Populus trichocarpa is an important forest tree species for the generation of lignocellulosic ethanol. Understanding the genomic basis of biomass production and chemical composition of wood is fundamental in supporting genetic improvement programs. Considerable variation has been observed in this species for complex traits related to growth, phenology, ecophysiology and wood chemistry. Those traits are influenced by both polygenic control and environmental effects, and their genome architecture and regulation are only partially understood. Genome wide association studies (GWAS) represent an approach to advance that aim using thousands of single nucleotide polymorphisms (SNPs). Genotyping using exome capture methodologies represent an efficient approach to identify specific functional regions of genomes underlying phenotypic variation.P. trichocarpa clones, representing 101 provenances collected from Oregon and Washington, and established in California. A GWAS performed on 20 traits, considering single SNP-marker tests identified a variable number of significant SNPs  in association with diameter, height, leaf carbon and nitrogen contents, and δ15N. The number of significant SNPs ranged from 2 to 220 per trait. Additionally, multiple-marker analyses by sliding-windows tests detected between 6 and 192 significant windows for the analyzed traits. The significant SNPs resided within genes that encode proteins belonging to different functional classes as such protein synthesis, energy/metabolism and DNA/RNA metabolism, among others.We identified 813 K SNPs, which were utilized for genotyping 461 P. trichocarpa biomass required to support the development and application of marker breeding technologies.SNP-markers within genes associated with traits of importance for biomass production were detected. They contribute to characterize the genomic architecture of  Populus species and their hybrids are suitable feedstocks for second-generation biofuel production due to their rapid growth rates and favorable cell wall chemistry [Populus trichocarpa Torr. & A. Gray (black cottonwood), native to western North America, has been used in breeding for generating commercial cultivars [P. trichocarpa cultivars, as well as their improvement, depend on multiple biological and environmental factors [P. trichocarpa for complex traits related to growth, phenology, morphology, ecophysiology and wood chemistry [hemistry , 2. In pultivars . Biomass factors . Considehemistry –10. Theshemistry , 12, budhemistry , 13, 14,hemistry , water-uhemistry , 17, sechemistry  and woodhemistry . This sohemistry , 19.Populus species [Association analyses based on SNPs have been applied in recent years to identify polymorphisms controlling variation in complex traits of interest for biofuel production in  species , 18–21. Populus species, which have experienced a whole-genome duplication event [P. trichocarpa [Among “next-generation” sequencing alternatives, genome complexity reduction by sequence capture, or targeted sequencing, represents an efficient approach to performing genome wide analysis . This meon event . It was chocarpa .P. trichocarpa population of 461 clones from 101 provenances collected from the Pacific Northwest (Oregon and Washington) in the United States. In an previous study [P. trichocarpa range in North America, and it was established and evaluated (at age two) in a trial located significantly to the south than that range. That represents a particular environmental/experimental condition, useful to determine, for example, the effects of geographic relocation on the P. trichocarpa performance. Understanding genetic variation at a genome-wide scale is fundamental for developing genome-based breeding technologies suitable for supporting the development of genetically improved plantations for bioethanol production.In the present study, we employ sequence capture for genotyping and performing a GWAS in a us study , represeP. trichocarpa, which determine its potential as feedstock for lignocellulosic ethanol. This approach complements our previous phenotypic characterization of the same association population [P. trichocarpa gene exome.We used GWAS to identify DNA polymorphisms associated with biomass production and wood chemical composition in pulation  by identP. trichocarpa in the Pacific Northwest of the United States. In a previous study [The association population used in this study consisted of 461 clones (from 101 provenances), comprising part of the natural distribution range of us study , we obseP. trichocarpa genome in the association population, and after filtering, a set of 813,280 SNPs was used for association analyses  and the slowest decay on chromosome 11 (r2 0.2 at 51.6 kbp). Genome-wide LD decay exhibited different extents among chromosomes (Table r2 < 0.2 was observed on average at 26.9 kbp. High variation of LD across the genome (among and within chromosomes) has been reported for this species [r2 0.2 at ~ 0.5 kbp) and Wang et al .[r2 0.2 at ~ 8 kbp) for P. trichocarpa. Distinct methodologies, number of markers, population sizes, genetic origins and standard errors among the studies may account for the different findings. Compared with other tree species extent of LD estimated in this study is similar to species belonging to Fraxinus [Prunus [Eucalyptus [The extent of linkage disequilibrium (LD) was analyzed across each chromosome. On average, the LD over physical distance decayed below  species . The estg et al .  were identified for DBH, h, leaf C and N content, and δ15N. Figure Significant associations . Similarly, in the case of wood metabolites, considering a selected subset of those with the top five highest heritability estimates, no significant associations meeting the adjusted p-value were identified for Adenosine (Ade), Hydroxybenzoic Acid (HbA), Galactinol , Galactonic Acid (GAc) and Alpha tocopherol (Toc). The proportion of phenotypic variation accounted for the cumulative effect of significantly associated SNPs was 0.2, 1.1, 0.1, 0.7 and 0.7% for DBH, h, leaf C content, leaf N content, and δ15N, respectively.In general, and consistently with chromosome length, the highest numbers of significant associations were observed for chromosomes 1 and 5. The lowest number of associations was observed for chromosome 16. The proportion of significant SNPs of the total analyzed, ranged from 0.02 ‰ to 0.50 ‰ for leaf C content on chromosome 10, along with δ15N. Among genes related with proteins involved in DNA/RNA Metabolism, one for a helicase senataxin (without gene model in Phytozome) was significant for height and leaf N. For genes in the Energy/Metabolism functional class, a representative was one (Potri.015G119700) encoding a Domain of unknown function (PGG), which was associated with DBH. For the Signal transduction class, the gene encoding a Rop Guanine Nucleotide Exchange Factor 1 (Potri.009G140100) was significant for height and leaf N.Significant single nucleotide polymorphisms associated with phenotype were identified mostly in exonic regions. SNPs are part of genes encoding proteins belonging to the functional classes: Protein Synthesis/Modification (54.5%), DNA/RNA Metabolism 27.3%), Energy/Metabolism 9.1%) and Signal transduction (9.1%) , GWAS performed on single-SNPs was successful in identifying polymorphisms associated with growth traits (DBH and h), leaf C and N-contents, as well as stable isotope parameters (δ15N) , GWAS did not detect single-SNP associations. On the other hand, for traits with low to moderate H2i (e.g. leaf C-content and δ15N) a relatively higher number of SNPs were identified. Similar situations were observed for phenology traits in previous studies with P. trichocarpa [H2i and the phenotypic variance collectively accounted for by SNPs, could be explained by non-additive effects  or epigenetic factors acting on some traits. These types of effects are usually underestimated because MLM utilized for GWAS only suppose additive interactions [p-value adjustment in GWAS, given the correlated nature of markers along a chromosome [p-value < 0.00001 (and even lesser) in traits such as Vol, DBF, lignin or GAc, they did not reach the adjusted p-value threshold and were considered non-significant.Considering the applied significance threshold with Bonferroni correction  and WRKY Transcription Factor 10-related (Potri.013G086000), were associated with Gal and C5-sugars, respectively. Concerning the Protein Synthesis/Modification class, examples of identified genes are those encoding a Similar to Threonyl-tRNA synthetase and Leucine Rich Repeat (LRR_1)//Leucine rich repeat (LRR_8), associated with C5-sugars and height, respectively.Considering the top three most significant windows across all chromosomes and traits, the most represented functional classes were Protein with Unknown Function 21.2%), Energy/Metabolism (21.1%), Protein Synthesis/Modification (19.2%), and Transcription (15.4%) Fig. b. A list.2%, Enerp-value 4.70E-25) detected in chromosome 5 for leaf δ15N  and produced significant variation at the level of leaf δ15N means , which included SNPs and windows associated with leaf δ15N. The exoribonuclease function of this gene links it to transcription, RNA metabolism and RNA interference in eukaryotes [XNR gene family produced sensitivity to N starvation in Saccharomyces cerevisiae [Arabidopsis thaliana [XRN4 with leaf δ15N, an indicator of N use efficiency [XRN4 on photosynthesis and biomass production.Significant associations for traits underlying growth, nutrient metabolism and xylem formation, among others, define SNPs and genes, which might represent logical candidates for functional studies focused on confirming their role and impact on phenotype of karyotes . In plankaryotes  and respkaryotes , 43. Mutrevisiae  and morpthaliana , 46. Thuficiency , could bP. trichocarpa wood was previously performed in the same population by our group [p-value < 0.0001 threshold. Results indicated both overlap and divergence between the two studies  were significantly associated with C6-sugars in both studies. For this trait, we also detected SNPs in TUA5 gene, which were not identified by Wegrzyn et al. [CesA2B and CesA1A) were differentially detected  in both studies. We also identified SNPs belonging to 4- Serine Hydroxymethyl Transferase SHMT6. Finally, for the S:G ratio, our analyses detected SNPs in Laccase LAC1A, Phenylalanine Ammonia-Lyase PAL5 genes, which were not identified by Wegrzyn et al. (2010). In spite of the genotypes and wood chemical characterization methods were mostly the same in both studies, distinct trial sites , sampling height or differential presence of juvenile wood, among other factors, might explain the differences in the findings reported previously [Association analyses of the chemical composition of ur group  using a ur group , which en et al. . In the eviously  and thosP. trichocarpa clones at different levels. Our results thus provide a starting point to define candidate genes suitable for functional characterizations addressed to confirm their biological role. At the same time, the genome-wide scale applied for the association analyses revealed a large number of SNPs which could be utilized to develop genomic selection schemes. Further efforts to define the utility of SNP polymorphisms in generating genomic breeding values will illuminate the path to breeding programs that incorporate molecular markers for bioethanol production. The upshot will be the estimation of parental hybridization values and an earlier, more precise genotypic selection from segregating F1 hybrid populations, that together will increase the overall magnitude of the realized genetic gain per unit of time.Forest trees are an important source for multiple wood and non-wood products. Genetic improvement aimed to develop such products, including lignocellulosic biofuels, depends on the variation underlying commercial traits. This variation is characterized by a complex genetic control and the influence of environmental factors. In this study, we identified a series of DNA polymorphisms controlling the phenotypic variation of growth, nitrogen use and wood composition of P. trichocarpa clones. These represented 101 provenances, within 14 river systems located west of the Cascade Mountains in Oregon and Washington between 48°54′ N latitude  and 43°47′ N latitude  collected by GreenWood Resources [The association population was comprised of a set of 461 esources . A clonaH2i) was estimated using formula http://dendrome.ucdavis.edu/treegenes/) under the accession number TGDR050.Data used in the phenotypic characterization is available at the TreeGenes platform , at age two, were measured in October 2010, as described by Guerra et al. . In part2× h . Additio2× h .Wood cores collected from tree stems (0.3 m above ground level), at age three, in September 2011, were utilized for analyzing the chemical composition and metabolome of wood . The con15N, SLA, and NArea.Morphological and ecophysiological characteristics are determinants of biomass productivity . For thaAdditional young leaves were collected for DNA isolation. Circle punches were obtained and deposited in plastic vials, along with silica gel packets, until desiccation. This sort of leaf samples is compatible with automated grinding and pipetting systems. DNA isolation was performed using the Qiagen DNeasyPlant Mini Kit , according manufacturer’s instructions. Quality was assessed with spectrophotometer . Acceptable extractions had a 260/280 ratio of 1.7–2.0 with a minimum concentration of 20 ng/μl.P. trichocarpa (corresponding to assembly version 2.0 of the black cottonwood genome) were used to design oligonucleotide baits, complementary to short genomic regions that targeted exons, promoter, and intergenic control regions, as described by Zhou et al. [P. trichocarpa genome. As the cumulative length of the predicted exons exceeded the available baits, following bait design in eArray, we looped through the gene list, selecting one bait for each gene at each pass, until the maximum number of baits  was reached. In addition to exons, baits were included in the design for genes with an annotated 5′-UTR targeting the 240 bp upstream, as well as 1000 baits targeting intergenic regions to be used as selectively neutral control regions. These control regions were selected at random from non-repetitive intergenic intervals at least 1000 bp from any gene model. This strategy of bait design has demonstrated previously that capture efficiency has not been significantly impacted by the presence of paralogous genes [XT protocol (Version B). Briefly, 3.0 μg of poplar genomic DNA was sheared on a ultrasonicator (Covaris S220) at the Virginia Bioinformatics Institute (VBI), followed by end repair, 3′-end adenylation, adaptor ligation, and amplification. Agencourt AMPure XP beads were used to purify the libraries following each step, and library quality was assessed using an Agilent Bioanalyzer 2100 instrument, with Agilent DNA 1000 chips. Samples were randomly assigned one of 96 available index sequences and subsequently randomly assigned to groups of 16, each of which corresponding to a HiSeq sequencing lane. The prepared libraries were hybridized to the RNA baits in solution at 65 °C in an Eppendorf Mastercycler PCR machine , and subsequently purified on magnetic beads. The multiplexed libraries were sequenced using an Illumina HiSeq 2500 System in a 2 × 100 paired-end format at VBI. Sequences of the prepared libraries are available at the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra/) under the accession number SRA058855.Phytozome version 7.0 annotation and assembly files for u et al. . A totalus genes . After dhttps://github.com/maasha/biopieces; version 2.0). First, interleaved pair-ended sequences were filtered based on the Illumina filter flag, and subsequently trimmed of adapters and bases with quality < 35. Following trimming, very short reads were eliminated (length < 35) to prevent ambiguous alignments. Lastly, reads having poor local quality scores  were removed from the analysis. The short reads were aligned to the Populus trichocarpa (version 3.0) reference genome with the Burrows-Wheeler Aligner mem algorithm [https://broadinstitute.github.io/picard/command-line-overview.html) and the Genome Analysis Toolkit v3.x , with MarkDuplicates and DuplicateReadFilter functions, respectively [min_base_quality_score > 9, and standard_min_confidence_threshold_for_calling > 29) [Short reads from poplar samples were pre-filtered using a collection of scripts in Biopieces (lgorithm . Resultilgorithm  to theirectively . Indels ng > 29) . Varianthttp://cnsgenomics.com/software/gcta/index.html). SNP data were first converted to PLINK format using TASSEL v3.0 [http://www.maizegenetics.net/tassel), and then converted to binary PLINK (bed file) using PLINK v1.9 command line tool [cmdscale function in the package Stats in R [Stats in R [Stats in R [p-values was 6.1479E-8 at significant level of 5% after Bonferroni correction . We additionally used the Random Forest method to estimate the percentage variance explained by the top SNPs for each trait (SNP contribution), by using the randomForest package in R [ntree) grown as 2000 and averaged 50 repeats per trait in our estimates.The distributions of the different traits were checked for departures from normality. Logarithmic transformations were applied to normalize the variables Vol, DBF, C and N concentration, C/N ratio, SLA, NArea, lignin, C5-sugars and C6-sugars. Outlier observations were excluded from tests. Clonal means were adjusted by Best Linear Unbiased Predictor, using Proc MIXED in the software SAS v9.2 , in order to correct a significant block effect observed across the trial area. Prediction model included the factors clone and block, considered as random and fixed effects, respectively. The GWAS was based on Mixed Linear Models (MLM), implemented in the software GCTA v1.25   for 10 k window size with 1 k slide, using a custom script. Empirical p-values for each slide were estimated assuming the significant slides follow a Poisson distribution [Multiple-SNP testing for each trait was carried out using an overlapping sliding-window analysis on association results, based on the number of the significant associations  and reported for each trait. Finally, p-values were visualized using a Manhattan plot, highlighting only the significant slides.Where lambda (ℷ) is the average number of significant SNPs, r2. The r2 value between pairs of SNP markers, within each chromosome, was estimated using TASSEL 5.2 [r2 values [Linkage disequilibrium (LD) was estimated among pairwise combinations of SNPs per chromosome. It was expressed in terms of the squared correlation of allele frequencies SSEL 5.2 , utilizi2 values . AnalysiPopulus trichocarpa v 3.0) and Quick GO site (http://www.ebi.ac.uk/QuickGO-Beta/), respectively.Gene models and gene ontologies were obtained from the Phytozome platform version 12 (Additional file 1: Table S1. a Number of significant single SNP-markers. b. Proportion of significant SNPs on total analyzed SNPs. Table S2. Number of significant sliding windows. Table S3. Top three significantly associated single SNPs. Table S4. Top three significantly associated SNP-windows.Additional file 2: Table S5. SNPs belonging to genes encoding enzymes for lignin and cellulose biosynthesis pathways.Additional file 3: Figure S1. Linkage disequilibrium decay per chromosome.Additional file 4: Figure S2. Manhattan plots for assessed traits.Additional file 5: Figure S3. Manhattan plots for sliding window analysis tests.


Populus deltoides W. Bartram ex Marshall. The objective of this study was to understand how genomic diversity of this foundation species was shaped by its environment  to gauge its adaptive capacity. We used two complementary approaches to get a full portrait of P. deltoides genetic diversity at both the species and whole‐genome ranges. First, we used a set of 93 nuclear and three chloroplastic SNP markers in 946 individuals covering most of the species' natural distribution. Then, to measure the degree of intraspecific divergence at the whole‐genome level and to support the outlier and genomic‐environment association analyses, we used a sequence capture approach on DNA pools. Three distinct lineages for P. deltoides were detected, and their current distribution was associated with abiotic and biotic variations. The comparison between both cpDNA and ncDNA patterns showed that gene flow between the lineages is unbalanced. The southern and northeastern populations may benefit from the input, through river flow, of novel alleles located upstream to their local gene pools. These alleles could migrate from populations that are already adapted to conditions that fit the predicted climates in the receiving local populations, hotter at the northeastern limit and drier in the Central United States. These “preadapted” incoming alleles may help to cope with maladaptation in populations facing changing conditions.The speed and magnitude of global change will have major impacts on riparian ecosystems, thereby leading to greater forest vulnerability. Assessing species’ adaptive capacities to provide relevant information for vulnerability assessments remains challenging, especially for nonmodel species like the North American  Recent findings suggest that rapid climate shifts may outpace existing compensatory mechanisms of tree species, such as physiological acclimation, leading to greater forest vulnerability and tree mortality  and selective evolutionary processes. Thus, identifying the main abiotic and biotic factors that have influenced genomic diversity and the way genes are exchanged between populations may be one of the first steps to take when characterizing the ability of taxa to persist in a changing environment. Species with wide distributions across varying climates and environments may help elucidate how genetic makeup is shaped and determine the main underlying factors influencing this makeup.Populus deltoides W. Bartram ex Marshall) that constitute the structural basis of vital ecosystems  and biotic conditions. We could thus hypothesize that such a vast range of environmental variations would translate into different adaptations resulting from the selection of different alleles across the species’ range.Evolutionary processes such as hybridization and speciation have shaped the genomic diversity of species, and the study of ongoing ecological speciation processes may help to understand what drives differentiation  may be restricted by this unidimensional aspect of riparian forests. Moreover, their riparian setting means that they are subjected to a regime of frequent disturbances caused by floods  factors that influence the genomic diversity of P. deltoides. We used four sets of environmental data: geographic, climatic, edaphic, and biotic variables. For the latter, we used genomic data from Sphaerulina musiva, P. deltoides's associated fungal pathogen, that causes leaf spots in natural stands. We compare the effect of each set of variables on the diversity of P. deltoides and identify those that best explain the differentiation observed between populations. This characterization will provide information for a global change vulnerability assessment, which in turn helps prioritize adaptation measures, in terms of forest and land management, for this foundation species.We pursued two objectives in this study. First, we wanted a portrait of the population structure and genomic diversity of 22.1P. deltoides natural range was obtained from various sources or sampled in natural stands  using CLC Genomic Workbench 7.5.1 (QIAGEN). We selected the biallelic SNP variants in regions presenting a minimum coverage of 15 individuals out of 20. We tried to choose SNPs that were in the same genes as those used in a previous study focusing on the intraspecific diversity of Populus balsamifera in order to be able to compare the two species  of our SNP by the McGill University and Génome Québec Innovation Centre  using internal protocols. Using the 114 SNPs, three different iPlex Gold assays (Sequenom) were built with 34, 40, and 40 SNPs, respectively. The 1,004 2.3K value ranging from 1 to 10. The optimal number of clusters was estimated using the Delta K statistic  as suggested by Wang (n = 150–200).We used Structure software .Following population structure analyses, we calculated diversity and differentiation indices for all samples and for the different clusters  using the Fstat software Goudet, . A hieraQ‐value thresholds of 0.667 and of 0.9, and we also tested population groupings using populations with more than three trees and more than five trees. Analyses were conducted over the three clusters and by comparing all pairs of clusters. To optimize the number of individuals analyzed for paired analysis, datasets with populations of more than three trees were used. The Fdist2 simulations were performed assuming three genetic groups consisting of 100 populations each. Significance was tested using 20,000 simulations and corrected for multiple testing using a false discovery rate of 0.05  and one biotic (pathogen genomic variation). In addition, in order to visually compare the geographic distribution of 2.4.1Climatic variables covering the 1948–2010 period  were obtained using ClimateNA software v5.21  soil, (b) geographical, (c) climate mean, and (d) climate standard deviation variables. Adjusted R2 was used to measure the importance of each fraction in the variance partitioning. Moreover, associations between these explanatory variables and tree genomic variation were also tested using redundancy analysis (RDA). This analysis was conducted on two different datasets: first, on “pure” individuals presenting Structure Q‐values >0.9 and, second, on admixed trees presenting Structure Q‐values <0.9. Our objective in this case was to test whether similar genotype–environment associations occur in both “pure” and admixed regions. The idea was to look for specific environmental variables that would explain the lineage differentiation not only in allopatric regions with very different environmental conditions but also in sympatric zones that presented a mix of conditions.Before analyzing genomic and environmental data associations, we performed a PCA for the environmental variables alone. We then used variance partitioning  number of latent factors and was run for 10 independent runs, each using 10,000 iterations with the first 5,000 iterations discarded as a burn‐in. The independent replicates were then combined using the Fisher–Stouffer method, and the resulting p values were adjusted using the genomic inflation factor (λ) and then corrected for multiple testing using a false discovery rate of .05  . We used four pools of 20 diploid individuals: three were comprised of trees presenting Structure Q‐values >0.9 for each of the three Structure groups identified: the subsp. monilifera, the northeastern subsp. deltoides, and the southern subsp. deltoides, and the fourth pool was comprised of trees located in the admixed region between the southern and northeastern deltoides subsp. groups  (Table P. deltoides genome v2 (phytozome.jgi.doe.gov), a clone from Issaquena County, Mississippi, with the CLC Genomics Workbench v.7.5.1 (QIAGEN), keeping only unbroken sequence pairs. We then identified the variants within each pool, and on the combination of the four pools, using the low‐frequency variant detector in the same software suite: freq. threshold = 2%, min. cov. = 80  or 200 (merged pool), and min. qual. = 20. Only the biallelic SNPs that appeared in the individual pools, with a coverage comprised between 150 and 500, and merged pools were kept. We also kept only SNPs included within the 19 chromosomes of the genome assembly by excluding the few SNPs observed in other scaffolds. Observed pool allele frequencies below 2% were brought down to 0% as they were under the detection limit.In order to compare the genome‐wide variation and differentiation between the different genetic lineages identified with our SNP array, we conducted a sequence capture experiment on pools of trees representative of the different groups. We selected probes from the 9) Table . The DNAijx point corresponds to the number of SNPs found with a specific allele frequency combination in each pair of populations (pools) that are compared. Hence, in the absence of gene flow between the two populations, the frequencies of SNPs found in only one population are different from the SNPs in the other populations as a consequence of the genetic drift; but, in models with gene flow, we should observe many SNPs with similar frequencies in the two populations. Each plot was composed of a 41 × 41 grid that corresponds to the 41 possible allele frequencies in each pool; that is, considering N and M individuals in each populations, the AFS matrix should be (2N + 1) × (2M + 1), to allow for derived allele counts of 0. We produced six AFS plots to compare each pair of pools.We used the SNP allele frequency estimates in each pool to draw allele frequency spectrum (AFS) plots  for each SNP per group. We then obtained the total heterozygosity (Ht) using the mean He per SNP for each group. Using these values, we were able to calculate the differentiation index (Fst) (a) between each pair of pools (including the SNPs that were monomorphic for the pairs of pools compared) and (b) each single pool v. the three others, to draw Manhattan‐like plots . We used this approach to visualize the divergence impact at the genomic scale by identifying chromosomic regions that presented higher Fst. We also used them to visualize the SNPs previously identified by the FDist2 and LFMM analyses as being potentially under selection.The allele frequency measures were also used to calculate the expected heterozygosity . The Structure partition was also supported by the PCA analysis  separated trees from the northeastern region from those of the southcentral region. These two groups correspond to the same deltoides subspecies. The Structure analysis conducted using alternative parameters and the PCA performed on several random subsamples led to the unequivocal identification of the same three groups . The AMOVA analysis showed a very strong differentiation between the three clusters (Table deltoides subsp. and monilifera subsp. (FCT = 0.284). Within clusters (Table Fsc = 0.041). There was no significant difference between each population differentiation (Fst) and inbreeding values calculated for each of the three clusters. Genomic diversity appeared significantly higher in the subsp. deltoides (northeastern and southern groups) than in the monilifera subsp., a tendency that was confirmed at the genome scale (see below). The Hardy–Weinberg equilibrium tests detected a slight deficit in heterozygotes in the two northern clusters, that is, the monilifera subsp. (Fis = 0.021) and the northeastern deltoides subsp. cluster (Fis = 0.030), as well as in the admixed group (Fis = 0.081).The Structure and PCA results also showed that the three major groups integrated smoothly at their borders. Most of the admixed individuals had a genomic background from the two northern groups  associated to another lineage or subspecies. This type of pattern is the consequence of cpDNA capture . More specifically, we observed 37 trees presenting a n Figure b.3.4Q‐value threshold of 0.667 and of 0.9) and using a minimum number of three or five individuals per population , while the second axis separated the northern and southern groups of the deltoides subspecies. For “pure” and admixed datasets, 28.6% and 18.1%, respectively, of the total genomic variance were constrained by our set of noncorrelated variables. Similarly, the two first axes explained about twice the variation in the “pure” dataset compared with the admixed one . All climate variables were highly significant (p < .001) for the “pure" dataset, while the only significant soil variables were S_C , S_CEC , and S_GRAVEL . Similarly, all climate mean variables were highly significant for the admixed analysis and about half of the soil and climate standard deviation variables had a p value <.05 (data not shown).The RDA indicated that both datasets (“pure” and admixed trees) showed similar patterns Figure  and thesThe preliminary PCA analysis and the Tracy–Widom test indicated that five latent factors should be used for the LFMM analyses. Significant associations were identified between 16 distinct SNPs and environmental variables tested. Details are presented in Table 3.5.2P. deltoides and its native fungal pathogen (S. musiva) revealed a strong correlation . Although pathogen sampling was sparser than for its host tree, the visual comparison of the two Bayesian clustering results . From these, 24,526 were polymorphic in each pool and the southern ks Table .deltoides subsp. and monilifera subsp.  are often shared between the groups  might likely result in a similar trend. The Manhattan‐like plots  and the southern group.We detected a strong geographical structure consisting in three genetic lineages for P. deltoides as well as to look for DNA polymorphisms possibly associated with environmental variation. However, the global diversity measured with this SNP set was likely lower than if we would have used noncoding regions   and by the sequence capture approach. Precipitation and winter humidity appeared to be the most important climatic drivers of differentiation between monilifera and deltoides subsp.  were proposed in the past based on minor morphological differences . That being said, we cannot exclude the possibility that the deltoides subspecies differentiation observed could have resulted from an isolation by distance effect, although the two complementary Structure and PCA analyses also pointed toward a three clusters configuration  individuals. Only variables from the “climatic means” category appeared to satisfy this condition. Soil variables and climate standard deviations, which represent climate variability, had quite different patterns for “pure” and admixed individuals Figure . AccordiSD) indices for climate data was conducted as an attempt to capture an adaptive plasticity signal  number of trees representative of the different lineages we detected in the entire sampling. The combination of these two approaches provided better support for some SNPs identified using the FDist2 or LFMM analyses, identified more genes potentially under selection, and, at the genome scale, confirmed and detailed the differentiation pattern we observed with the small genotyping SNP assay.To study adaptation at both the species and genomic scales, we first used a low (but selected) number of SNPs from a large number of individuals that covered the entire range of monilifera subsp.  were all found in genomic regions with high Fst values between the three pools . Less congruence was observed when considering differentiation between the northeastern and southern deltoides subsp. groups, possibly because these two lineages were less divergent than the monilifera subsp., so their specific signal was diluted.From the 18 SNPs previously identified as potentially under selection by the FDist2 and LFMM analyses, 14 were present in the sequence capture dataset and identified in the Manhattan‐like plots Figure . Congrue. Figure a,b. Alths Figure a. Less cP. balsamifera study . The corresponding gene, WRKY‐1, could be associated with drought adaptation in both species. A similar interpretation could be proposed for the three SNPs found in the genomic region presenting high differentiation  that is identified as a coding region. The J026 and K008 SNPs are both located in a WRKY transcription factor gene  while L031 is located in a HDZIP transcription factor gene. The WRKY genes identified in the present study were not the same previously identified to be involved in resistance to Melampsora rust fungi (Azaiez et al., P. deltoides (Fahrenkrog, Neves, Resende, Dervinis, et al., P. trichocarpa (Chhetri et al., Of the six SNPs identified to be putatively under selection in the P. deltoides. The consecutive experiments that would formally validate the adaptive role of a SNP or a gene are often more complex than the gene identification experiments that precede them. For example, these types of validation tests may use, in addition to genomics, genetic transformation (Fernandez i Marti & Dodd, Criticism has been voiced concerning biological narratives extracted from literature and data mining information for genes identified by genome scan analyses (Pavlidis, Jensen, Stephan, & Stamatakis, 4.3P. deltoides is associated with riparian habitats, we expected that its genomic diversity might have been connected to the delineation of the drainage basins which was the case for Populus fremontii (Evans et al., monilifera and subsp. deltoides lineages is a probable consequence of river flow direction differences. Since P. deltoides seeds can also be dispersed by wind (Braatne et al., Given that deltoides subsp. may benefit from the arrival of new alleles from the western monilifera subsp. that would have been naturally selected to be adapted to a drier climate. Similarly, the northeastern deltoides subsp. may benefit from the input of new alleles coming from the southern lineages that could exhibit new characteristics that are and will potentially fit the hotter conditions of the projected future climate. Such a phenomenon has recently been documented for populations of an annual wildflower found at the edge of its northern range, where descendants having one parent coming from a warmer southern climate showed higher fitness in warmer conditions than the local plants (Bontrager & Angert, P. deltoides groups, but it would certainly be interesting to test this effect on fitness for this riparian tree.Such unbalanced gene flow between the different lineages could be an example of “unassisted” migration within a tree species. Indeed, gene flow direction in our case is in line with future climate predictions in the continental southern USA. In fact, the effect of climate change has already been observed in the region since the “100th Meridian divide,” which historically separated arid western USA from the humid east, was found to have actually moved toward the east (Seager et al., 5P. deltoides, is becoming more fragile. Although it remains challenging to translate results from a genomic study to the scale of conservation practices (Shafer et al., P. deltoides can help boost the use of evolutionary processes in forestry management (Lefèvre et al., P. deltoides is distributed across diverse habitats offers a simple way to assess its vulnerability at the scale of the species range. Our data showed that natural mechanisms are actually in place that may favor the adaptive capacity of the southern and northeastern populations because they can benefit from the input of “preadapted” alleles from adjacent lineages. Although the translation of such gene exchanges in increased fitness for P. deltoides remains to be demonstrated, this idea is consistent with the Hamilton and Miller (monilifera subsp. in the western U.S. and Canada. Our results showed a lower genetic diversity and less gene exchange with other lineages. Moreover, these regions are predicted to be severely impacted by climate change with an increase in aridity (Cook, Woodhouse, Eakin, Meko, & Stahle, With global change, riparian ecosystem functioning, which largely depends on foundation species like d Miller  proposalNone declared. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Internal organs indirectly affect economic performance and well-being of animals. Study of internal organs during later layer period will allow full utilization of layer hens. Hence, we conducted a genome-wide association study (GWAS) to identify potential quantitative trait loci or genes that potentially contribute to internal organ weight.A total of 1,512 chickens originating from White Leghorn and Dongxiang Blue-Shelled chickens were genotyped using high-density Affymetrix 600 K single nucleotide polymorphism (SNP) array. We conducted a GWAS, linkage disequilibrium analysis, and heritability estimated based on SNP information by using GEMMA, Haploview and GCTA software.2 = 0.493, 0.753). A total of 23 highly significant SNPs that associated with all internal organ weights were mainly located on Gallus gallus autosome (GGA) 1 and GGA4. Six SNPs on GGA2 affected heart weight. After the final analysis, five top SNPs were in or near genes 5-Hydroxytryptamine receptor 2A, general transcription factor IIF polypeptide 2, WD repeat and FYVE domain containing 2, non-SMC condensin I complex subunit G, and sonic hedgehog, which were considered as candidate genes having a pervasive role in internal organ weights.Our results displayed that internal organ weights show moderate to high (0.283 to 0.640) heritability. Variance partitioned across chromosomes and chromosome lengths had a linear relationship for liver weight and gizzard weight (ROur findings provide an understanding of the underlying genetic architecture of internal organs and are beneficial in the selection of chickens. The internal organs are involved in many biological occurrences, including oxygen transportation, lipid metabolism, and digestion. Each organ has its own developmental process and gene expression profile . Liver dGallus gallus autosome (GGA) 1. Rosário et al [With the development of deep sequencing and statistical methods, it is possible to detect the quantitative trait loci (QTLs) of complex traits. In recent years, great advances have been gained in the selection of economic traits in chickens with molecular biology techniques. Nones et al  identifiio et al  found noio et al  identifiIn this study, we conducted a genome-wide association study (GWAS) on an F2 resource population generated from Dongxiang Blue-Shelled (DX) and White Leghorn (WL) chickens with a 600 K Affymetrix chip. Furthermore, a linkage disequilibrium (LD) analysis, variance estimated percentage, and gene annotation were performed by biotechnology. This research will provide the genetic architecture that underlies the weight of internal organs in layer chickens.Animal procedures and sample collections were carried out following the guidelines established by the Ministry of Agriculture of China. The Institution of Animal Care and Use Committee in the Poultry Institute, Chinese Academy of Agricultural Science, Yangzhou, China approved the procedures.2 resource population by reciprocal crosses between WL and DX chickens. The WL was from a commercial line and DX from a Chinese indigenous strain, therefore the two lines represent two very different genetic pools, including morphological, physiological, and production traits, such as feed efficiency [1 generation. The F1 population was generated from WL (6 ♂)×DX (133 ♀) and DX (6 ♂)×WL (80 ♀) by initial reciprocal crosses, producing 1,029 and 552 chicks, respectively. The experimental animals were kept indoors under standardized conditions at the research base in the Jiangsu Institute of Poultry Science, Yangzhou, China. Birds had ad libitum access to feed and water that met all NRC requirements and were housed in single-hen cages under a 16 L:8 D lighting regime. The F2 birds were slaughtered at 72 weeks; immediately after the slaughter we measured the weights of heart (HW), liver (LW), proventriculus (PW), and gizzard (GW). DNA was collected by standard venipuncture. Data including a descriptive phenotype and normality test statistics were summarized using R software.We constructed an Fficiency , folliclficiency , and theficiency . The F2 http://affymetrix.com/) software was used to analyze genotype calling and quality control (QC). A total of 1,512 hens and 532,299 single nucleotide polymorphisms (SNPs) remained valid after the application of APT for QC. An R script was used to calculate the SNP QC metrics and filter out individual SNPs falling below given thresholds. The effectiveness of the detecting quality was tested by PLINK v1.90 [−6 were kept for further analysis. After QC, 1,512 individuals and 435,867 SNPs were used in further GWA analysis.The standard phenol/chloroform method was used to extract genomic DNA, which was genotyped against a 600 K Affymetrix Axiom Chicken Genotyping Array . Affymetrix Power Tools v1.16.0 (APT)  and 1.69×10−5 , respectively.A principal component analysis was used to eliminate spurious associations resulting from the presence of cryptic relatedness or hidden population stratification. Then, the full SNP set to 41,130 independent SNPs were pruned via the –indep-pairwise 25 5 0.2 command (PLINK), and we included five principal components (PCs) as covariates in the mixed model. After simpleM was used to determine the threshold by correcting the number of multiple tests, the genome-wide significant and suggestive p-values were 8.43×10The GEMMA v0.94 package , with thWhere y represents an n-vector of phenotypic values for n individuals; W is a matrix of covariates (fixed effects with a column of 1s and top five PCs), α is a vector of the corresponding coefficients including the intercept; x is an n-vector of the genotypes of the SNP marker, β is the effect size of the marker; μ is an n-vector of random effects; and ɛ is an n-vector of errors.The Manhattan plots and quantile–quantile (QQ) plots were drawn by “gap” packages in R software. The genomic inflation factor was calculated by using the GenABEL package in the R project.Galgal5 assembly, using BioMart system supported by Ensemble . The genes nearest or harboring significant SNPs of internal organ weight were chosen as candidate locations.The LD were implemented by Haploview v4.2 to test the association of significant SNPs with each other. When the distance between two consecutive genome-wide significant SNPs was greater than 10 Mb, they were considered as two separate QTL. The LD analyses would find a haplotype blocks because of these loci located in a strong LD region. But, a genuine causal locus could not be distinguished by present GWAS method. Therefore, a functional gene annotation on these significant SNPs were essential performed to characterize potential candidate genes. In our study, we searched candidate genes based on h2snp) for GWAS or the variance contributed by each chromosome were also estimated with GCTA.The contributions to phenotypic variance (CPV) explained by the significant association SNPs was calculated with GCTA software  using unDescriptive data for each organ are displayed in The results of the GWAS were as follows. There are 380 significant SNPs on GGA1, 49 SNPs on GGA4 were related to internal organ weights, and six SNPs on GGA2 were related to HW . The regGTF2F2), potassium channel tetramerization domain containing 4, spermatid associated, succinate-coA ligase ADP-forming β subunit, 5-hydroxytryptamine receptor 2A (HTR2A), WD repeat and FYVE domain containing 2 (WDFY2), asparagine-linked glycosylation 11, NIMA related kinase 3, and cytoskeleton associated protein 2. The SNP on GGA1 possessing a MAF of more than 0.4 and the values of beta on GGA1 were all negative  of HTR2A, rs315120631 located on the intron of GTF2F2, rs13972990 was downstream  of WDFY2.By using gene annotation analysis, it was obtained that the 17 significant SNPs on GGA1 were harbored in nine genes, general transcription factor IIF polypeptide 2 , leucine aminopeptidase 3, LIM domain binding 2, quinoid dihydropteridine reductase, prominin 1, and transmembrane anterior posterior transformation 1. We found one locus rs14491030 that located in the missense of the gene NCAPG and another SNP close to NCAPG.SHH) and close to genes 5-Hydroxytryptamine receptor 5A, insulin induced gene 1, and limb development membrane protein. The SNPs rs316413705 explained the highest CPV (3.109%) for HW.rs312726815, rs315120631, and rs13972990 on GGA1 as primary candidate loci, with correspond to genes HTR2A, GTF2F2, and WDFY2 associated with HW, LW, and PW/GW, respectively. The NCAPG gene harbored the missense locus rs14491030 on GGA4 were considered as a candidate gene for all internal organs, SHH gene with intron harbored the SNP rs14491030 on GGA2 had an association with HW.The LD analysis was performed because of the potentially strong LD between neighboring variants. The LD analysis results revealed that significant SNPs on GGA1, GGA4, or GGA2 were all extremely strong in a LD status . Combine2 population. Only the partitioning spectrum of LW and GW were estimated. The estimates of variance contributed by each chromosome exhibited a medium and strong linear relationship with the length of the chromosome for LW  for whiOur results showed that phenotypic data displayed a large variation for all internal weights, which was probably because these traits were not chosen as selection index. The heritability showed a moderate to high, higher than that the previous reports. For LW, the heritability estimates were 0.31  and 0.33Using GWAS method, we totally found 17 SNPs on GGA1 and 6 SNPs on GGA4 associated with all internal organ weights, and 6 SNPs on GGA2 were associated with HW. Previous studies showed that QTL for internal organ weight, such as HW and LW, at 9 weeks was detected on GGA1 and GGA4, respectively . HW and The most significant loci in the GGA1 spanned from 166.99 to 169.66 Mb, which is also related to comb weight , egg weirs3127 26815, rs315120631, and rs13972990, correspond to genes HTR2A, GTF2F2, and WDFY2, respectively, were selected as the candidate loci. HTR2A was considered as a candidate gene for HW. The mRNA of the gene, which encodes a receptor of 5-hydroxytryptamine (serotonin), is expressed within vascular smooth muscle, endothelial cells, and cardiomyocytes, and plays a role in vasoconstriction and cellular proliferation [HTR2A-encoded protein is associated with blood pressure and heart rate and is a contributing factor to cardiovascular disease in humans [HTR2A was responsible for heart weight, the detailed information needs to be further investigated. Second gene GTF2F2 nearby SNP rs315120631 was considered as candidate gene for LW. GTF2F2 forms a heteromeric general transcription initiation factor [GTF2F2 widely exists in many tissues and organs, especially in the liver, lung, and kidney. The liver is the main organ regulating body homeostasis [GTF2F2 is association with rat liver regeneration [GTF2F2 in chicken internal organs are needed. The third SNP, rs13972990, is near gene WDFY2, which is associated with PW and GW. WDFY2 (WD repeat and FYVE domain containing 2) is an endosomal protein, modulating the PI3K/AKT pathway, which is known to be involved in oncogenesis [WDFY2 overexpression can increase adipogenesis, which may play a role in metabolic disorders such as diabetes in humans [WDFY2 may participate in proventriculus and gizzard growth though its involvement is not clear.After rigorous statistic and LD analysis, three SNPs feration . Also, tn humans . In birdn humans . Therefon factor . GTF2F2 eostasis , where aneration . Liver iogenesis . WDFY2 oNCAPG harbored the missense rs14491030 was a candidate gene for all internal weight. The gene was related to residual feed intake in cattle [NCAPG was considered as candidate gene for all internal organ weight though no selection for these traits, pleiotropic effects of the locus on other traits that are under selection, or close linkage and LD with QTL that are under selection [Moreover, the region on GGA4 was related to egg weight, eggshell weight, oviduct weight in our population ,40. NCAPn cattle  and withn cattle  in previelection . We provSHH could be considered as a candidate gene. The SHH gene is one of the Hh proteins, which plays a crucial role in the development of all animals and regulates morphogenesis of a variety of tissues and organs in the embryo [SHH is necessary for secondary heart field proliferation in humans [SHH in the chicken heart may involve heart differentiation.In addition, GWAS results showed that one QTL on GGA2 associated with HW. The e embryo . Previoun humans . ConsequIn present study, a GWAS strategy was performed to detect potential QTLs or genes association with internal organ traits. Our study provides evidence that the internal organ weight at 72-weeks old appears to moderate a high heritability and may share the similar genetic mechanisms. Five candidate genes were identified with significant effect on internal weights. These promising loci or genes could be helpful to simultaneous improve layer chicken economic trait and fitness trait to accomplish sustainable use of the chicken.


The trans mQTL SNPs were found to be over-represented in 1 Mbp subtelomeric regions, and on chromosomes 16 and 19. There was a significant increase in trans mQTL DNA methylation sites in upstream and 5′ UTR regions. The genetic heritability of a number of complex traits and diseases was partitioned into components due to mQTL and the remainder of the genome. Significant enrichment was observed for height (p = 2.1 × 10−10), ulcerative colitis (p = 2 × 10−5), Crohn’s disease (p = 6 × 10−8) and coronary artery disease (p = 5.5 × 10−6) when compared to a random sample of SNPs with matched minor allele frequency, although this enrichment is explained by the genomic location of the mQTL SNPs.DNA methylation plays an important role in the regulation of transcription. Genetic control of DNA methylation is a potential candidate for explaining the many identified SNP associations with disease that are not found in coding regions. We replicated 52,916  The contribution of genetic variation to the inheritance of DNA methylation levels across a range of tissues has been widely demonstrated both through studies investigating the heritability of DNA methylation using twin pairs and families6, and through the identification of methylation quantitative trait loci or mQTL acting in both cis and trans21.DNA methylation plays an important role in transcriptional regulation and is increasingly recognised as having a role in health and disease22, it is hypothesised that the SNPs act through the perturbation of the regulation of gene-expression. DNA methylation QTL have been associated with other genomic marks that affect gene regulation, including DNase I accessibility and histone modifications17, as well as directly with gene-expression16. Therefore, they are potential causal variants for disease. Indeed, the overlap between mQTL and disease SNPs has been investigated previously, finding inflation for the number of mQTL in bipolar risk SNPs11, schizophrenia18 and autoimmune disease17.As the majority of single nucleotide polymorphisms (SNPs) associated with complex traits and disease are found in non-protein coding regionscis to DNA methylation sites, the mQTL SNPs are also preferentially located in genic regions. Genic regions are also known to explain a larger proportion of the genetic variation underlying complex traits and disease23. Therefore, any analysis looking into the overlap of mQTL with SNPs identified in genome-wide association studies (GWAS) needs to account for the proportion of methylation sites assessed in different genomic regions. In addition, determining the overlap between a mQTL and disease SNP often uses criteria such as an arbitrary linkage disequilibrium (LD) threshold of r2 > 0.8 between the best disease GWAS SNP and the mQTL SNP. This implicitly assumes that a common causal variant for the mQTL and disease is being tagged by two different SNPs, rather than there being two different causal variants.These published studies indicate that mQTL have an influence in disease risk, however some aspects of the methodological approach in determining the significance of the overlap may be sub-optimal. For example, most identified mQTL have been found using Illumina HumanMethylation arrays, but the analytical methods have not recognised that the measures of DNA methylation are distributed non-randomly throughout the genome. Most of the DNA methylation probes on these arrays are located in genic regions, and, given that the majority of mQTL are found in 24 and the Lothian Birth Cohorts of 1921 and 1936 (LBC)27. We then use LD Score regression29 to partition the genetic variation for complex traits and diseases into components due to mQTL SNPs and the remainder of the SNPs in the genome using summary statistics from large GWAS meta-analyses. These results are compared to null distributions generated by selecting random sets of SNPs that have been matched by allele frequency or by both allele frequency and genomic annotation. This analysis both avoids the selection of an arbitrary linkage disequilibrium threshold above which mQTL and disease SNPs are considered as overlapping, and accounts for the non-random distribution of methylation sites tested across the genome, providing an unbaised assessment of the role of mQTL in complex traits and disease.In this study we identify >50,000 mQTL in whole blood that are replicated at a stringent significance level in the Brisbane Systems Genetics Study (BSGS)cis SNP effects on DNA methylation, we firstly tested for association in a window spanning 2 Mbp on both sides of the target CpG site. This window is larger than what is usually considered for cis mQTL, but our prior observation of significant cis mQTL effects spanning this far in the MHC region on chromosome 6 indicated a larger window is warranted6. This was further justified by noting that the number of cis mQTL rapidly drops off to a constant background level between 1 and 2 Mbps from the target CpG site  replicated in the other cohort at Bonferonni corrected significance threshold of p < 10−6 and also had SNP effects on DNA methylation in the same direction in the other cohort. The correlation of cis mQTL effect sizes between the two cohorts was 0.97. Thus we have stringently replicated cis mQTL for more than 13% of the methylation sites tested.A total of 62,257 and 61,180 Trans mQTL were defined using a more stringent significance threshold of p < 10−13 to account for the extra multiple testing burden from testing association with the whole genome. The number of significant trans mQTL found in the BSGS and LBC was 2,454 and 2,048 respectively. Of these, 2,025 replicated in the other cohort with a Bonferonni corrected p-value of p < 10−5 and also had the same direction of effect. The correlation in trans mQTL effect sizes across the two cohorts was 0.91. The location of the replicated mQTL are given in Fig. cis- and trans-mQTL in independent samples demonstrates the high quality of the data and reliability of the results.cis mQTL explaining greater than 50% of phenotypic variation. While trans mQTL still explain a substantial proportion of the phenotypic variance, the overall distribution has fewer mQTL explaining very large amounts of variance. The effect of the “winner’s curse”, where the variance explained by the top SNPs identified in a GWAS is biased upwards, is likely to be small in this study given the stringency of testing and the high replication rate.The proportion of phenotypic variation in DNA methylation levels explained by all replicated mQTL in the LBC cohort is given in Fig. trans mQTL, it is very unlikely that a SNP in the probe site was associated with the mQTL SNP, particularly given the very stringent significance thresholds that were used for mQTL mapping. This is reflected in 499 (25%) trans mQTL having a SNP in the probe site, which is the same as the null proportion of probes that do not have an associated mQTL that have SNPs in their binding site . SNPs were found within the probe binding site for 22,267 (42%) of cis mQTL. Thus, we can potentially attribute 15% (42–27%) of cis mQTL to genetic variation within the probe location causing genotype specific measurement error. However, it can also be argued that the majority of cis mQTL are found within a very small distance of the probe location, and it would not be surprising for genetic variation very close to a CpG site to have a genuine effect on methylation levels. To take an extreme example, a SNP falling within a CpG site completely disrupts DNA methylation at this site, which occurs for 6,160 (12%) of cis mQTL. For this reason, we include all mQTL – regardless of the identification of SNP within the probe site – in the further analyses.There is potential for SNPs located within DNA methylation probe binding regions to have an effect on the measurement of methylation levels, and thus potentially create false positive mQTL. To address this, we used the 1000Genomes (v3) European samples to identify any genetic variation within a probe site and identified a SNP in 27% of the probes passing QC. It is of note that many of the SNPs identified within probe sequences are rare and would not be in strong linkage disequilibrium with the common (>1% frequency) SNPs used for the GWAS. For trans mQTL SNPs is non-randomly located throughout the genome. This is investigated in Fig. trans mQTL SNP located on chromosomes 16 and 19 given their respective sizes. This may not be surprising under a polygenic model of inheritance given those chromosomes have a higher gene density than other chromosomes. However, this inflation is beyond that expected given the gene count on those two chromosomes. The rest of the genome shows a strong correlation between number of genes on a chromosome and the number of trans mQTL SNPs, except for chromosome 1 which has fewer trans mQTL SNP than expected. Of interest, chromosome 19 contains DNMT1 (DNA methyltransferase 1) that has a role in the establishment and regulation of DNA methylation. However, there is no clustering of trans mQTL SNPs around its location.From Fig. trans mQTL SNP are located in telomeric regions covering the 1 Mbp at the end of chromosomes, which represents 1.53% of the genome. There is also some inflation of the numbers of trans mQTL methylation probes found in the 1 Mbp subtelomeric region (7.0%), but this is primarily due to the increased number of array probes in the subtelomeric region (5.5%) and this inflation is reflected in the number of cis mQTL methylation probes also (7.5%). Given the association with trans mQTL SNP in subtelomeric regions, we tested whether the trans CpG probes or SNPs were significantly associated with telomere length in the LBC1936 cohort. This identified no inflation of test statistics for either the SNPs or methylation compared to the whole genome .To assess the role of mQTL in driving the phenotypic variation of complex traits and disease, we used LD Score regression2 of greater than 0.8. This allows for straightforward generation of sets of SNPs to estimate the distribution of variance explained under the null hypothesis, as then the LD structure is similar to that of a random set of minor allele frequency matched SNPs. Two different null hypotheses were used. The first (null #1) accounted for the fact that on average SNPs with a higher heterozygosity explain more variation in a trait by drawing random sets of SNPs with a matched minor allele frequency (in bins of 0.05 width). The second (null #2) in addition matched the genomic location of randomly sampled SNPs using annotation from ANNOVAR38. This accounts for the observation that a large proportion of the genetic variation in complex traits is explained by genic regions and that the array (and thus cis mQTL locations) is very gene centric.The replicated mQTL were firstly filtered to have no SNP pairs with an estimated rUnder null #1, height, ulcerative colitis, Crohn’s disease and coronary artery disease all showed a significant inflation of the proportion of genetic variation explained by mQTL Table , althougf is the minor allele frequency of the SNP. After correcting for minor allele frequency, no significant correlation was observed between the effects sizes of the SNPs on the mQTL and the corresponding SNP effect sizes on any of the tested traits  on both DNA methylation and the trait was used as it is expected that there will be variation in whether DNA methylation is protective or not for different regions of the genome. In addition, the effect sizes were corrected for the expected relationship between effect size and minor allele frequency by multiplying the effect size by ts Table .cis and 2,025 trans mQTL that are replicated across two independent cohorts at very stringent significance levels. While the mQTL can explain a large proportion of the genetic variation underlying DNA methylation variation, there is still substantial genetic variation remaining to be explained. Using the twin family structure in the Brisbane Systems Genetics Study, we have previously shown that the average heritability of DNA methylation at sites measured by the Illumina HumanMethylation450 array is 0.1876. The average proportion of phenotypic variation explained by all mQTL across all DNA methylation probes in this study (including probes that had no mQTL and thus explained zero variation) is 0.021. Thus, the mQTL identified here explain approximately 11.2% of the total genetic variation for DNA methylation. This implies there is substantial genetic variation for DNA methylation remaining to be discovered through additional variants in cis and/or many more trans variants with small effects in larger samples.We have identified 52,916 cis to the DNA methylation probes, which also tend to be found in genic regions due to the design of the array, combined with the observation that genic regions explain more of the heritability for many traits23. Previous studies that have shown a relationship between mQTL and bipolar disorder11 and schizophrenia18 QTL whilst only considered MAF when sampling SNPs for the null distribution, and, as demonstrated here, the results are likely to be driven by the common genomic function of the SNPs. Testing for a role of mQTL in complex traits and disease beyond that explained by genomic location is difficult due to the large number of mQTL replicated in this study. This means that a large proportion of genes in the genome are tagged by an mQTL and any null sample of SNPs will cover many of the same genomic regions. This makes any test for the proportion of heritability explained by mQTL being extremely conservative.By partitioning heritability into components due to mQTL SNPs and the rest of the genome, we established that the identified mQTL explained a significant amount of the genetic variation for a number of complex traits and diseases. Using a null distribution generated by randomly sampling SNPs from the genome with matching minor allele frequencies showed significant amounts of genetic variation were explained by mQTL for height, schizophrenia, ulcerative colitis, Crohn’s disease, and coronary artery disease. This enrichment of mQTL in disease associated regions was explained by the genomic location of the mQTL SNP. This is due to most mQTL SNP being 41, or in combination with other genomic data44. Reducing the set of potential causal variant(s) underlying a mQTL using these approaches is helped by the large amount of phenotypic variation the mQTLs explain. There is also strong potential to determine causal SNPs for mQTLs in cell lines using CRISPR genome editing45 as the end phenotype is directly observable in the cell, unlike the case for complex traits and disease where a phenotype to investigate in cell lines is generally unclear.Determining whether associations detected in the same genetic region for DNA methylation and a disease are the result of (mediated) pleiotropy or just close linkage is a difficult prospect. To have potential for pleiotropy, the set of potential causal variants for the two associations will need to overlap. Fine-mapping to a set of potential causal variants can be determined by statistical prioritisation using only association statisticstrans mQTL SNP in the 1 Mbp subtelomeric region of the genome, as had been previously noted17. No association of the trans mQTL SNP or methylation probes was found with telomere length in the LBC1936 cohort. The trans mQTLs were significantly inflated for methylation probes found in the upstream regions of genes, indicating a potential effect on the regulation of gene-expression. However, there was no overlap with trans eQTLs identified in the BSGS24. The mechanism and potential importance of subtelomeric regions in altering DNA methylation throughout the genome warrants further investigation and at this stage artefacts of the technology cannot be excluded.We observed a strong over-representation of cis and trans. We demonstrated an overlap of mQTL and loci for complex traits and diseases, which was explained by the genomic location of the mQTL SNPs.In summary, we have identified and replicated a large number of genetic loci associated with DNA methylation in both 24. Families consist of adolescent monozygotic (MZ) and dizygotic (DZ) twins, their siblings, and their parents. DNA was extracted from peripheral blood lymphocytes by the salt precipitation method46. The BSGS study was approved by the Queensland Institute for Medical Research Human Research Ethics Committee, and all methods were performed in accordance with the relevant guidelines and regulations. All participants gave informed written consent.DNA methylation was measured on 614 individuals from 177 families of European descent recruited as part of a study on adolescent twins and selected from individuals in the Brisbane Systems Genetics Study27. The LBC1921 and LBC1936 are longitudinal studies of ageing, with a focus on cognition, in groups of initially healthy older people. DNA methylation was measured in 446 LBC1921 subjects at an average age of 79 years, and in 920 LBC1936 subjects at an average age of 70 years47. Following informed consent, venesected whole blood was collected for DNA extraction by standard methods in both LBC1921 and LBC1936. Ethics permission for the LBC1921 was obtained from the Lothian Research Ethics Committee (Wave 1: LREC/1998/4/183). Ethics permission for the LBC1936 was obtained from the Multi-Centre Research Ethics Committee for Scotland (Wave 1: MREC/01/0/56), the Lothian Research Ethics Committee (Wave 1: LREC/2003/2/29) and all methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all subjects.Methylation data were analysed from the combined data of the Lothian Birth Cohort 1921 (LBC1921) and the Lothian Birth Cohort 1936 (LBC1936)47. The HM 450 BeadChip-assessed methylation status was interrogated at 485,577 CpG sites across the genome. It provides coverage of 99% of RefSeq genes. Methylation scores for each CpG site are obtained as a ratio of the intensities of fluorescent signals and are represented as β-values. DNA methylation data for the BSGS is available at the Gene Expression Omnibus under accession code GSE56105, and the LBC data is available at the European Genome-phenome Archive under accession number EGAS00001000910.DNA methylation was measured using Illumina HumanMethylation450 BeadChips as described in detail elsewhere48 were removed from the analysis, as were non CpG sites. Probes with excess missingness or high numbers of individuals with detection p-value less than 0.001 were also removed. After cleaning, 397,710 probes remained for association analysis in both cohorts.Probes on the sex chromosomes or having been annotated as binding to multiple chromosomes2, sex × age and sex × age2. In addition, the LBC data were corrected for white blood cell counts . The LBC data were normalised for the two cohorts individually before combining the data for further analysis.Array data were background corrected, followed by individual probes being normalised using a generalised linear model with a logistic link function. Corrections were made for the effects of chip (which encompasses batch processing effects), position on the chip, sex, age, ageOutlying data points can result in a high number of false positive in GWAS analysis when associated with rare variants. To address this, the BSGS cohort removed any measurement at a probe that was greater than five interquartile ranges from its nearest quartile. In the LBC, probes that had such outliers were restricted to testing association with SNPs having a minor allele frequency greater than 5%.50. After standard quality control, the BSGS and LBC had 528,509 and 549,692 SNPs remaining respectively.Both the BSGS and LBC were genotyped on Illumina 610-Quad Beadchip arrays, with full details of genotyping procedures described elsewhere52 and imputed against 1000 Genomes Phase I Version 354 using Impute V256. Raw imputed SNPs were filtered to remove any SNPs with low imputation quality as defined by an r2 < 0.8. Subsequent quality control removed SNPs with MAF < 0.05, and those with HWE p < 1 × 10−6. The “best-guess” (highest probability) genotype was used for the GWAS analyses.The remaining genotyped SNPs were phased using SHAPEIT57 using the–fastAssoc option for the BSGS cohort (to account for family structure) and PLINK58 for the combined LBC cohorts.Genome-wide association (GWAS) was performed individually on the BSGS and LBC cohorts, with each serving as an independent discovery cohort and replication performed in the other. Association testing was performed using MERLINcis region to the methylation probe – defined as a window 2 Mbp each side of the target CpG site location – was investigated. A significance threshold of 10−11 was used, which is a stringent p = 0.05 Bonferroni correction for the approximate number of independent SNPs in the window and number of probes analysed. Significant associations were replicated with a Bonferroni corrected (based on the approximate number of independent mQTL) p-value of 10−6 and having effect in the same direction in the other sample. When a single methylation probe had a replicated association from both cohorts but at a different SNP, the SNP with the best combined evidence of association was selected for further analyses.To reduce the massive computational burden, GWAS was performed in two stages. Firstly the trans SNPs  was performed in two steps. Firstly, all chromosome/probe pairs were analysed on non-imputed genotyped data, which reduced the number of tests performed by a factor of 10. This was particularly important for the BSGS cohort which had related individuals and thus was much slower to analyse. Any chromosome/probe pair that had an association at p < 10−7 was then reanalysed using imputed SNP data. An experiment-wide significance of 10−13 was used for trans associations, which is the standard GWAS genome-wide significance threshold of 5 × 10−8 Bonferroni corrected for the number of probes tested. The replication threshold of 10−5 was used, again being more stringent than a 5% significance Bonferroni corrected for the number of associations to be replicated.Association with 38, using the hg19 annotation with the distance of the upstream and downstream regions of genes being 2 Mbp to align with our definition of cis loci.SNPs and the CpG targets of methylation probes were functionally annotated using ANNOVAR59. The intra-assay coefficient of variation was 2.7% and the inter-assay coefficient of variation was 5.1%. Four internal control DNA samples were run within each plate to correct for plate-to-plate variation. These internal controls are cell lines of known absolute telomere length whose relative ratio values  were used to generate a regression line by which values of relative telomere length for the actual samples were converted into absolute telomere lengths. Measurements were performed in quadruplicate and the mean of the measurements used. PCRs were performed on an Applied Biosystems  7900HT Fast Real Time PCR machine.Telomere length was measured using the same blood sample as methylation in the LBC1936 cohort using a quantitative real-time polymerase chain reaction (PCR) assay29. The sum of the LD r2 values for between that target SNP and all other SNPs within the 1 Mbp region centred on the target SNP60, and was calculated using the European samples from the 1000 Genomes project54 using the software GCTA (–ld-score option)61. The LD score at a SNP, j, is then calculated as:n is the number of SNP in the window and N is sample size used to calculate the r2 measures.The heritability of a trait explained by all GWASed SNPs was partitioned in to a component due to all discovered mQTL and all remaining SNP using LD Score regressionj. The heritability attributable to mQTL is calculated asMmQTL is the number of mQTL SNPs and NGWAS is the sample size of the GWAS from which the summary statistics were obtained. The heritability attributable to the rest of the genome is calculated similarly.Using the summary statistics from a large GWAS for a quantitative trait or disease, the heritability of the trait is partitioned into components due to mQTL and the rest of the genome using a regressionSupplementary Figures and TablesSupplementary table 1: List of replicated mQTL


PED = 0.164; 0.103 to 0.306) and outbred Retinta bulls . We studied the length of the fragments, their abundance, and genome distribution using high-density microarray data. The number of ROH was significantly higher in the HI group, especially for long fragments (>8Mb). In the LI group, the number of ROH continuously decreased with fragment length. Genome-wide distribution of ROH was highly variable between samples. Some chromosomes presented a larger number of fragments , others had longer fragments , while other ones showed an increased ROH accumulation over specific loci . Similar differences were observed in the analysis of 12 individuals produced by a similar inbred event (FPED3 = 0.125). The correlation between the fraction of the genome covered by ROH (FROH) and FPED was high (0.79), suggesting that ROH-based estimations are indicative of inbreeding levels. On the other hand, the correlation between FPED and the microsatellite-based inbreeding coefficient (FMIC) was only moderate (r = 0.44), suggesting that STR-based inbreeding estimations should be avoided. Similarly, we found a very low correlation (r = -0.0132) between recombination rate and ROH abundance across the genome. Finally, we performed functional annotation analyses of genome regions with significantly enriched ROH abundance. Results revealed gene clusters related to pregnancy-associated proteins and immune reaction. The same analysis performed for regions enriched with recently formed ROH (> 8 Mb) showed gene clusters related to flagellum assembly. In both cases, the processes were related to male and female reproductive functions, which may partially explain the reduced fertility associated with inbred populations.The analysis of runs of homozygosity (ROH), using high throughput genomic data, has become a valuable and frequently used methodology to characterize the genomic and inbreeding variation of livestock and wildlife animal populations. However, this methodology has been scarcely used in highly inbred domestic animals. Here, we analyzed and characterized the occurrence of ROH fragments in highly inbred (HI; average pedigree-based inbreeding coefficient F Inbreeding depression is the reduced survival and fertility of offspring from related individuals. According to Leroy , the decThe increase in the average inbreeding coefficient observed in cattle during the last 20 years may have been caused by two main reasons: 1) the high selection intensity applied in the breeding schemes of highly productive breeds , and 2) PED) [MIC) [PED, even when large pedigrees are considered [The F coefficient has been conventionally estimated from pedigree data (FPED)  or microD) [MIC) . HoweverD) [MIC) . Nowadaynsidered . Additionsidered , 20. Onensidered . ROH anansidered  in severnsidered –24 and dnsidered , 26 cattnsidered , allowinMicroarray density and parameter settings have been proven essential for accurate ROH identification . In thisIn humans, the accumulation of ROH in certain genomic positions has been used to analyze the demographic history of populations . This stPED coefficients within the population. This makes the Retinta breed an interesting model for animal inbreeding research.Retinta is the second largest Spanish cattle breed characterized by the quality of its meat, its rusticity and its adaptation to marginal pasturelands and extreme weather . All thePED values over the last 3 generations to evaluate the reliability of FPED as a predictor of inbreeding depression. Finally, we estimated the influence of the recombination rate on the ROH patterns and identified loci putatively affected by inbreeding in highly related individuals through a functional annotation analysis.The aims of this study were to characterize the occurrence of ROH in a Retinta population with extremely inbred and outbred animals. We analyzed the length, number, and genomic distribution of the ROH, obtained from HD genotyping data, as well as the relation between long ROH and recent inbreeding events retrieved from pedigree and microsatellite records. We also determined the differences among ROH patterns in a group of individuals with the same increase in FAll the samples were obtained from the National Association of Breeders of Select Retinta Cattle . DNA was obtained from frozen sperm straws (one per bull) using the HigherPurity™ Tissue Genomic DNA Purification Kit . Management and treatment of the animals during sperm extraction were complied the ethical guidelines of the Animal Ethics Committee of the University of Cordoba, Spain. No ethics committee approval was necessary since no experimental procedures were carried out in animals during this study.PED values. FPED, FPED3 (FPED for last three generations) and ECG values were determined as described by Meuwissen and Luo [MIC was determined using a 17 STR-based genomic test recommended by the International Society for Animal Genetics for parentage testing as described by Caballero and Toro [PED = 0.164; 0.103 to 0.306) and outbred .In total, we analyzed 54 bulls belonging to the artificial insemination (AI) program of the National Breeders Association of Select Retinta Cattle . Individuals were selected based on the number of equivalent complete generations (ECG) and F and Luo  and Maig and Luo  using EN and Luo . FMIC waand Toro  using Moand Toro . For theSamples were genotyped using the Axiom® Genome-Wide BOS 1 Bovine high-density SNP Array  in a GeneTitan® Multi-Channel platform (Affymetrix). Samples were run at the IGEVET Genomics Core Facility . Raw data were processed using Axiom™ Analysis Suite software (Affymetrix) and setting call rates and DQC at 0.97. Commonly used genotype filters such as minor allele frequency (MAF) were not applied since they would have led to an underestimation of ROH . MAF filns is the number of SNPs per individual, ni is the number of individuals, α is the percentage of false positive ROH (0.05), and het is the average SNP heterozygosity across all SNPs. ROH were divided into five different categories according to their length: 1–2 Mb, 2–4 Mb, 4–8 Mb, 8–16 Mb and >16 Mb. Short ROH (< 1 Mb) were not included in the analyses since many of them may derive from the inheritance of common allozygous haplotypes [ROH were estimated using cgaTOH software . The minplotypes .nH) and missing (nM) genotypes allowed in each ROH category for each chromosome was calculated asmL is ROH minimum length, dS is the average distance between SNPs in the chromosome, eG is the genotyping error rate (0.25% according to Affymetrix standard procedures), and mG is the average missing genotype rate in the chromosome. The number of missing and heterozygous SNPs allowed per length category and chromosome are shown in reduce function of the GenomicRanges R package [The number of heterozygous  length, according to McQuillan, Leutenegger [The mean, minimum and maximum length of the ROH and the standard error were determined for each sample group globally and also for each chromosome, individual and length category. Statistical differences were analyzed using a T standard test with a significance threshold of 0.05. Ftenegger . ResultsROH as inbreeding estimator compared with FPED, FPED3, and FMIC through Spearman correlations, since the distribution of the SNP data among samples was not normal (Kolmogorov–Smirnov test). The correlation between FROH>8Mb and FPED (six generations), as well as between FROH>16Mb and FPED3 (three generations), were particularly estimated to evaluate the relationship between the length of ROH and the number of generations since the common ancestor, as was proposed by Fisher [We evaluated the reliability of Fy Fisher .To determine if ROH were accumulated over loci with low recombination rates, we estimated the correlation between the number of ROH detected at a given position and the recombination rate at the same locus. To this end, recombination rate at each genomic position was obtained from data reported by Ma, O'Connell . This waCog_Ontology, Up_Keywords, Up_Seq_Feature, Goterm_Bp_Direct , Goterm_Cc_Direct (cellular components), Goterm_Mf_Direct (molecular functions), Kegg_Pathway, Interpro, and Smart. The classification stringency was set to medium and groups were defined by enrichment scores greater than 1.301, which is equal to–log10 (0.05). The analysis was performed on the full set of statistically enriched regions  and on a reduced set comprising only ROH>8Mb to evaluate the effect of recently formed fragments.To identify putative effects on the biological functions of the highly inbred animals, we performed a functional annotation analysis considering genes within loci that were statistically enriched with ROH. These loci were determined by performing a permutation test under the null hypothesis that ROH are equally distributed along the genome. The number of ROH at each position was randomized one million times using newly designed scripts in JULIA . Then, tThe number of ROH was statistically different between the HI and LI groups . This statistical difference was also observed at the length category level . The HI/A similar analysis performed by chromosome showed similar results, with statistical differences in 25 different chromosomes  , but HI h of ROH . SamplesSignificant differences were also observed in the length of ROH by chromosome but in a lesser extent. Only seven chromosomes showed statistical differences between groups , showing a high correlation with FPED (0.79). On the contrary, correlations between FPED and FMIC (0.44) and FROH and FMIC (0.36) were much lower, which is consistent with comparisons performed between groups. Correlations between FPED and FROH>8Mb, FPED3 and FROH>16Mb, and FPED3>0  and FROH>16Mb were 0.60, 0.54 and 0.42, respectively.In the HI group, FROH values were significantly higher in the HI group (P<0.001). The percentage of FROH value explained by each length category is shown in ROH value in LI, showing a decrease in contribution towards the 8–16 Mb category. On the other hand, a similar contribution was observed for each length category in HI. The percentage of FROH explained by short fragments (1–2 Mb) was significantly higher in LI, whereas the percentage explained by long fragments  was significantly higher in HI.FROH in HI in 26 chromosomes  and the recombination rate at the same locus was -0.0132. Similar results were obtained when the analysis was performed by group (r = -0.0178 in HI and r = -0.02715 in LI) and chromosome . When alThe accumulation of ROH across the genome of the highly inbred samples was analyzed by length category: all ROH lengths , ROH>8MbROH and average length of ROH in the HI group   . The patTo analyze putative biological functions affected in zones with high ROH abundance, we retrieved a list of genes from the intervals significantly enriched based on the permutation test. As before, to characterize recently formed fragments, we conducted separate analysis for all ROH fragments (no length restriction) and for ROH>8Mb. The lists of genes obtained were then submitted to DAVID Bioinformatics for functional annotation clustering. When all fragment lengths were considered, the number of retrieved known genes was of 769. These genes were distributed across 22 different chromosomes, but eight of them were lightly represented (less than 10 genes). The most represented chromosomes in terms of genes covered by ROH were BTA7, BTA14, and BTA12 with 108, 94 and 77 genes, respectively. Three significant functional clusters were detected in the annotation clustering analysis . The oneDNAAF1, DNAH1, LRRC6, and ZMYND10). The remaining two clusters were related to calcium binding and metabolism, and hyaluronan metabolism.When focused on regions ROH>8Mb, that hypothetically formed during the last six generations, we retrieved 362 known genes. These genes were distributed over seven chromosomes. BTA22, BTA24, and BTA14 were the most represented ones, with 87, 68 and 64 genes, respectively. Three significant clusters were identified in the functional annotation analysis . The mosIn this study, we characterized the abundance and distribution of ROH in inbred and outbred cattle. To our knowledge, this is the first genomic report to use HD genotyping to analyze the relation between IBD fragments and known recent inbreeding events in individual cattle with extreme F values. This is also the first analysis of a cattle population that has been bred and selected under standardized conditions and can be clustered into two highly divergent groups in terms of individual inbreeding values.PED and FROH in comparison with previous reports [ROH and FPED: 1) the persistence of ancestral short ROH through time due to low recombination rates, which are ignored in the estimation of FPED [ROH values, which may indicate that inbreeding events that occurred in the population before the fifth parental generation are scarce. For this reason, our correlations are hypothetically reliable. On the contrary, the STR-based inbreeding estimations (FMIC) were poorly correlated with both FPED and FROH, demonstrating that the use of STR is a poor source of information in highly inbred individuals. This fact was previously described in cattle but using a moderately inbred population, as was shown by Baumung and Solkner [Our results showed a higher correlation between F reports , but onl reports . Three c of FPED , 2) the  of FPED , 56 and  of FPED . In this Solkner .PED. Since our population had a well-known history of recent inbreeding, we expected a higher correlation between estimates coming from long ROH (FROH derived from ROH>8 and ROH>16) and FPED values from the corresponding number of generations (FPED for six and FPED3 for three). There was a general agreement between increased ROH lengths and inbreeding events in the inbred individuals, showing correlations between r = 0.6 and r = 0.42 when the long ROH fragments were used to estimate FROH. However, these correlations were lower than the overall correlation obtained using the analysis of the whole population. One possible explanation is the stochastic nature of IBD inheritance [PED can be easily underestimated when the pedigree depth is shorter than 20 generations. In our case, FPED was estimated using only 6 generations on average, thus the influence of previous common ancestors was not accounted for.Seven years ago, Howrigan, Simonson  modeled eritance . Howevereritance  demonstrPED. A similar variability was also described in Brown Swiss cattle and Valle de Belice sheep [PED and ROH length.The length of the ROH was highly variable between and within the two sample groups. For instance, five LI samples showed ROH longer than 20 Mb, when presence of long ROH was not expected. Although the mean length of the ROH should at some extent reflect the number of generations since the common ancestor, we should also consider that ROH formation is a complex process involving dynamic rates of recombination along the genome and the stochastic nature of gamete formation processes . In our ce sheep , 38. HowSignificant differences were also observed in the number of ROH detected by length category. Shorter ROH predominated in LI, but the number of ROH decreased as the length increased. This pattern may be attributed to a “foundational inbreeding” produced during the creation of the breed, as suggested in other breeds and species , 24, 59.In a recent study, Zavarez, Utsunomiya  reportedD. melanogaster. In that study, the author demonstrated that the chance of finding a homozygous genotype at a specific position of the genome after an event of inbreeding depends on the recombination rate of the locus and the length of the chromosome. According to the author, the abundance of inbreeding blocks should be higher in chromosomes longer than 1M, but the variability should be lower in chromosomes shorter than 0.5M. To show that, we analyzed the distribution of recently formed ROH (>8Mb and >16Mb) on 12 individuals with the same increase in F during the last three generations (FPED3 = 0.125). Once again, results showed an uneven genome distribution with a moderate accumulation of ROH over specific loci (up to nine ROH>8Mb on some loci of BTA24). Besides, seven chromosomes showed a total absence of ROH >16Mb. Despite the limited number of samples, our study shows that high inbreeding levels might not always lead to inbreeding depression, as was recently also shown in a study of isolated cattle [PED.The uneven genome-wide distribution of the ROH is particularly important since breeders usually associate an increase in the inbreeding coefficient with a proportional detrimental effect , 62. Howd cattle . In factThe functional study of genomic regions significantly enriched with ROH constitutes a practical approach to identify metabolic pathways putatively affected by inbreeding. In our case, we performed two separate analyses: one considering all the ROH fragments and another one considering only putatively modern ROH. The purpose of this separation was to distinguish the putative effects of short-term inbreeding from the rest. But also, it was suggested that the analysis of biological functions affected by recent inbreeding (ROH >8 Mb) is a more powerful tool than the use of ROH of shorter lengths to analyze inbreeding depression .The analysis including all the ROH showed a highly enriched annotation cluster including several precursors of the PAG family. These proteins are used as pregnancy-status indicators since their expression varies through pregnancy stages, and they are associated with placental mass, fetus number, and birth weight in cattle , 65. NowStaphylococci mastitis [In cattle, inbreeding has been also associated with changes in the immune system  and an imastitis . Retardemastitis , 79, as mastitis , even inmastitis . Both camastitis .DNAAF1, DNAH1, LRRC6 and ZMYND10 genes. All those processes have been related to an impaired axonal assembly of the dynein arms that produced an abnormal movement of flagellum and cilia [DNAH1 have been heavily associated with dysplasia of the sperm fibrous sheet [The analysis of the genomic positions affected by recently formed ROH showed three enriched clusters, but with a lower significance compared with the previous analysis. The most affected cluster was associated with microtubule structures, flagellum assembly, inositol polyphosphate kinase activity, chemotaxis and ATP-binding features. This cluster included the nd cilia , 83. Recus sheet , severalus sheet  in humanus sheet . We hypoROH as IBD predictor in extremely inbred Retinta bulls. We showed that although it is correlated to the pedigree-based inbreeding coefficient (FPED), ROH seemed to distribute in an uneven fashion among individuals with similar FROH and FPED coefficients. Furthermore, we found that individuals with a recent history of high inbreeding showed an increased number of long ROH (>8Mb). In this population, specific regions of the genome showed high accumulation of ROH, which was not associated to the recombination rate. ROH harbored genes related to pregnancy-associated proteins, cell motility, skeletal reorganization and immune system. These results are in line with a previous study where we showed that some of these bulls presented an increased hyperactivation-like motility pattern. Additionally, we demonstrated that animals with the same inbreeding coefficient may present different phenotypes, which may not always lead to a detriment in the production trait of interest.In this study, we analyzed the use of FS1 TableFootnote: L: minimum number of SNPs; nH: number of heterozygous genotypes allowed per ROH; nM: number of missing genotypes allowed per ROH.(DOCX)Click here for additional data file.S2 TableFootnote: Statistical differences were estimated between groups per chromosome using a T test.(DOCX)Click here for additional data file.S3 TableFootnote: Statistical differences were estimated between groups per chromosome using a T test.(DOCX)Click here for additional data file.S4 TableFootnote: Data was analyzed by chromosome in the whole population (WP) as well in HI and LI separately, using ROH abundance of any length or ROH>8Mb. Spearman correlation. Correlations were estimated per SNP within chromosome.(DOCX)Click here for additional data file.