE and Folkman & Lazarus’s Ways of Coping scale. The CISS measure of task-oriented coping parallels problem-focused coping as assessed by other coping scales. The CISS measure of avoidance-oriented coping largely encompasses distraction, including social diversion, somewhat analogous to self-distraction on the Brief COPE. However, the CISS measure of emotion-oriented coping is comprised of items that are largely about self-blame, which would be categorized by other coping scales as ineffectual coping. In contrast, on the COPE or Ways of Coping, emotion-focused coping encompasses positive reappraisal, acceptance, use of emotional support, etc.negative emotionality. However, there is no explicit mention of the measurement issue, no explicit explanation that the emotion-oriented scale is best understood as a measure of neurotic coping, and no recognition that genetic and environmental contributions to emotion-focused coping as commonly understood are not actually evaluated in this study. The section headed “Resilience, coping style and neuroticism” could be a place to discuss this issue, and to redefine the CISS emotion-focused coping scale as negative emotionality or neurotic coping. It is necessary to appreciate this point in order to make sense of the findings and to consider translational implications. The authors indirectly acknowledge this problem, in that, in the discussion section, they frame the negative relationship between resilience and emotion-oriented coping as a relationship between reduced resilience and The data are available through applying to Generation Scotland and completing a data and materials transfer agreement.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This study aims to disentangle the genetic and environmental contributions to resilience and coping style in Generation Scotland (GS), a population and family-based cohort with genome-wide genotyped data. Psychological resilience was evaluated by the Brief Resilience Scale (BRS), while the coping style was assessed by the Coping Inventory for Stressful Situations (CISS) which measures task-oriented (ToC), emotion-oriented (EoC) and avoidance oriented (AoC) coping styles. The contributions of genetic and shared environmental components to the phenotypic variance in resilience, ToC, EoC, and AoC was estimated using a variance component methodology based on the work of Zaitlen (2013) and Xia (2016) which allows to separately and simultaneously estimate, in genotyped family based studies, the heritability (the proportion of phenotypic variance attributable to genetic variance) due to common genetic variants and to pedigree associated variation. The authors find that common variant associated genetics and family shared environmental effects moderately contribute to psychological resilience , while the contribution of genetics and family shared environmental is a bit larger in all the coping styles. Interesting are the results from the bivariate analysis estimating the genetic correlations between resilience and EoC, where large genetic correlations is found for both common-variant associated and pedigree associated genetic effects, suggesting that genetic effects have a shared influence on both traits in opposite directions: genetic factors that increase negative emotionality lead to reduced psychological resilience. The paper frames the objective of the study in the contest of the current literature and reports clearly and quite accurately its various phases.  The study design is appropriate and the work is technically sound. Details of methods are adequate and analysis could be replicated by other researchers. As regards the statistical analysis, I’m always a bit uncomfortable with the stepwise model selection procedures and I rather prefer to discuss the results from full models, especially when the study sample is underpowered to detect small effects. Furthermore, results from full models are more suitable to be compared across various studies. I appreciate in this paper the effort to illustrate, also in the supplementary materials, the findings of the various models. The authors declare that due to the confidential nature of the genetic and phenotypic data provided by participants in this study, it is not possible to publicly share the data on which the analyses are based. However, non-identifiable information from the Generation Scotland cohort is available to researchers through application to the GS Access Committee. The authors claim that psychological resilience and coping styles are moderate heritable traits, with a relatively small proportion of their variances explained by genetic factors (common variant or pedigree) and, with even more less extent,  by environmental family shared factors. They propose that both genetic and environmental contributors to resilience and coping style need to be considered in future research, perhaps in order to shrink the missing  heritability. Certainly this is a good research suggestion, but I would like to see here a bit of discussion or speculation on the huge proportion of phenotypic variance unexplained by the mentioned component: a part from the role of the error, there is any room for debating on the importance of unshared environment  on the occurrence or prevention of these psychological traits? Minor points: I suggest to use always K or p to denote the pedigree associated genetic component. It easier for the reader to follow the notation. Table 1: are the values in parentheses really the SEs? or the p<0.01? Table 3: the last footnote, second last row, first word: variance should be variant.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.