As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets. Parallelized analysis in clinical genomics can lead to sample or data mislabelling, and could have serious downstream consequences. Here the authors present a tool to quantify sample genetic relatedness and detect such mistakes, and apply it to thousands of datasets from the ENCODE consortium. Parallelized NGS analysis of tissues from many different patients is also commonplace in clinical genomics pipelines. In these settings, sample or data mislabeling, where datasets are incorrectly associated with a donor, can lead to erroneous conclusions, misdirect future research, and affect treatment decisions3 Fig. . Verifyi10. The common logic behind these tools is that each genome harbors a unique set of single-nucleotide polymorphisms (SNPs) that are shared between datasets originating from the same donor. A limitation of these methods is their requirement that sequencing reads from both inputs overlap the exact genomic position of informative SNPs. When insufficient reads satisfy this condition—for example when the input datasets are shallow or target different genomic regions (i.e different transcription factors), the power to evaluate sample relatedness is compromised. Many NGS-based studies now integrate multiple types of assays15 and utilize shallow sequencing to reduce cost at the expense of read depth. This is commonly encountered in highly multiplexed experiments, sequencing spike-ins, and large cohort sequencing efforts in population and cancer genomics . We therefore set out to develop a method for quantifying sample relatedness that was both robust to shallow sequencing depth and that could be systematically applied to modern large-scale projects incorporating multiple data types.Several methods utilize genetic information from NGS datasets as an endogenous barcode to verify sample relatedness16. This association implies that comparing datasets across SNPs in high LD—termed LD blocks—would provide more statistical power to compare datasets than using single SNPs alone. Because of LD, two non-overlapping reads from different datasets may support (or provide evidence against) a common genetic background, as long as they overlap SNPs in the same LD block , and is routinely used for quality control by the Broad Institute’s Genomics Platform, using a small set of LD blocks optimized for use with whole-exome-sequencing data.Linkage disequilibrium (LD) is the non-random association of alleles at different loci within a given populationock Fig. . For eac11 project, the majority of which lie in LD blocks of two or more SNPs in order to maximize the probability of informative read overlap , while SNPs between blocks are approximately independent (r2 < 0.10). Increasing or decreasing the thresholds for within-block and between-blocks correlations by 0.05 had no effect on the method’s performance on a testing dataset (described in the next paragraph). Finally, in order to reduce bias from donor ancestry, we required that LD blocks have similar allele frequencies across different human sub-populations. The pipeline for creating haplotype maps exists as a standalone tool (https://github.com/naumanjaved/fingerprint_maps) and comes with pre-compiled haplotype maps for both hg19 and GRCh38. The pipeline can be customized to create LD blocks in specific genomic areas (i.e. coding regions) and with different parameters (i.e. different intra or inter-block r2).We reasoned that applying Crosscheck across a large, genome-wide set of LD blocks (haplotype map) would allow us to compare the genotype of diverse datasets and would be robust to low coverage and sequencing errors. We constructed a map consisting of nearly 60,000 common  ≥ 10%) bi-allelic SNPs from the 1000 Genomeslap Fig. , MethodsIn the rest of this manuscript we demonstrate that Crosscheck, used with the haplotype map that we generated, can reliably detect donor mislabeling with fewer errors than other existing methods. It is particularly superior in challenging settings such as low sequencing depth or when comparing datasets from diverse data types. We demonstrate the suitability of Crosscheck for large-scale production operation by applying it to 8851 datasets from the ENCODE consortium, and discuss the misannotations that this analysis uncovered.12 database, which hosts data from thousands of diverse NGS experiments (Methods). Classification performance was measured in terms of the false flag rate (FFR), the fraction of donor-matched pairs incorrectly flagged as donor mismatches, and the false-match rate (FMR), the fraction of donor-mismatched pairs incorrectly identified as donor matches. Our testing set comprised all pairwise comparisons between 281 RNA-, DNase-, and ChIP-seq  datasets with verified donor annotations , using LD blocks decreased the FMR and FFR by nearly 10% relative to using single SNPs for cross-assay comparisons.We next quantified how using LD blocks improves classification performance. We generated two equally sized subsets of our full haplotype map—one comprised solely of unlinked SNPs and the other containing only LD blocks with two or more SNPs, and used these to classify the same testing dataset pairs. To simulate sparse datasets generated by spike-ins and multiplexed sequencing, we conducted each comparison at a range of sequencing depths, expressed as the percentage of reads subsampled from the original datasets . NGSCheckmate10 (NGSC) is a model-based method that compares datasets by correlating allele fractions across a panel of reference SNPs, and was the only other method that could be directly compared to Crosscheck on the testing dataset. At high and intermediate read depths, both methods show similar performance. At lower read depths (≤15% subsampling), however, Crosscheck outperforms NGSC, as indicated by a consistently lower FMR and FFR  were flagged as potential donor mismatches (LOD ≤ −5), and only ~0.1% yielded inconclusive results (−5 < LOD < 5) Fig. . We nextThis analysis uncovered three major categories of mislabeling .In conclusion, we present a robust and easy-to-use method for quantifying sample relatedness which outperforms similar methods. Combined with our method for database analysis and haplotype map, CrosscheckFingerprints can be readily applied for detecting sample mislabeling in large, diverse databases without any optimization. We suggest it as a critical component of any NGS quality control pipeline.https://github.com/broadinstitute/picard/raw/master/docs/fingerprinting/main.pdfHere, a basic overview of the fingerprinting LOD score derivation is provided. A more detailed derivation is available at the Picard repository at: A and minor allele B, and two sequencing datasets x and y. Let θ and φ denote the diploid haplotype of datasets x and y, respectively, at this locus. θ and φ can each take one of three possible haplotypes: AA, AB, or BB. Let s be a Bernoulli random variable where s = 1 denotes a sample swap  with posterior probability p, and s = 0 denotes a shared genetic origin . Using Bayes’ rule and the prior probability of no-swap, the posterior odds ratio of a no-swap vs. swap is given byConsider an LD block/locus containing a single bi-allelic SNP with major allele s = 1 and θ = φ. Given that x is conditionally independent of φ and y given θ, and y is conditionally independent of θ given φ, we can also write We assume that in the case of a swap, the distinct individuals are independently sampled from the population and that samples from the same individual have the same genotype, allowing us to write With these two expressions, we derive thatSubstituting the results of Eq.  into Eq.i with correspondingly indexed θi, φi, xi, and yi. We assume that the haplotypes at distinct blocks are independent, and that reads at one block give no information about another. In practice, this assumption is enforced by guaranteeing that a single read cannot be used to provide genotype evidence at more than one locus. We calculate Next, we consider evidence over multiple blocks into Eq.  to get4\Finally, since the odds ratio of no-swap to swap may vary by several orders of magnitude depending on the input files, we compute the base 10 logarithm in order to facilitate comparison and interpretation:x and y arising from the same individual, while a negative LOD is evidence of a sample swap, i.e., the two datasets arose from different individuals. Scores close to zero are inconclusive, and tend to result from low coverage, or poor overlap between the two datasets, at the observed sites.The program assumes a conservative prior of p(θ). The expected LOD contribution needs to be marginalized over the different possible genotypes, leading to a 10 2 ≈ 0.45 at an allele frequency of 0.5  are vanishingly small when the data does not match the genotype. Thus, the LOD for a single locus reduces to −log p(y|θ) is nonzero for only one value of θ. In this case the contribution to the likelihood ratio for that locus reduces toThere is no theoretical lower limit to the contribution of a single locus. This is because, in theory, overwhelming evidence  of different genotypes for two datasets at even a single locus is sufficient to rule out that the samples are derived from the same donor. However, as noted below in the section on the limitations of LOD calculation, there are multiple factors that this formulation does not account for. Our approach ultimately relies on cumulative evidence, albeit noisy, from a large number of loci, rather than looking for the small number of high-confidence cases. It is for this reason that in the implementation of Eq.  in the cx and θ is due to a sequencing error, 10−3 is a reasonable ballpark estimate of p(x | θ)17. With this, the actual score calculated by Crosscheck isσ = −3 by default, and is a parameter that can be set by the user.If both samples are in fact from the same donor, and the discrepancy between The program assumes that sequencing data arrive in the form of reads from a single individual (i.e. not contaminated), from a diploid location in the genome, and with no reference bias. Only non-secondary, non-duplicate reads with mapping quality greater than 20 are used to calculate likelihoods. In addition, bases must have a quality score of at least 20 and must agree with either the reference or pre-determined alternate base to support observations at haplotype blocks. Since the algorithm assumes that read evidence is independent, the reads should have been duplicate marked prior to fingerprinting. The algorithm does not use SNPs from the same read-pair twice, since this would violate the assumption of independence.x for which we observe n total sequencing reads, denoted by rk, at a locus containing a single bi-allelic SNP with major allele A and minor allele B. The possible block haplotypes are then rk which overlaps the SNP, let rk) given the haplotypes. The likelihood of a single base observation I is an indicator function such that A to B.Consider a dataset r can then be written asThe likelihood model for all reads r2 correlation cutoff), we make the simplifying assumption that the genotype at any SNP within an LD block is perfectly correlated with the genotype of the anchor SNP, and that all SNPs within a block have the same allele frequency, equal to that of the anchor SNP. Then, reads overlapping any SNP within a block can be used to infer a total block haplotype, which is represented by the possible diploid genotypes of the anchor SNP. For example, consider an anchor SNPs S1 with major allele A and minor allele B, and a linked SNP S2 with major allele C and minor allele D. Then any observation of allele C at SNP S2 is taken as evidence of allele A at S1, and any observations of allele D at S2 is taken as evidence of allele B at S2. Using this strategy, evidence across all SNPs within a block can be used to infer a total block haplotype, which can be represented by the three possible diploid genotypes of the anchor SNP. That is, for an anchor SNP with major allele A and minor allele B, the possible block haplotypes are AA, AB, and BB, with prior probabilities dependent on the allele frequencies of A and B.The calculations above assume an LD block containing a single SNP for ease of computation, but the framework is easily extended to account for LD blocks containing multiple SNPs, which increases power of comparison. Each LD block used for genotyping contains an “anchor” SNP which is in high linkage with all other SNPs within the block, and independent of all other anchor SNPs in other blocks. Given that all SNPs in a block are tightly linked (enforced with a strict 200 probability of a shared vs. different genetic origin), as the model does not fully account for sequencing noise, data quality, contamination, and relatedness. In addition, we did not model the incomplete dependence between haplotype blocks, nor the incomplete dependence of SNPs within blocks.Though the magnitude of the LOD score reflects greater genotyping confidence, it cannot be directly interpreted as a likelihood ratio , non-conforming LD blocks, or high contamination. These samples could be genotyped as heterozygous due to the noisy region or the non-confirming LD block structure. Including these error modes into the model would increase robustness and accuracy.https://github.com/broadinstitute/picard.Crosscheck is implemented as part of the Picard-Tools suite, a set of Java command line tools for manipulating high-throughput sequencing data. It accepts VCF/BAM/SAM formatted inputs and can perform comparisons at the level of samples, libraries, read-groups, or files. Crosscheck is provided alongside a utility called ExtractFingerprints which for an input bam, outputs a VCF containing the genotypes and genotype likelihoods across all LD blocks within the supplied haplotype map. This VCF can be used to store fingerprints for downstream analyses or for use with Crosscheck. More information is available at For BAM mode, running Crosscheck requires approximately 2.5 gb RAM for a single input pair of BAMs. Runtime is dependent on the size of the input file. Based on our benchmarking experiments, runtimes are <10 min for DNAse-seq, <30 s for ChIP-seq, and are on average about 2 h for RNA-seq datasets. For VCF mode, Crosscheck requires approximately 2.5 gb of ram for a single pair of inputs, with runtimes <30 s using the standard hg19 haplotype map. CrosscheckFingerprints is multi-threading enabled in order to speed up comparisons and fingerprint generation when multiple input pairs are provided. All comparisons were conducted on Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80 GHz processors.11 phase 3(1000GP3) SNPs which are bi-allelic, phased, and have an MAF ≥10%. This MAF threshold is introduced since the expected maximal LOD contribution is obtained at an allele frequency of 0.50 . Additionally, SNPs must not differ in their MAF by more than 10% between the five ancestral sub-populations  present in 1000GP3. This is to correct for potential sub-population bias due to differing linkage and MAF frequency of SNPs across different populations. Using PLINK2 (ref. 18), we pruned SNPs meeting these criteria in order to create an independent set of “anchor” SNPs, between which no pairwise r2 correlation exceeded a threshold of 0.10. A window size of 10 kilobases (kb) and a slide of 5 SNPs were used for pruning. By creating this set of independent SNPs, we ensure that individual haplotype blocks are independent from each other. Next, we greedily added SNPs to the blocks represented by the anchor SNPs. Adding was done in order of LDScore19 of the anchor SNPs, with the highest LDScoring anchor SNP first . Recombination maps containing mappings between genomic coordinates and recombination rates for both the hg19 and GRCh38 assemblies were obtained from http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/ and http://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3/. We only added SNPs if their correlation with the anchor SNP has r2 ≥ 0.85 and they were located within a genomic window of 10,000 kb. In this way, we prioritize the creation of larger, more genetically informative blocks that span several kb regions. The haplotype maps used for the ENCODE database analysis and benchmarking, along with the python code used to generate them, are available at: https://github.com/naumanjaved/fingerprint_maps.Maps are constructed from 1000 Genomesr2 ≥ 0.85) to other SNPs, resulting in 20,792 SNPs. To construct the map containing only blocks with size ≥2 used to quantify the benefits of accounting for linkage, we subsampled the full haplotype map. Starting with the largest blocks by number of SNPs, blocks were successively added to this map until the total number of SNPs approximately reached the number of SNPs in the map containing only independent SNPs .The map containing only single SNP blocks was constructed by aggregating all SNPs in the full haplotype map not in strong linkage  from the ENCODE Tissue Expression (ENTEX) project. The ENTEX project contains datasets from experiments on samples derived from four different tissue donors, each of which has whole-genome sequencing (WGS) data available. The WGS data for each donor can be used to verify the nominal donor of each dataset comprising the testing set. For each dataset, the corresponding hg38 alignments were compared to the hg38 WGS alignments for its nominal donor. Only datasets that yielded a positive LOD score >5 using CrosscheckFingerprints (with the full hg38 version of haplotype map) and a “match” result from NGSCheckMate were included in the testing set. The final testing set of files and accompanying metadata are included in Supplementary Data 101 transcription factor and chromatin modifier (CM) ChIP-seq datasets without ground-truth annotation: To test Crosscheck and other methods on transcription factor and chromatin modifier datasets, we downloaded 101hg19 ChIP-seq datasets from the ENCODE project. For these datasets, there was no ground-truth donor sequencing data available for the nominal donor as there was for the ENTEX datasets. In this case, the false-mismatch rate  cannot be assessed, since there is a non-negligible probability that one of the two datasets with the same nominal donor annotation is incorrectly annotated. However, the FMR can still be assessed, since we estimate that the probability that two datasets with different donor annotations may actually share the same true donor is very low. Therefore, we only characterized the ability of NGSCheckmate and Crosscheckfingerprints to accurately classify donor-unmatched pairs for this testing set. In the context of detecting sample swaps, this performance measure is also more relevant than the accurate detection of donor-matched datasets. All datasets and accompanying metadata are available in Supplementary Data 20 and processed using Picard’s MarkDuplicates tool with default settings to remove duplicates. We noted that collapsing duplicates was especially important for RNA-seq datasets since PCR bias can alter allele fractions and lead to incorrect sample classification. Downsampling was conducted on the duplicate marked, sorted files using the command samtools view –s seed.F with a seed value of 5.Datasets were sorted using Samtools10 and created VCFs for each input file using the default provided SNP panel from the NGSCheckMate github and the command samtools mpileup-I -uf hg19.fasta -l SNP_GRCh37_hg19_woChr.bed sample.bam | bcftools call -c - >./sample.vcf. NGSC was then run in batch mode using default settings with the hg19 reference SNP panel. For Crosscheck, we first used Picard’s ExtractFingerprint utility with default settings and the standard hg19 haplotype map to pre-compute VCFs for each input bam. Comparisons were then conducted using Crosscheck’s batch mode with default settings and the standard hg19 map.To speed up analysis of a large number of bams with NGSCheckmate, we followed the author recommendations6. Without considerable modifications, HYSIS is therefore not suitable to handle the general use case that Crosscheck is intended for.HYSIS is intended for tumor-normal concordance verification with a priori knowledge of homozygous germline mutations in the normal tissue7. Bam-matcher calls programs such as GATK21 to call variants for each input BAM. Though the resulting variants can be cached to speed up future comparisons, we did not find a way to easily call and store variants for each input bam in the testing set, and without that, performing the hundreds of thousands of benchmarking comparisons becomes unfeasible.Bam-matcher is a tool intended for verifying genotype concordance for whole-genome sequencing, whole-exome sequencing, and RNA-sequencing data–min-cov parameter set to 1. Pileups were pre-generated using GATK 4.1.7.0 with the recommended settings8. BAMixChecker was run with standard settings for hg19 (ref. 9) and using GATK 4.1.6.0 for variant calling. Conpair outputs a genotype concordance percentage, which should be <50% for different donor and above 80% for same donor datasets. Any genotype concordance between 50 and 80% is considered inconclusive. BAMixChecker outputs a concordance score between 0 and 1 with no explicit inconclusive range. However, we found that BAMixChecker outputs a concordance score of exactly 0 when there is no overlap between the SNP reference panel that the program uses and the input dataset. Therefore, we labeled any result from BAMixChecker with a concordance score of 0 as an inconclusive genotype call. We found that both methods were unable to yield a conclusive result for more than 25% of the comparisons even when the full datasets are considered, and the inconclusive rates became even higher at the lower subsampling rates . Datasets for the following accessions were downloaded: SRR8505344, SRR8505340, SRR8505343, SRR1258219, SRR1258220, SRR1258218, and SRR8505347. Fastqs were aligned to the GRCh38 reference using STAR23 2.6.0c with default parameters. Before analysis, bams were sorted using samtools and duplicate marked/collapsed using Picard’s MarkDuplicates. All comparisons were conducted using the default settings and SNP panels for the GRCh38 assembly for each method.Paired fastqs for RNA-seq data from CEPH/Utah Pedigree 1463 were downloaded from the Gene Expression Omnibushttps://www.encodeproject.org/. Metadata were filtered to yield accessions for hg19 ChIP-, RNA-, and DNase-seq ENCODE bams from donors with at least four datasets. These bams were downloaded from a Broad google bucket and processed (see below) with a custom Workflow Description Language24 script. All dataset accessions and associated metadata are available in Supplementary Data ENCODE metadata was downloaded from samtools sort, and filtered using BEDTools25 in order to only keep reads overlapping SNPs in the haplotype map. This facilitated efficient storage of files, resulting in approximate 10-fold reduction in file size. Finally, duplicates were marked and removed for each file using Picard’s MarkDuplicates function with default settings. All comparisons were conducted using the version of CrosscheckFingerprints available in commit #078b0ba of Picard.Files were first sorted using To detect mislabeled samples, each dataset is compared against a reference set of three samples that provide a high-quality representation of the “true” genotype for each ENCODE tissue donor. To construct this reference set of samples, a self-LOD score is calculated for each sample by “comparing” each file to itself. This score correlates with the dataset’s overlap with the haplotype map, and the highest self-LOD samples are those containing the most genetic information relevant for genotyping. To ensure that the reference set of samples for each tissue donor does not contain any swapped samples, all reference samples are compared against one another to ensure self-consistency, which is defined as an LOD score greater than 5 for all three pairwise comparisons between the three samples. In the case of one swapped sample in this reference set, two negative LOD scores and one positive LOD score will be obtained. In this case, the next highest self-LOD scoring bam replaces the putative swap, and representative concordance is re-assessed. This is repeated until a concordant set is found. More complex patterns of swaps in the representative set are assessed on a case-by-case basis. Finally, all reference samples across all nominal donors are compared against one another in order to identify larger cross-donor swaps and preclude the possibility that all reference samples for a nominal donor are actually swaps from the same true donor.Each sample not in the reference set is compared against the top three representative samples for its nominal donor. Samples yielding an LOD ≤ −5 against any of the top three representatives are flagged as swaps for review, while those yielding an LOD score between −5 and 5 are flagged as inconclusively genotyped.Varying numbers of randomly sampled reads from two unrelated ENCODE ChIP-seq datasets, ENCFF005HON ENCFF007DFB, were mixed together to create simulated contaminated datasets. Each mixed sample consisted of ~5 million reads and contained varying proportions of the original datasets . Mixed samples were then compared to ENCFF007NTA and ENCFF029GAR, which are ChIP-seq datasets from the same donor as ENCFF005HON. Comparisons were conducted on VCF files generated using Picard’s ExtractFingerprint utility using Crosscheck’s VCF mode with default settings.Further information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Cronartium ribicola has resulted in substantial economic losses and ecological damage to native North American five-needle pine species. The high susceptibility and mortality of these species, including limber pine (Pinus flexilis), creates an urgent need for the development and deployment of resistant germplasm to support recovery of impacted populations. Extensive screening for genetic resistance to WPBR has been underway for decades in some species but has only started recently in limber pine using seed families collected from wild parental trees in the USA and Canada. This study was conducted to characterize Alberta limber pine seed families for WPBR resistance and to develop reliable molecular tools for marker-assisted selection (MAS). Open-pollinated seed families were evaluated for host reaction following controlled infection using C. ribicola basidiospores. Phenotypic segregation for presence/absence of stem symptoms was observed in four seed families. The segregation ratios of these families were consistent with expression of major gene resistance (MGR) controlled by a dominant R locus. Based on linkage disequilibrium (LD)-based association mapping used to detect single nucleotide polymorphism (SNP) markers associated with MGR against C. ribicola, MGR in these seed families appears to be controlled by Cr4 or other R genes in very close proximity to Cr4. These associated SNPs were located in genes involved in multiple molecular mechanisms potentially underlying limber pine MGR to C. ribicola, including NBS-LRR genes for recognition of C. ribicola effectors, signaling components, and a large set of defense-responsive genes with potential functions in plant effector-triggered immunity (ETI). Interactions of associated loci were identified for MGR selection in trees with complex genetic backgrounds. SNPs with tight Cr4-linkage were further converted to TaqMan assays to confirm their effectiveness as MAS tools. This work demonstrates the successful translation and deployment of molecular genetic knowledge into specific MAS tools that can be easily applied in a selection or breeding program to efficiently screen MGR against WPBR in Alberta limber pine populations.Since its introduction to North America in the early 1900s, white pine blister rust (WPBR) caused by the fungal pathogen  Pinus flexilis James) is a native five-needle pine in western North America, naturally distributed from British Columbia and Alberta in Canada to southern California in the USA. Despite limited economic value, limber pine is a keystone species for high elevation montane ecosystems. Because of the species’ high tolerance to drought, high winds, and exposure, individual trees can live for over 1,000 years in harsh environments where few other conifers are distributed. In these environments limber pine provides essential ecosystem services, including slope stabilization, headwater streamflow control, and subalpine tree island formation. They provide cover that allows less exposure-hardy plants to establish and grow and provide both shelter and a nutritious food source for wildlife such as birds, bears, and small mammals , outbreaks of native mountain pine beetle (Dendroctonus ponderosae Hopkins), and habitat loss resulting from changes to fire regimes and climate change. C. ribicola causes white pine blister rust (WPBR) in five-needle pines all around the world. Because of its high susceptibility to C. ribicola, in environments suitable for the pathogen, over 60 percent of limber pine trees in Alberta were observed to be infected during 2009 surveys in Canada, and WPBR caused over 40 percent mortality in many regions .A species recovery plan has been prepared and executed for limber pine conservation and restoration in Alberta . Geneticd as Cr4 . Recentld as Cr4 , in stanTraditional breeding approaches are effective in selecting and testing for genetic resistance to WPBR, but they can take decades to identify sufficient numbers of resistant parent trees and reliably produce large cone crops. In addition, it can be difficult using conventional methods for tree breeders to capture and transfer genetic variability of a suite of complex traits together, such as host resistance to pathogens and pests and other related adaptive traits. Genetic dissection of complex phenotypic traits is thus still an obstacle in forest genetics. In recent years, genomic resources have been developed by next generation sequencing approaches in a few five-needle pine species, including genome-wide marker discovery , high-deCr4 on the Pinus consensus linkage group 8 (LG8) (Genotyping a subset of in-silico SNPs anchored  8 (LG8) . Genes w 8 (LG8) . Fine-sc 8 (LG8) . These g 8 (LG8) .Cr4 known to occur in limber pine or whether they co-locate with other R genes. We aim to develop specific SNP markers that can be used for operational assays for MGR selection in breeding programs of limber pine to increase the frequency of WPBR-resistant genotypes.Here, we report an association study using unrelated open-pollinated limber pine seed families originating from Alberta to determine whether families expressing phenotypic signs of MGR have genotypic MGR markers. We also determine whether putatively resistant genotypes co-locate with previously identified Ten Alberta open-pollinated seed families  and nineC. ribicola life cycle, only basidiospores are able to infect needles of five-needle pine species. They develop on the underside of Ribes spp. leaves  during cool, wet weather conditions in late summer or early fall and infect the needles of five-needle pines. Ribes spp. leaves infected with a heterogenous mixture of C. ribicola genotypes were collected from multiple field locations in Oregon in the fall of 2016 and 2017. Since no virulent pathotype capable of overcoming limber pine MGR has yet been detected . Basidiospore germination averaged 99% for the trials. Seedlings remained in the inoculation chamber for about 48 h to promote spore germination and needle infection. After removal from the inoculation room, seedlings from the 2016 inoculation were then transferred to a greenhouse for the duration of the trial, while the seedlings from the 2017 inoculation were subsequently planted outdoors in boxes.Six and 18-month-old seedlings were inoculated in the fall of 2016 and 2017, respectively, using standard Dorena Genetic Resource Center protocols . InoculaDisease development in the 2016 trial was assessed a total of 7 times during the period from October 2016 to December 2017. Each seedling was evaluated for disease symptoms on needles and stems including presence/absence of needle spots (lesions), spot types and number, as well as necrotic or abscising needles, presence/absence of cankers, aecia, and spermatia. Resistant or susceptible phenotypes were determined based on stem canker development as described previously . For the2 tests by comparing phenotypic segregation of the same families inoculated in the two different years or with that from a previous report  was constructed between all possible pairs of SNP loci with minor allele frequency (MAF) >5%. Association of markers with the resistant phenotype in the Alberta seed families was performed using a general linear model (GLM) and a mixed linear model (MLM) with the TASSEL software version 5.0  were calculated and used to explain the proportion of phenotypic variation explained by each SNP locus. Associated SNPs and polymorphic genes were annotated based on their homologies to the available databases  using BLAST2GO (The extent of linkage disequilibrium (LD) was investigated along LG8, and a matrix of the squared allele frequency correlations  approach using the GMDR software package  to studyBased on results of the association analysis performed above, the top SNP loci with strong phenotypic association and potential R gene functional candidates were selected for design of TaqMan assays Table S32 tested p values ranging from 0.68 to 1) or 3:1 for the family PB #2 (χ2 tested p = 0.13) . PB #2 was not included in the 2017 inoculation trial, but its phenotypic segregation ratio from a 2014 inoculation (2 tested p = 0.80). This consistency of MGR phenotypic segregation indicates that there was no obvious environmental interaction for MGR phenotypic expression across Alberta MGR seed families.In a comparison of the 2016 and 2017 inoculation trials with greenhouse and outdoor conditions respectively, segregation ratios were highly consistent for families #7027, #6470, and #6665  was detected for the SNP pairs in the same genes or in different genes located at the same genetic position on LG8. Average 2r between two SNP loci separated by distances less than 10 cM was 0.96 in the family PB #2 and 0.25 across the four families. The high average 2r values at less than 10 cM indicate that the association mapping was powerful enough to detect causal loci at a relatively long distance in these limber pine populations using multiple seed families. A scatterplot of LD decay over genetic distance (cM) was generated to estimate the LD decay trend. Nonlinear regression revealed that LD decay was rapid as distances between the two markers increased  when the distance was over 25 cM . The resistance-associated SNPs were distributed in 17 genes localized on LG8 from positions 18.33 cM to 156.48 cM. Based on the LG8 genetic map updated in the present study  or marker-Rsq  on a genome scale with marker positions (cM) across LG8 , followed by M287456-634R and M287456-700R with 2r = 0.730  detected a total of 23 SNP loci Table S4Cr4, indicating that the R locus was the same (Cr4) or very closely positioned in Canadian and USA seed families despite the large geographic distance between them.Association analysis across all four open-pollinated seed families detected 18 SNP loci within 13 genes in significant association with the resistant phenotypes Table S5Cr4 , a one‐gene model was first identified with the SRBP gene (M304083Y) for MGR; it scored 10/10 for cross‐validation consistency. A two-gene interaction was identified between SRBP (M304083Y) and DCTD (M286987Y), while a three-gene interaction was identified among SRBP (M304083Y), NBS-LRR (M287456-700R), and 6PGDH (M259257Y) which scored 9/10 and 7/10 for cross‐validation consistency, respectively. The 10-fold cross validation for significance test showed all top tree models with  = 0.001 Table S7GMDR analysis calculated interactive variables in the score distributions, with a positive score for the MGR resistant group and a negative score for the susceptible group (2r) across all four families and M287456 was a NBS-LRR candidate, SNP loci within both genes were selected to design TaqMan assays  was much slower than in diversity panels as reported in other conifers , or different R gene alleles positioned very close to each other.Association study results are commonly affected by the diversity of sampled individuals and population size. While previous studies assessed populations with <100 samples , we sampC. ribicola cells. Most well characterized R proteins in molecular plant-microbe interactions belong to the superfamilies of NBS‐LRR and receptor-like protein kinases (RLK), acting either as intracellular or cell-surface receptors for reorganization of pathogenic effectors  genome (P. monticola ex D. Don) transcriptome  genome  and westcriptome , suggestlinc1 mutants showed basal immunity towards Pseudomonas syringae and enhanced resistance to Botrytis cinerea infection  through signal transduction networks . A serienfection .C. ribicola infection, including one exclusively responsive in the incompatible Cr2-avcr2 interaction  WP-BR interactions (Cr2-avcr2) WP-BR interactions  . Due to low level expression of the NBS-LRR gene, only a partial fragment of M287456 was assembled in the limber pine transcriptome. Our previous exome-seq analysis mapped over 600 NBS-LRR gene sequences in limber pine and most of them were mapped in clusters throughout the genome. However, few NBS-LRR genes were mapped in the 4 region . The NBS4 region . We also(RenSeq) . CoupledSelection of favorable alleles or allelic combinations is a lengthy process requiring breeding of outcrossing conifer species. In genotypic selection focused on a few genes with large phenotypic effects (such as MGR), application of MAS tools will enable shortened breeding cycles by allowing precise prediction of phenotypes for those genes. In limber pine breeding, ideal MAS tools will allow testing of needles of parent trees in the wild to determine if they have MGR, minimizing the need to wait to collect a viable seed crop, grow and inoculate seedlings, or at least reducing the number of seed families that must be tested to find MGR. Another important use of MAS tools for MGRs is for genotypic confirmation to support pyramiding of multiple resistance mechanisms. Some types of QDR may also exhibit stem symptom-free traits. Infection by virulent pathotypes may negate the MGR and allow observation of QDR among seedlings. Therefore, MAS tools may be applied to within-family selection for joint MGR and QDR as well as to forward selection in seed orchards when incorporating new parents that have both MGR and QDR genotypes.MAS tools are too narrow for general use in more complex traits which are controlled by multiple genes. Although modeling of multiple gene interactions may expand the application of MAS tools to larger areas with more complex genetic backgrounds, genomic selection is a more attractive approach for breeders to select and predict complex traits with minor gene effects. Genetic characterization of genes or QTLs contributing to various complex traits of interest is an essential prerequisite for genomics-based breeding. GWAS and QTL identification for disease resistance are still relatively rare in five-needle pines. When SNPs underlying important quantitative traits are available from future GWAS and QTL mapping, their conversion into similar genotyping arrays will be useful for genomic selection in breeding programs. Our MAS tools and genotyping arrays, as well as previously reported genetic maps, offer valuable resources for future GWAS, QTL mapping, and genomic selection of these complex traits in limber pine and other related five-needle pines.Cr4, these genotyping arrays can be applied to any limber pine families to search for additional R genes. Novel R genes will be confirmed in those families if all Cr4-linked SNPs of the genotyping arrays show no association with resistant phenotypes.Future analysis aimed at assaying genetic variation across the whole genome is likely to facilitate a much more comprehensive elucidation of the genetic basis of phenotypic variation. Over 150 additional Alberta limber pine families from parents in heavily infected stands are currently under evaluation for WPBR resistance. As resistance related phenotypic data becomes available from conservation and breeding programs in the coming years, it would be interesting to expand the study to a wider geographical area where other resistance mechanisms may be present. Because the Sequenom-based SNP genotyping arrays developed here were verified for their application in association mapping of All relevant data is contained within the article. Gene sequence data for design of Sequenom iPLEX MassARRAY are shown in J-JL, RAS, RS, and JK conceived the research. RS, GA, JK, and RAS provided the genetic materials. RAS, RZ, and AK conducted the disease ratings and analyzed phenotypic data. J-JL, HW, and AZ conducted SNP genotyping and analyzed genotypic data. AS shared SNP data and some genotypes of the limber pine species for the study. J-JL wrote the original draft with contributions from all authors. J-JL and RAS supervised the project. All authors contributed to the article and approved the submitted version.This research was supported in part by the CFS-Pest Risk Management Program and funding from Waterton Lakes National Park of Canada.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.


Campylobacter is the leading cause of bacterial foodborne gastroenteritis worldwide. Handling or consumption of contaminated poultry meat is a key risk factor for human campylobacteriosis. One potential control strategy is to select poultry with increased resistance to Campylobacter. We associated high-density genome-wide genotypes (600K single nucleotide polymorphisms) of 3000 commercial broilers with Campylobacter load in their caeca. Trait heritability was modest but significant (h2 = 0.11 ± 0.03). Results confirmed quantitative trait loci (QTL) on chromosomes 14 and 16 previously identified in inbred chicken lines, and detected two additional QTLs on chromosomes 19 and 26. RNA-Seq analysis of broilers at the extremes of colonisation phenotype identified differentially transcribed genes within the QTL on chromosome 16 and proximal to the major histocompatibility complex (MHC) locus. We identified strong cis-QTLs located within MHC suggesting the presence of cis-acting variation in MHC class I and II and BG genes. Pathway and network analyses implicated cooperative functional pathways and networks in colonisation, including those related to antigen presentation, innate and adaptive immune responses, calcium, and renin–angiotensin signalling. While co-selection for enhanced resistance and other breeding goals is feasible, the frequency of resistance-associated alleles was high in the population studied and non-genetic factors significantly influenced Campylobacter colonisation. Campylobacter caused 95 million illnesses, 21,000 deaths and loss of 2.1 million disability-adjusted life years globally in 20101. In the United Kingdom alone, there were 63,946 laboratory-confirmed human cases in 2017, most of which were due to C. jejuni. The ratio of unreported cases of human campylobacteriosis to those captured by national surveillance in the United Kingdom is estimated to be 9.3 to 12, therefore the true burden may exceed half a million cases per annum at a projected median cost to the national health service of £50 million3. Human infections typically involve acute gastroenteritis, however debilitating sequelae may occur including Guillain–Barré syndrome (GBS) and other inflammatory neuropathies4. Poultry are the main reservoir of human campylobacteriosis and up to 80% of human cases may be attributable to the avian reservoir as a whole6. During 2016–2017, a United Kingdom-wide survey indicated that 54% of fresh retail chicken was contaminated with viable Campylobacter7. The number of C. jejuni in the caeca of chickens frequently exceeds 108 colony-forming units/gram and carcass contamination at slaughter can be difficult to avoid9. Quantitative risk assessments predict that even a relatively modest 2 log10 reduction in the number of Campylobacter on broiler carcasses could reduce the incidence of human disease due to contaminated chicken by up to 30-fold10. A pressing need therefore exists for strategies to reduce the entry of Campylobacter-contaminated poultry meat into the food chain.Human campylobacteriosis exerts profound societal and economic costs. The World Health Organisation estimated that Campylobacter in poultry are lacking, much interest exists in the potential for breeding chickens with improved resistance to intestinal colonisation by C. jejuni. The scope for genetic control of Campylobacter in poultry has been demonstrated in commercial broiler lines that vary in resistance17 and inbred layer lines exhibiting heritable differences in C. jejuni colonisation following experimental inoculation19. Breeding for Campylobacter resistance may also benefit avian intestinal health and productivity. In some commercial broiler chicken lines, experimental inoculation of C. jejuni elicits damage to the intestinal mucosa, diarrhoea and impaired weight gain21. In some birds, Campylobacter colonisation may impact intestinal barrier function22, nutrient absorption and transporter expression23, the translocation of Escherichia coli to extra-intestinal organs24, and may lead to a dysbacteriosis. In other studies, where Campylobacter inoculation was via natural exposure mimicking field exposure, there was no association between Campylobacter levels and bird growth rate25 or gut pathology26, and selection for Campylobacter resistance remained compatible with other breeding goals26.As effective vaccines and treatments for pre-slaughter control of C. jejuni intestinal colonisation, where phenotypes were analysed as a binary trait in a novel dual-purpose chicken breed, revealed a resistance-associated locus on chromosome 11 near the CDH13 gene encoding T-cadherin, and a second candidate locus on chromosome 5 was identified close to calmodulin (CALM1), a calcium-activated modulator of cadherin function17. Both genes differed in relative expression in a manner associated with resistance17. Studies in inbred layer lines 61 and N found heritable differences in caecal colonisation by various C. jejuni strains18. Initial low-resolution mapping studies using reciprocal (61♀ × N♂) and (N♀ × 61♂) F1 crosses and the progeny of an (N♀ × 61♂) F1♂ x N♀ backcross indicated that resistance was controlled by a single autosomal dominant locus18, but subsequent analysis of a backcross population using 1243 fully-informative single nucleotide polymorphism (SNP) markers revealed quantitative trait loci (QTL) on chromosomes 7, 11 and 1419. Using a ninth-generation advanced intercross (61xN) line and a 600K genome-wide SNP array, the location of the QTL on chromosome 14 was confirmed and refined, and additional QTLs were identified on chromosomes 4 and 16, indicating potential involvement of the Major Histocompatibility Complex (MHC) region19. QTL for resistance of chickens to enteric carriage of Salmonella have been reported at the same regions of chromosome 1427 and 1629 that were implicated in host resistance to C. jejuni colonisation. Analysis of caecal gene expression in chicken lines of varying susceptibility to Campylobacter colonisation has identified transcriptional signatures associated with differential resistance, including genes influencing the immune response16.A previously published genome-wide association study (GWAS) on Campylobacter naturally present in the litter. The genomic architecture of resistance was analysed using imputed high-density SNP genotyping, and resistance was further characterised by transcriptome analyses of intestinal tissue from birds of the predicted resistant or susceptible genotypes at the corresponding extremes of caecal Campylobacter colonisation phenotype. Our data provide valuable insights into the prospects for genetic control of Campylobacter in poultry.In the present study, we conducted a comprehensive genome-wide association study on a commercial pedigree broiler population exposed to C. jejuni ± standard deviation of 7.057 ± 1.023 log10 colony-forming units (CFU) per gram (g), with a maximum of 10.64 log10 CFU/g and minimum of 2 log10 CFU/g, equal to the limit of detection by direct plating. We were unable to detect Campylobacter by direct plating for just 10 out of 3000 caecal samples (0.003%). Sex had a marginal effect on Campylobacter levels (P < 0.05), with males (n = 2141) having a higher Campylobacter load (7.178 ± 0.034 log10 CFU/g) compared to females . Campylobacter levels showed seasonal variability, with date of sampling having a significant effect (P < 0.05), while body weight did not have a significant effect on the trait. No significant maternal effects were identified. Modest, but statistically significant heritability for caecal Campylobacter level was estimated (h2 = 0.11 ± 0.03).In the 3000 commercial broilers sampled, we estimated a mean number of caecal From 50K SNP genotype data obtained for 3000 broilers, genotypes were imputed to 600K SNP for 2718 birds. 282 samples failed the pedigree integrity testing prior to imputation and were removed. Imputation was not possible on chromosome 16 due to the complexity of the MHC region. These data were analysed using two genome-wide association methodologies: a genome-wide association study (GWAS) for single SNPs and regional heritability mapping (RHM) to consider associations with genomic regions comprising multiple consecutive SNPs. Using the SNP data on chromosome 16 the MHC haplotypes of these birds were assembled and a haplotype analysis was performed.Campylobacter levels, after a Bonferroni correction for multiple testing, are shown in Table Campylobacter in the caeca at the genome-wide level and another ca. 100 SNPs on the same chromosome with a suggestive genome-wide significant association with the trait. All the significant SNPs were in high linkage disequilibrium (LD) and located within the same LD block (length 230 kb), in the same MHC region  was detected at much higher frequency , compared to the other two . The ensuing MHC haplotype analysis identified statistical significant (P < 0.05) associations between the MHC haplotypes and the log-transformed number of Campylobacter in the caeca. The prevalent MHC haplotype AA was associated with colonisation resistance accounting for 1 log10 difference in the Campylobacter levels compared to the susceptible BB haplotype; a significant favourable dominance deviation was also observed for the heterozygous haplotype AB  plots for the GWAS results using the 50K and the imputed 600K arrays are shown in Fig. 2 = 0 between AX-75778268 and AX-75798135 markers; on chromosome 26: r2 = 0 between AX-76346701, AX-76339934 markers). The GWAS analysis using the imputed 600K SNP array information confirmed the associations on chromosomes 14 and 26.Additionally, one SNP with a genome-wide significant association and another one, located 1.4 Mb away, with a suggestive association were identified on chromosome 26. Similarly, two SNPs identified on chromosome 14, albeit 7.5 Mb apart from each other, crossed the suggestive genome-wide significant threshold. The significant SNPs on chromosomes 14 and 26 were not in LD (on chromosome 14: r10 CFU/g), while the SNPs on chromosome 26 had a significant dominant effect (ranging from 0.3 to 0.4 log10 CFU/g) on Campylobacter levels. The significant SNPs in the QTL region on chromosome 16 accounted for 6% of the phenotypic variance, while collectively all the SNPs in the three candidate regions accounted for 17% of the phenotypic variance of caecal Campylobacter levels.The additive and dominance genetic effects, and the proportion of the total phenotypic variance explained by each of these SNPs are summarised in Table A Manhattan plot and Q–Q plot for the RHM analysis are shown in Supplementary Fig. TRIM10 (tripartite motif 10), TAP1 (transporter associated with antigen processing 1), RACK1/GNB2L1 (receptor for activated C kinase 1), TRIM27 (tripartite motif 27), and TRIM32 (tripartite motif 32). The SNP on chromosome 19 corresponds to a synonymous variant within HIP1 (huntingtin interacting protein 1), and the SNP on chromosome 26 corresponds to a missense variance within ADORA3 (adenosine receptor 3). Details of the SNP annotation are presented in Supplementary Table Most of the significant SNPs identified in the GWAS analysis were located upstream (40%) or downstream (34%) of predicted genes or within introns (19%). However, eight of the SNPs on chromosome 16, one SNP on chromosome 26 and one SNP located within the significant RHM SNP-window on chromosome 19 were found within exons. The exonic variants on chromosome 16 were located within Campylobacter levels contained a relatively small number of genes, collectively comprising 173 annotated protein-coding genes, 7 microRNAs and 2 snoRNAs. Details of the genes and non-coding RNAs located in the candidate regions are presented in Supplementary Table The candidate QTL regions for caecal 30, we performed RNA-Seq analysis of the caecal tonsil transcriptome of 23 broilers to identify expression QTLs (eQTLs) and potential allelic imbalance of candidate genes within the regions associated with Campylobacter resistance identified by GWAS and RHM analyses. The birds used were selected to represent combinations of predicted resistant or susceptible genotypes (based on the identified QTLs) that were correspondingly at the extremes of caecal C. jejuni colonisation phenotype , as well as the average (n = 7) of measured caecal Campylobacter load  correction for multiple testing, 3 protein-coding genes were found to exhibit significant differential expression . The BF2 gene is an MHC class I gene while the other two are both BG-like genes belonging to the butyrophilin family. In order to identify more subtle patterns of differential expression, a relaxed significance threshold of unadjusted P value of 0.001 was implemented. A total of 33 genes exhibited differential expression between high-, average-, and low-colonised birds at this threshold . Interestingly, there were three DE BF2 transcripts, two of which were downregulated  and the other upregulated (ENSGALT00000087837) in the resistant birds. There were also two DE BF1 transcripts, both of which were upregulated in the resistant birds.After false discovery rate  correction for multiple testing, we detected 102 significant cis-eQTL (Supplementary Table BG1) . Another three significant cis-eQTLs were detected within TMEM11 (transmembrane protein 11) and the COPS3 , two genes located within the QTL region on chromosome 14   Fig. A. This ely) Fig. .Figure 3trans-eQTLs within the QTL region on chromosome 19 and two on chromosome 26 (Supplementary Table trans-acting elements are for genes related with metabolic processes. The trans-acting SNP on chromosome 26 is for a microRNA (gga-mir-1553) located on chromosome 7, close to the peak of a previously identified QTL for C. jejuni resistance using a back-cross population of inbred lines 61 and N19.We detected a total of 13 significant cis-acting SNP it is expected that the two alleles of the gene will be expressed unequally causing allelic expression imbalance. To verify the cis-QTLs detected above, and identify additional ones, we identified genetic variation within the QTL regions identified by the GWAS and RHM using the RNA-Seq data and performed allele-specific expression (ASE) analysis for all the SNPs located within these regions. Several significant ASE events were identified in all QTL regions . Specifically, 14 significant ASE events were identified for 3 genes located on chromosome 14, 30 for 6 genes on chromosome 16, 11 for 2 genes and one microRNA (gga-mir-142) on chromosome 19, and 35 for 5 genes on chromosome 26 (Supplementary Table CLCN7). ASE results on chromosome 16 were consistent with the presence of a cis-acting polymorphism in BG1, with 6 SNPs in high-LD showing allelic imbalance (P < 10–13). Moreover, ASE analysis highlighted potential cis-acting polymorphisms for other genes of interest in the region, namely MHC class 1 (BF1 and BF2) and class 2 (BLB2 and BLB1) , C–C motif chemokine ligand 4 (CCL4), complement C3b/C4b receptor 1-like (CR1L), C4b-binding protein (C4BP), polymeric immunoglobulin receptor (PLGR) and BCL2 antagonist/killer 1 (BAK2).If an individual is heterozygous for a BF1, BF2, ENSGALG00000024357 and ENSGALG00000032221), were found to be differentially transcribed in the caecal tonsils of birds of predicted resistant or susceptible genotypes with divergent caecal Campylobacter load, after adjusting for sex and seasonality. This confirmed that the expression of each gene differed significantly between resistant and susceptible birds (P ≤ 0.05 after Tukey’s HSD post-hoc test adjustment). The correlation estimates (r2) between the quantified by qRT-PCR and RNA-Seq expression levels of BF1, BF2, ENSGALG00000024357 and ENSGALG00000032221 were 0.85, 0.78, 0.83 and 0.71, respectively   found these genes to be enriched for pathways involved in innate and adaptive immune responses, antigen presentation, inflammatory responses, calcium signalling, renin–angiotensin signalling, epithelial cell signalling and interactions , MHC-related genes  and calcium (Ca+) regulated genes. We subsequently extracted the gene ontology terms for each of these genes and performed functional annotation clustering analysis. The genes were organised into 41 clusters, each given an enrichment score (ES). The first (ES = 4) and the second (ES = 3.5) clusters were both enriched for genes functionally annotated as involved in ‘antigen processing and presentation via MHC class I and class II molecules’   for one gene cluster related with immune response, defence response, response to stress, symbiosis, encompassing mutualism through symbiosis, interspecies interactions between organisms.Functional analysis of the DE genes using the IPA software showed significant enrichment for pathways related with immune response  and metabolism . Moreover, one network of molecular interactions related to ‘cell death and survival, and organismal injury and abnormalities’ was constructed based on the DE genes. A core gene in this network was Campylobacter colonisation and evaluate the potential for selective breeding of poultry with enhanced resistance to control Campylobacter at farm level. Using samples from 3000 commercial chickens exposed to Campylobacter, we detected heritable variation associated with caecal Campylobacter levels and identified genomic markers and regions associated with colonisation that can be used to inform breeding decisions. Candidate genes, cis- and trans- acting elements, canonical pathways and networks, and MHC haplotypes that were implicated in resistance to Campylobacter colonisation were also identified.We sought to investigate the genetic basis of resistance of chickens to 2 = 0.11) for caecal Campylobacter colonisation. This was lower compared to a previous estimate for this trait using the progeny of crosses of inbred White Leghorn chicken lines with differing resistance to Campylobacter colonisation (h2 = 0.25)19. The difference is likely attributable to the use of field data on naturally colonised broilers in the present study compared to experimental challenge of inbred lines. The heritability of resistance to Campylobacter colonisation is similar to that observed for other livestock pathogens and diseases, such as bovine tuberculosis (h2 = 0.09–0.17)32 and bovine and ovine mastitis (h2 = 0.10–0.20)33, where the development of genetic evaluations to guide breeding decisions was deemed to be feasible.We estimated significant heritability  gene, major (BF2) and minor (BF1) MHC class I genes, the major (BLB2) and minor (BLB1) MHC class II beta chain genes. In addition, the BF2, BF1, BG1 and two BG-like genes were found to be differentially expressed in chickens with different caecal Campylobacter levels. The major MHC class II beta chain gene (BLB2) gene is widely expressed at high levels in hematopoietic cells, whereas the minor MHC class II beta chain gene (BLB1) is generally poorly expressed, although highly expressed in spleen, intestinal epithelial cells, and particularly the caecal51. The CIITA transactivator gene that controls expression of MHC class II genes52 has been found to be differentially expressed in chickens with high and low C. jejuni colonisation levels in a previous experimental RNA-Seq study of the caecal tissue16. Previous human studies have associated MHC class II alleles as genetic risk factors for inflammatory neuropathies secondary to C. jejuni infection53. C. jejuni can increase surface expression of MHC class II on murine bone marrow-derived dendritic cells and activate them to induce T cell responses54, however the extent to which this applies in chickens is ill-defined. In that study, MHC class I genes BF1 and BF2 were also found to be differentially expressed in the caeca of chickens with high and low C. jejuni levels16. Furthermore, similar to our findings, MHC-related BG genes have also been reported to be differentially transcribed in the spleen of two chicken lines that differ in susceptibility to C. jejuni colonisation. The BG region of MHC is very repetitive, and it is therefore difficult to distinguish specific BG genes due to copy number variation55. In mammals, gammadelta (γδ) T cells recognize a wide variety of ligands, among members of the butyrophilin family encoded by BG genes, and recent studies suggest that the same may apply in chickens56. In mammals, γδ T cells enriched in the intestinal epithelium and play a key role in control of mucosal immunity and inflammation57. Indeed, human γδ T cells proliferate after exposure to C. jejuni and have been implicated in GBS following C. jejuni infection58. The role of γδ T cells and recognition of BG ligands during Campylobacter infection in poultry therefore merits further study.Despite the MHC region being polymorphic and repetitive, making it challenging to identify causative genes and mutations underlying disease resistance, our analyses revealed a number of candidate genes for Campylobacter infection of avian cells59 and chickens in a breed-dependent manner60, and may underlie breed-specific differences in gut inflammation and pathology61. Furthermore, multiple interferon-related genes were found to be differentially expressed in the caecal transcriptome of chickens with high and low C. jejuni colonisation levels in a previous RNA-Seq study16. Interestingly, the major class I and II molecules, as well as other MHC related genes have interferon response elements (IREs) in their promoters which respond to IFN-γ and therefore their differential expression may be subject to interferon regulation. Furthermore, our recent analysis of whole genome sequence of commercial broilers and layers, and the transcriptome of isolated macrophages from a broiler-layer F2 sibling backcross, also revealed substantial differences in the expression of interferon-regulatory factors (IRF) family members as well as in the BLB1 and BLB2 genes between individual birds62 that could underlie this phenotype.In the present study, network analyses detected interferon signalling among the pathways associated with resistance. Interferon-γ (IFN-γ) has been reported to be induced following Campylobacter resistance using a backcrossed ([61 × N] x N) population of inbred lines 61 and N19. One of these QTLs (located at 12 Mb) overlaps with a QTL identified for the same trait using a ninth generation advanced intercross population of these lines19, as well as a QTL for resistance to Salmonella colonisation in chickens27, suggesting that a mechanism of resistance common to both pathogens may exist. CREB binding protein (CREBBP), a key immune regulatory protein implicated in Salmonella resistance in chickens63, lies in close proximity to the marker of this QTL. In the present study, a mutation  with a predicted high impact on the encoded protein of this gene was significantly (P < 0.05) associated with Campylobacter colonisation resistance (data not shown). Furthermore, pathway analysis in the present study confirmed the enrichment for CREB signalling reported previously in inbred lines19. The other QTL region (located close to 5 Mb) on chromosome 14 in the present study overlapped with an expression QTL for Campylobacter resistance. Specifically, the SNP marker significantly associated with Campylobacter resistance was also a cis-acting element for two genes (upregulates COPS3 and downregulates TMEM11). The protein encoded by COP9 signalosome complex subunit 3 gene possesses kinase activity that acts as a site for complex phosphorylation of many regulators involved in signal transduction such as I-kappa-B-alpha, p105, and c-Jun64. This protein is part of a complex that plays a key role in diverse cellular processes including cytokine signalling and antigen induced responses65.The present study identified two distinct QTLs on chromosome 14, both located within the interval of a previously identified QTL for PLGR) which is highly expressed in intestinal epithelial cells and mucosa, and plays a crucial role in the transcytosis of polymeric soluble immunoglobulins and immune complexes to the gut mucosal surface66. Genes and pathways involved in the immunoglobulin production and function were reported to be upregulated in chickens relative resistance to C. jejuni16. PLGR has been associated with intestinal immune defence against the lumen-dwelling parasite Giardia in mice67. The QTL for Campylobacter resistance identified on chromosome 26 encompasses the calcium/calmodulin-dependent protein kinase IG which belongs to a calcium-triggered signalling cascade and phosphorylates the transcription factor CREB (https://www.uniprot.org/uniprot/Q96NX5). A previous GWAS study of C. jejuni resistance identified a suggestive significant association proximal to the calmodulin gene17. Intracellular calcium levels in the intestinal epithelium are affected by C. jejuni in some lines23, however the extent to which this affects bacterial colonisation, or is induced by it, is unclear.Several immune-related genes in the QTL regions on chromosomes 19 and 26 showed evidence of allele-specific expression. Among these genes were the polymeric immunoglobulin receptor  giving a total of 3000 birds phenotyped over a period of 16 months. After culling of birds by cervical dislocation by trained personnel, cardiac blood was collected for DNA extraction, the two caeca were collected for enumeration of viable Campylobacter, and the two caecal tonsils were stored in RNAlater  for subsequent RNA extraction.A total of 3000 birds of an outbred pure-bred commercial broiler line from the Aviagen breeding programme were housed within a non-bio-secure environment (referred to as the sib-test environment) that aimed to resemble broader commercial conditions and where full siblings and half siblings of selection candidates are placedCampylobacter, serial ten-fold dilution series of weighed contents of the two caeca were separately prepared to 10–7 in phosphate-buffered saline and 100 μL of each dilution plated to modified charcoal deoxycholate (mCCDA) agar supplemented with cefoperazone (32 mg/L) and amphotericin B , followed by incubation for 48 h under microaerophilic conditions  at 41 °C. Dilutions were plated in duplicate and colonies with morphology typical of Campylobacter were counted and expressed as CFU/g. The theoretical limit of detection by the method used was 100 CFU/g. In instances where no colonies were observed after direct plating, a Campylobacter load equal to the theoretical limit of detection was assumed, as enrichment to confirm the absence of Campylobacter in caecal contents was not performed.To enumerate 74 to the 600K SNP Affymetrix Axiom HD array75 based on parent, grand-parent and great-grand-parent 600K SNP Affymetrix data. Of 3000 birds sampled, genotypes for 2718 birds were successfully imputed. Imputation failures likely reflect a lack of compatibility between the pedigree information and the genotypic data.All the birds were genotyped with a proprietary 50K high-density genome-wide SNP array and then imputed using AlphaImputeCampylobacter levels were not normally distributed, all counts were log-transformed and expressed as log10 CFU/g. Genetic parameters were estimated for caecal Campylobacter colonisation resistance using a mixed linear univariate model that included the date of sampling and the sex as fixed effects, and the random effect of the individual birds linked to each other with the pedigree genetic relationship matrix. Body weight and maternal effects were also tested but their effects on Campylobacter levels were not significant and therefore were not included in the final model. Genetic relationships between birds were calculated using a three generations pedigree and included in the analyses. The heritability of the trait was calculated as the ratio of the additive genetic variance to the total phenotypic variance. The analysis was performed using ASReml v4.076.As caecal 77 with parameters of minor allele frequency > 0.05, call rate > 95% and Hardy–Weinberg equilibrium (P > 10−6). After quality control, 37,498 and 288,381 SNP markers remained for further analysis . Positions of SNP markers were obtained using the GalGal5 annotation, available via the Ensembl Genome Browser (http://www.ensembl.org). Population stratification was investigated using a genomic relatedness matrix generated from all individuals. This was converted to a distance matrix that was used to carry out classical multi-dimensional scaling analysis (MSA) using the R package GenABEL78 to obtain its principal components. The GEMMA v0.98.1 algorithm79 was used to perform GWAS analyses using a standard univariate linear mixed model in which date of sampling and sex were fitted as fixed effects and the genomic relatedness matrix among individuals was fitted as a polygenic random effect. After Bonferroni correction for multiple testing, significance thresholds for analysis with the 50K array were P ≤ 1.33 × 10−6 and P ≤ 2.66 × 10−5 for genome-wide significant levels  and suggestive significant levels , respectively, corresponding to − log10(P) of 5.87 and 4.47. The significance thresholds for the 600K array after Bonferroni correction were P ≤ 1.73 × 10−7 and P ≤ 3.46 × 10−6 corresponding to − log10(P) of 6.76 and 5.45. The extent of linkage disequilibrium (LD) between significant SNPs located on the same chromosome regions was calculated using the r-square statistic of PLINK v1.0977.The 50K and 600K SNP genotype data were subjected to quality control measures using PLINK v1.09P) due to each SNP locus were calculated as follows:76.Individual markers found to be significant in the previous step were further examined with a mixed model that included the same fixed effects as used in the GWAS, the fixed effect of the corresponding SNP locus genotype and the random effect of the animal. Additive (a) and dominance (d) effects, and the proportion of total phenotypic variance  and suggestive levels , respectively, corresponding to − log10(P) of 4.57 and 3.27.RHM analyses were performed using DISSECThttp://www.ensembl.org/Tools/VEP). Moreover, all the genes that were located within the 20 SNP windows found to be significant by RHM; and the 250 kb 5′ and 3′ regions of the significant SNP markers identified by the GWAS were annotated using GalGal5 data obtained by the BioMart data mining tool (http://www.ensembl.org/biomart/martview/). This allowed us to catalogue all the genes that were located in the vicinity of the identified significant SNP markers for Campylobacter colonisation resistance.All significant SNPs identified in the GWAS and RHM analyses were mapped to the GalGal5 reference genome and annotated using the Ensembl Variant Effect Predictor . Details of the birds selected are shown in Supplementary Table http://genomics.ed.ac.uk/) using the Illumina TruSeq mRNA (poly-A selected) library preparation protocol . The mRNA was sequenced by Edinburgh Genomics at a depth of > 40 million strand-specific 75 bp paired-end reads per sample, using an Illumina HiSeq 4000. Expression levels for the 23 samples were estimated using Kallisto v0.43.081. Rather than aligning RNA-seq reads to a reference genome, reconstructing transcripts from these alignments and then quantifying expression as a function of the reads aligned, Kallisto employs a ‘lightweight’ algorithm, which first builds an index of k-mers from a known transcriptome. As a reference transcriptome, we obtained from Ensembl v89 the set of GalGal5 cDNAs and ncRNA transcripts . Expression levels were then estimated directly  by quantifying exact matches between reads and k-mers. Expression is reported per transcript as the number of transcripts per million, and is summarised to the gene level as described previously82.Total RNA was prepared from the caecal tonsils of 23 broilers, selected on their genotype  and caecal Campylobacter load, after adjusting for sex and seasonality, using the Kallisto output with the R/Bioconductor package ‘Sleuth’ v0.29.083. Differential expression was considered significant for FDR corrected P values ≤ 0.05 and suggestive significant for uncorrected P values ≤ 0.001. Additional differential expression analyses were performed using the qRT-PCR output for a subset of the genes found to be significantly differentially expressed in the initial RNA-Seq analysis. Least square mean pairwise comparisons between different Campylobacter levels were conducted. Tukey’s HSD post-hoc test adjustment was applied at a significance level of 0.05.Differential expression analysis was run on caecal tonsils from birds with high, intermediate and low 84. The number of transcripts per million, derived from Kallisto analysis as described above, were used as a measure of gene expression. Several covariates  were included in the association analysis. Cis- and trans-eQTLs were obtained, considering cis-acting SNPs to be within 100 kb from the 5′ start or 3′ end of a known gene. P values were corrected using false discovery rate (FDR) estimated with Benjamini–Hochberg procedure. SNP–gene expression association was considered significant for FDR corrected P values ≤ 0.05. For the significant cis-eQTL we estimated effect size using the log allelic fold-change (aFC) measurement. aFC is defined as the log-ratio between the expression of the haplotype carrying the alternative variant allele to the one carrying the reference allele and was calculated as described85. Briefly, the model assumes an additive model of expression in which the total expression of a gene in a given genotype group is the sum of the expression of the two haplotypes: e(genotype) = 2e_r, e_r + e_a, and 2e_a for reference homozygotes, heterozygotes, and alternate homozygotes, respectively, where e_r is the expression of the haplotype carrying the reference allele, and e_a the expression of the haplotype carrying the alternative allele. The allelic fold change k is defined as e_a = k e_r where 0 < k < ∞. aFC is represented on a log2 scale as s = log2 k.eQTL analyses were performed using the R package Matrix eQTL v2.1.086. Leading and trailing bases with a Phred score less than 20 were removed, and the read trimmed if the average Phred score over a sliding window of four bases was less than 20. Only reads where both forward and reverse pairs were longer than 36 bp post-filtering were retained. Filtered reads were mapped to the chicken genome 87 using STAR v2.6.1a88, with the maximum number of mismatches allowed for each read pair set to 10% of the trimmed read length, and minimum and maximum intron lengths set to 20 bases and 1 Mb, respectively. PCR duplicates were marked and SNPs were identified and genotyped called for individual samples using samtools v1.689, ignoring reads with mapping quality < 20 and bases with Phred quality scores < 20. SNPs within 5 bp of an indel, with mapping quality < 20, minor allele frequency (MAF) < 0.05 or where < 4 reads supported the alternative allele were also discarded. The SNPs located within the QTL regions identified by the GWAS were annotated using Variant Effect Predictor, as described above.In order to perform allele-specific expression analysis we aligned RNA-Seq reads to the reference genome and called the genomic variance in the previously identified QTL regions. Quality filtering and removal of residual adaptor sequences from the raw reads was first performed using Trimmomatic v0.3890. For every SNP in a region of interest, read counts were obtained for each allele present in a heterozygous animal, provided it was present in > 4 and < 17 heterozygous animals . SNPs with less than 10 reads were excluded. A binomial test was performed to assess the significance of the difference in allelic count. Allele-specific expression was considered significant if the mean P value across all heterozygotes was ≤ 0.05.Allelic-specific expression was assessed using the R package AllelicImbalance v1.24.0https://biotium.com/technology/pcr-dna-amplification/forget-qpcr-master-mix/) in 20 μL volumes containing 1 × Forget-Me-Not qPCR Master Mix, 0.5 μM of each forward and reverse primer, 50 nM of ROX reference dye and 2 μL of cDNA at a 1:10 dilution in template buffer. Gene-specific primers were designed and purchased from Sigma. Primer sequences are detailed in Supplementary Table r28S, TBP and GAPDH91.First strand synthesis was performed using 1 μg of total RNA and the Verso cDNA Synthesis Kit (Thermo Scientific) according to the manufacturer’s instructions. qPCR reactions were performed using a hot-start EvaGreen dye-based master mix (the Forget-Me-Not qPCR Master Mix (Biotium), Campylobacter colonisation resistance was performed using the Ingenuity Pathway Analysis (IPA) programme . IPA constructs multiple possible upstream regulators, pathways and networks that serve as hypotheses for the biological mechanism underlying the phenotypes based on a large-scale causal network derived from the Ingenuity Knowledge Base. IPA then infers the most suitable pathways and networks based on their statistical significance, after correcting for a baseline threshold92. The IPA score in the constructed networks can be used to rank these networks based on the P values obtained using Fisher’s exact test [IPA score or P score = – log10].Identification of potential canonical pathways and networks underlying the candidate genomic regions associated with Campylobacter colonisation resistance was also analysed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.893. Gene ontology (GO) was determined and functional annotation clustering analysis was performed. The Gallus gallus background information is available in DAVID and was used for the analysis. The enrichment score (ES) of the DAVID package is a modified Fisher exact P value calculated by the software, with higher ES reflecting more enriched clusters. An ES greater than 1 means that the functional category is overrepresented.The gene list for As these were commercial birds from an industry breeding programme and were not experimentally inoculated, the study was conducted outside the auspices of the Animals (Scientific Procedures) Act 1986, but was nevertheless subject to scrutiny and approval by the Animal Welfare & Ethical Review Body of The Roslin Institute, University of Edinburgh.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Table S9.Supplementary Captions.


In the present study, runs of homozygosity (ROH) detected with the use of a standard bovine 54k single nucleotide polymorphism (SNP) genotyping assay and two different ROH detection approaches, based on 50 (M1) or 15 (M2) consecutive SNPs, were compared with results of whole genome sequencing. Both microarray-based methods accurately recognised medium-sized ROH, however, it was found that M2 method seemed to better than M1 identify short ROH, but highly overestimated their number, leading to numerous false positive calls. Moreover, long ROH identified with microarray data tended to break into shorter segments in sequencing data because of the presence of regions with high heterozygosity within the ROH sequences. This may indicate, that these long ROH are formed by closely positioned shorter homozygous segments that may be of older origin or may be created by two similar but not identical haplotypes, showing minor internal recombination signs. Such finding also suggests that at least some of the results of previous studies in regard to long ROH may be biased leading to inaccurate estimations of genomes autozygosity via ROH classification into length categories. In recent years, the availability and continues reduction of costs of new high-throughput analytical methods such as genome sequencing and high-density SNP arrays has led to obtaining precise information about specific chromosomal segments which includes regions of extended homozygosity. These long homozygous stretches of the genome, so called runs of homozygosity (ROH), may be formed due to mating of related individuals which led to transition of haplotypes that are identical by descent In the latest years, most ROH analysis were maintained on medium or high-density SNP arrays - in the case of bovine - either 54k or 777 k chip. Despite a large number of studies, many of the parameters required to evidence ROH differ in the literature. In general, a minimal ROH length of 1 Mb and a 1 Mb maximum gap between SNPs is assumed in most studies performed with a 54 k chip. However, a number of consecutive homozygous SNPs required within ROH vary and ranges from 15 to 50. The number of heterozygotes allowed within ROH also differs between studies, ranging from 0 to 1 for the 54k chip, to even 16 for long ROH (above 16Mb) in the case of the 777k chip The material of the study was genomic DNA obtained from semen of a Polish Red bull. DNA was either genotyped with use of Illumina BovineSNP50 BeadChip (standard Infinium Ultra protocol) or sequenced-by-synthesis using Illumina HiScanSQ system. Across the genome, SNPs were detected using Freebayes software http://animal.nwsuaf.edu.cn/code/index.php/BosVar/loadByGet?address=BosVar/Download/varData.php). The .vcf files containing information about the variation in their genomes were processed in the same way as for Polish Red bull and included in total 4 250 029 SNPs for the first and 4 438 040 SNPs for the second animal. Microarray-based ROH (M1 approach) were detected based on reduced sequencing dataset involving markers common for both array and sequencing methods with cgaTOH software settings established individually per animal as described above.The obtained results concerning gaps breaking the longest microarray-based ROH into shorter segments in sequencing data were further validated in two other animals of Holstein breed which were sequenced in the course of another study For all approaches and data types (array/sequencing), ROH appeared on 5 different autosomes . The number of ROH and the sum of ROH lengths was the highest in the case of M2 approach and the lowest for the M1 method. Both sequencing-based approaches showed intermediate values. The detailed ROH positions along with their lengths are presented in Supplementary File 2, graphically in Figure M1 approach (using 50 consecutive SNPs) enabled to identify 5 ROH - three above 4 Mb of length and two spanned 3.3 and 2.1 Mb, while corresponding S1 method showed 13 ROH with size from 1 to 4.5 Mb. Three of the long ROH detected with the M1 method were broken into 2-3 shorter segments in the case of S1. Regarding M1 vs. S1, it was noted that microarray-based ROH overlapped with 75.6% of all ROH segments identified by sequencing-based approach, while in the case of sequencing method, it overlapped with 94.7% all ROH identified with the use of microarray data. The observed difference is associated with the presence of short ROH identified based on sequencing data that were not detected in microarray-based approach.Microarray-based M2 method (focusing on shorter ROH segments by requiring only 15 consecutive SNPs) has led to detecting 21 ROH - three long ones that were above 4 Mb and 18 with various short lengths ranging from 1 to 3.3 Mb. Corresponding sequencing-based approach (S2) showed 17 ROH that were from 1 to 4 Mb in length. Those three long ROH detected with M2 were identical with those identified by the M1 method and in a similar way, they were broken into 2-5 segments in S2 approach. In the case of M2 vs. S2, we found that microarray-based ROH overlapped with 88.1% of all ROH identified by sequencing-based approach, while in the case of sequencing data, it overlapped with 64.4% of all ROH identified with the use of microarrays. It is clear that M2 overlapped a higher percentage of ROH detected by sequencing approach than M1 due to the identification of a higher number of short ROH. However, it also resulted in detection of a high number of false short ROH (which were not detected using any sequencing-based approach). This false-positive short ROH caused that only 64.4% of all ROH segments overlapped between S2 and M2 approaches.Regarding all overlaps between all applied approaches (presented in Supplementary File 4) it is clear that M2 approach in general overlapped better with sequencing approaches than M1. However, when it was tested how M2 method is described by three other methods, the percentage of overlaps was the lowest. This proves the statement that M2 appears to overestimate the number of short ROH leading to the identification of many false-positives.In regard to the gaps breaking the longest microarray-based ROH into shorter segments in sequencing data, it was noted that S1 method detected 3 gaps: one on BTA3 and two on BTA26; while S2 had 7 gaps: three on BTA3 and 4 on BTA26. Moreover, all of the gaps identified by S1 were also present in S2. The ROH gaps had various lengths from 894 bp to 23306 bp and were formed by a different number of heterozygous SNPs in close vicinity (from 4 to 21). The gaps identified by S1 approach are especially interesting since they were formed by 12, 15 and 21 heterozygous SNPs, positioned in a close vicinity - all of them having high read coverage (from 12 to 21 with a mean of 16.5) and genotype quality , suggesting that they are unlikely genotyping errors. Also, calculated GC content within analysed regions . This observation confirms that the presence of short highly heterozygous gaps within long ROH detected with microarrays is a common phenomenon which is likely independent on technical genotyping aspects or animal-specific features. Moreover, in the research of In previous research, various authors suggested that 50k chips may not be dense enough to detect short ROH In addition, Ferenčaković et al. In various research, it has been established that in animal genomics, ROH may be used as a tool to estimate inbreeding by calculating the portion of the genomes covered in ROH (FROH) Supplementary information, figure, and tables.Click here for additional data file.