Here, we used microsatellite markers to reconstruct a multi-generation pedigree of wild Atlantic salmon  using archived scale samples collected with a total trapping system within a river over a 10 year period. Using a simulation-based approach, we determined the optimal microsatellite marker number for accurate parentage assignment, and evaluated the power of the resulting partial pedigree to investigate important evolutionary and quantitative genetic characteristics of salmon in the system.Pedigree reconstruction using genetic analysis provides a useful means to estimate fundamental population biology parameters relating to population demography, trait heritability and individual fitness when combined with other sources of data. However, there remain limitations to pedigree reconstruction in wild populations, particularly in systems where parent-offspring relationships cannot be directly observed, there is incomplete sampling of individuals, or molecular parentage inference relies on low quality DNA from archived material. While much can still be inferred from incomplete or sparse pedigrees, it is crucial to evaluate the quality and power of available genetic information 2 ~ 0.40) with more than 80% power when using 28 moderately to highly polymorphic markers.We show that at least 20 microsatellites  are required to maximise parentage assignment and to improve the power to estimate reproductive success and heritability in this study system. We also show that 1.5 fold differences can be detected between groups simulated to have differing reproductive success, and that it is possible to detect moderate heritability values for continuous traits (hThe methodologies and work flow described provide a robust approach for evaluating archived samples for pedigree-based research, even where only a proportion of the total population is sampled. The results demonstrate the feasibility of pedigree-based studies to address challenging ecological and evolutionary questions in free-living populations, where genealogies can be traced only using molecular tools, and that significant increases in pedigree assignment power can be achieved by using higher numbers of markers. Pedigree reconstruction provides a robust framework for the study of population evolutionary dynamics in the wild ,2. By inSalmo salar) is an economically important species, with many wild populations subject to long-term population monitoring and biological sample collection spanning many decades. In principle, given the high level of homing to their natal rivers prior to reproduction, retrieving pedigree information in salmon is more achievable compared to many other fish species. Indeed, pedigree reconstruction in salmon and other salmonids based on DNA genotyping has been shown to be both feasible and valuable for conservation biology and evolutionary research [Atlantic salmon . Whilstresearch -30. To dresearch ,31-33. TDespite the great potential for pedigree-based studies in wild salmonid populations, there are a number of possible problems that need to be considered. First, the sampling of potential parents in any given year is often incomplete. Partial sampling regimes may be due to several factors, such as logistical constraints (e.g. very large and/or complex aggregations of fish) or issues associated with the handling of wild fish, where possible impacts on behaviour or subsequent survival must be considered. Second, precocious male parr  can be important components of breeding populations ,35 whichS. salar. Indeed, this useful information has already been exploited in a number of previous studies examining, for instance, the fresh water and marine age composition in wild and hatchery reared groups [S. salar should allow a better understanding of such parameters.Atlantic salmon of the Burrishoole River system on the west coast of Ireland have been consistently monitored since 1956, with complete annual censuses conducted since 1969 ,37-39 F, Table 1d groups , physiold groups ,41, the d groups ,42, and d groups . DespiteIn this study, we have evaluated the feasibility of a pedigree-based approach for exploring population biology and stock dynamics in the Burrishoole River Atlantic salmon. First, we performed a simulation and sensitivity analysis to detect the number of microsatellites required for maximising successful parentage assignment, given the available molecular markers, incomplete sampling regime and the variable quality of DNA in the samples available. Second, we estimated the power to detect variation in reproductive success among different groups of breeders. Finally, we estimated the detection power and confidence intervals of heritability estimates using simulations based on the empirical sampling and pedigree structure, and estimated the power gain in heritability and reproductive success detection when using higher number of markers.′ N 09°37′ W; Figure 2. S. salar in the Burrishoole River system are anadromous, typically returning to fresh water between June and September to spawn in December. The majority (i.e. 90%) of juveniles spend two and a half years in fresh water followed by one year at sea (1SW), after which they return to fresh water to spawn [The Burrishoole River system is situated in NW Ireland  were sampled on their downstream migration (March – May). Sampled individuals were sexed and measured for length (± 0.25 cm) and three to five scales were removed and archived in individual vanilla paper scale envelopes. Sex was determined on the basis of secondary sexual characteristics , which are readily discerned in post-spawned individuals. The current study utilised archived scales from ten consecutive cohorts collected between 1977 and 1986 Table b.A total of 1640 fish were sampled Figure , of whicDNA was extracted from scale samples  using a QIAamp mini kit  and 5 μl of proteinase K . The extraction was performed following the manufacturer’s instructions for DNA extraction from tissues, except that the elution volume was reduced to 100 μl instead of the suggested 200 μl in order to increase the DNA concentration. Each fish was genotyped at 14 microsatellite markers  known to amplify reliably in archived scale samples . FurtherPopGenKit package 1.0 [HWE.test.genind function (with 100000 permutations) of the adEgenet package 1.3-6 [cervus 3.0 [. Allele frequencies were estimated for every combined parent-offspring cohort pair, both in parentage inference as well as when simulating genotypes, whereby no differentiation was assumed between parental and offspring cohorts. Therefore, we assessed temporal stability of the Burrishoole Atlantic salmon using genetic differentiation statistics θST[diveRsity package 1.5.0 [Unless otherwise stated, all statistical analyses were performed using R software version 2.15.2 . Genetickage 1.0 . Within-ge 1.3-6 . Null alvus 3.0 . Allelestics θST and Joststics θST with thege 1.5.0  and by bge 1.5.0 , since ige 1.5.0 .The exclusion probability of a marker, which describes the proportion of unrelated parental candidates expected to be excluded by a marker, was calculated using the formula:pji is the allele frequency of the ith allele of locus j. Although exclusion probability is not fully informative in models with genotyping error rate, it still provides a rough estimate of parentage assignment power on a per-locus basis and across all loci.where ASTERBAYES package 2.50 [ASTERBAYES incorporates the estimation of genotyping error rate and the number of unsampled individuals when inferring pedigree structure, which provides better assignment rates especially if unsampled individuals represent a considerable proportion of the potential parent set [We used a Bayesian approach in constructing the pedigrees as implemented in the Mage 2.50 . MASTERBrent set ,57. In tASTERBAYES framework based on the repeat genotyping of a proportion of the individuals in the dataset [cervus, we detected a total of 105 near identical individuals, which included 99 pairs and 6 triplicates that had been genotyped more than once. Nineteen of these were technical replicates  and 86 were biological replicates, in which DNA was isolated from different scales that had been sampled at different times  were excluded from the parentage analyses Table . The all dataset . Using tASTERBAYES was also used to estimate the number of unsampled parents within a Bayesian framework [Mramework ,57,59. Wramework , which wsimgenotype function in the MASTERBAYES package, based on the empirical allele frequency distributions of Panel 1 markers, and by defining the relationships among individuals based on the empirical parentage assignment using 14 markers (see below) with a low probability threshold (p = 0.50) to define a higher number of parental links. The genotyping error rate and missing genotype frequency were also parameterised based on the empirical results estimated above; for every simulated genotype, error rates were sampled from the posterior probability distribution. Six different marker numbers  were generated, and each pedigree associated with each of the six separate parent-offspring cohort pairs was simulated 100 times. The markers were generated randomly (with replacement) based on the empirical allele frequency distributions of Panel 1 markers, assuming independent assortment.Genotypes were simulated to generate hypothetical, but realistic, pedigrees in order to assess optimal marker number as well as for use in heritability power analyses. We simulated genotypes based on empirical data from the Burrishoole system using the ASTERBAYES with 13,000 iterations, 3,000 burn-ins and a thinning interval of 10. The mismatch tolerance for the initial data filtering was adjusted to two loci for all simulations except with 50 loci, where the mismatch tolerance was adjusted to three. Other parameters  were the same as above. Metropolis Hasting acceptance rates were in the range of 25% - 35%, after tuning scaling constants to 0.5 and 0.1 for unsampled dam and unsampled sire, respectively, using the tunePed function in MASTERBAYES. Likewise, autocorrelation of Markov Chain was monitored in all analyses and was reasonable (r2 < 0.06) in all runs at respective thinning intervals. Next, most potential parents were accepted using one of five Bayesian posterior probability thresholds , and pedigrees were constricted based on the inferred parent-offspring relations [ASTERBAYES, simgenotype function), and the parentage analysis was otherwise performed as above. Finally, we attempted to use colony2 software for parentage and halfsib inference [The parentage assignment in the simulated parent-offspring cohort pairs was conducted in Melations . For evenference , but theASTERBAYES package  and by using highly stringent (0.95) and less stringent (0.8) Bayesian posterior probability thresholds. The higher threshold is a commonly used threshold in the literature for the MASTERBAYES package e.g. [We assigned parentage in six parent-offspring cohort pairs (with 14 Panel 1 markers) using the Mage e.g. ,57, whilAdditionally, we used both Panel 1 and Panel 2  in assigning parentage in a subset of parent-offspring cohort pairs (1977–1981 and 1979–1983). This further analysis was used to determine the detection power of heritability estimations (see below) and to compare empirical and simulated parentage efficiency.Within a cohort, we simulated two hypothetical groups with differential reproductive success and estimated the detection power of this difference based on the demographics of Burrishoole salmon , and mopedantics package 1.04 [ASTERBAYES . The unsampled male and female numbers and error rates for the MASTERBAYES routine were the same as above. Then, phenotypic data were generated 100 times for every heritability value, using the pedigree information. Next, heritability was estimated from the simulated phenotypic data with an animal model using the PEDIGREEMM package 0.2-4 [p < 0.05) were calculated for every expected heritability value, using the likelihood ratio test as described in the RLRSIM package 2.0-12 [We evaluated the power to detect the heritability of a continuous trait in two parent-offspring cohort pairs for which both panels of microsatellite markers  had been genotyped , so that the pedigree structure was maximised (see Results for optimum marker number); we then compared the power to estimate heritability when using 14 vs. 29 markers. A continuous trait with heritability ranging from 0 to 1 with 0.05 increments was generated using the age 1.04  based onge 0.2-4  after a ge 0.2-4 . Finallye 2.0-12 .R2 = 0.50, F = 10.11, p = 0.013). The mean allelic richness among cohorts was between 10.2 and 11.4, with locus-specific levels ranging from 3.3 to 26.0  was 88.0%, ranging from 75.8% to 94.3% per locus  was estimated to be low , and the error rate due to stochastic genotyping errors (E2) was even lower . The posterior distribution of unsampled parents deviated from the prior estimates Table . The posThe parentage assignments with simulated datasets and different numbers of markers suggested that the optimal marker number for efficient parentage analysis in this system is approximately 20 to 28. Using 20 and 28 markers , a mean of 89.3% and 95.2%, respectively, of all parental links  were identified at a conservative 95% threshold Figure . In contWe tested five different probability thresholds for parentage assignment in the simulations. At all thresholds, the number of correctly and incorrectly assigned individuals was similar when using 20 or more markers. At lower numbers of markers, lower probability thresholds had higher number of correct links resolved, but at the same time, the number of incorrectly assigned individuals also increased Figure b.Ignoring the genotyping error rate and missing genotype ratio while simulating genotypes resulted in overestimating the assignment power, such that 78.1% and 75.8% of individuals were assigned at 14 markers, respectively, while only 67.0% were assigned when these empirical error rates were included in the simulations  and 420 pairs  were resolved at the 95% and 80% thresholds, respectively .Adding Panel 2 markers  increased the overall parentage assignment success Table , such thWithin a cohort, two to three fold differences in reproductive success between two groups (see Methods for details) could be detected in females Figure , while tUsing 29 loci, a continuous trait with a moderate heritability i.e., 40-55%) can be detected with 80% or higher power Figure . Standar0-55% canWe have presented simulation-based approaches aimed at optimising the quantity and quality of pedigree information that can be obtained from a partially sampled wild population. Using simulations parameterised by empirical data from the Burrishoole Atlantic salmon, we have illustrated a robust procedure  if the number of unsampled parents is incorrectly estimated in the model [Knowledge of the numbers of unsampled individuals is critical for efficient parentage assignments in the likelihood-based categorical allocation approaches (e.g. in cervus and MASTSTERBAYES), such tSTERBAYES,59. Highhe model . Althoughe model  . The stAnother factor that should be considered is the potential influence of maternal effects on the accuracy of heritability estimates, particularly given that the Burrishoole pedigree is currently heavily reliant on maternal connections Figures  and 4. TThis study demonstrates the feasibility of pedigree-based research to estimate key population parameters, which in turn can be used to resolve difficult ecological and evolutionary questions in wild populations. The approaches described above provide a robust methodological framework for evaluating historically sampled populations for pedigree-based research and to retrieve valuable quantitative genetic information, even where sampling represents only a proportion of the total census population.The authors declare that they have no competing interests.PMG, CRP, SEJ, TA, TFC, PAP conceived and designed the study. RP, DC, GR, PMG oversaw sample collection and archiving. TA, SEJ, CRP designed the simulations and analysed the data. TA, CRP, PMG, TR wrote the article. All authors commented on and approved the final manuscript.Philip McGinnity and Craig R Primmer shared senior authorship.Primers for each marker used in the study.Click here for fileDescriptive statistics of markers used in the study.Click here for fileError rate estimation procedure.Click here for fileAssumed lifetime reproductive success variation of Burrishoole Atlantic salmon for females (a), and males (b).Click here for fileDifferentiation metrics (CI) between parental and offspring cohorts of microsatellite markers used in the study.Click here for fileAssignment success in simulations (medians and standard deviation).Click here for fileAdditional pedigree statistics and figures.Click here for filePower to detect relative reproductive success among groups of fish based on Burrishoole Atlantic salmon demography and sampling information, additional figures.Click here for fileRanges of heritability estimates for a given heritability value.Click here for file


DRB1 allele to reduce the influence of potential allelic variant effects of the MHC locus. A genome-wide association study (GWAS) based on BovineSNP50 genotypes revealed six genomic regions located on bovine Chromosomes 3, 5, 8, 15 and 27; which were significantly associated (-10 plog=4.3) with Box-Cox transformed mean FEC (BC-MFEC). DAVID analysis of the genes within the significant genomic regions suggested a correlation between our results and annotation for genes involved in inflammatory response to infection. Furthermore, ROH and selection signature analyses provided strong evidence that the genomic regions associated BC-MFEC have not been affected by local autozygosity or recent experimental selection. These findings provide useful information for parasite resistance prediction for young grazing cattle and suggest new candidate gene targets for development of disease-modifying therapies or future studies of host response to GI parasite infection.Genetic markers associated with parasite indicator traits are ideal targets for study of marker assisted selection aimed at controlling infections that reduce herd use of anthelminthics. For this study, we collected gastrointestinal (GI) nematode fecal egg count (FEC) data from post-weaning animals of an Angus resource population challenged to a 26 week natural exposure on pasture. In all, data from 487 animals was collected over a 16 year period between 1992 and 2007, most of which were selected for a specific  Nematode parasites are a primary animal health constraint in ruminant livestock production on pasture . GastroiPrevious studies have suggested that host genetics significantly affects the number of GI nematode eggs per gram (EPG) in the feces of calves, which enables estimation of heritability of resistance . In cattWhile the search continues for candidate genes and traits indicative of nematode resistance in ruminants , many stDRB1 allele to homogenize any potential effects derived from the major histocompatibility complex (MHC) locus, it was important to discern how stratification within this population might influence a genome-wide association study (GWAS). Due to the closed breeding scheme where pedigree coefficients range from 0.1 to 0.35, the genomic autozygosity of this herd should also have increased rapidly. The genomic inbreeding coefficient (F) and autozygosity of specific regions can now be calculated directly with the availability of relatively dense and uniformly spaced SNP markers across the whole genome [Because the founder animals of the experimental Angus herd analyzed in both the aforementioned study  and thise genome . In prine genome . Previoue genome .In contrast, positive selection of animals can also change the genomic autozygosity by increasing frequency of haplotypes carrying advantageous alleles for fitness. Statistical tests including measuring the decay of association over distance for particular haplotypes by the extended haplotype homozygosity (EHH)  or integExperimental measurements of FEC are a costly and time consuming and require animals to undergo parasitic challenge . Thus, tDRB1 of the MHC locus to homogenize any effects on EPG. The specific haplotype of DRB1 selected has been found in all Bos taurus breeds and present in animals with varying FEC levels. Animals used for GWAS included 487 BARC Angus progeny that underwent parasite challenge studies, which is about a 50% increase in animals compared to our previous study [A selection program for parasite resistance was initiated at the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), Beltsville Agricultural Research Center (BARC) with parental stock derived from the Wye Angus herd at a University of Maryland farm . The experiments in this animal study were done under protocol number 05–013 approved by the USDA, ARS, Beltsville Area Animal Use and Care Committee. The pedigree is characterized by 45 half-sib families  that originate from two Wye Angus sires born during the 1960s, which are both paternally related to a bull born in 1944. During development of this experimental herd, new sires were from subsequent contemporary groups with EPG phenotype data and were selected to produce calves of desired EPG phenotypes based on this phenotypic data. Animals produced after 1993 were mated to be homozygous at us study . DNA forus study .Ostertagia ostertagi and Cooperia oncophora. Calves were monitored weekly for a number of parasitological and immunologic parameters along with selected measurements of animal growth (data not shown). FEC was determined in units of EPG of feces once a week for each animal. Animals born from 1992 to 2007 were included and data collection ranged from 4 to 26 weeks in 17 contemporary groups.Calves were maintained with their dams on pastures with extremely low numbers of parasites prior to weaning. When the median age of the contemporary group was 205 days, calves were weaned in dry lots for 14 days prior to being placed on pastures naturally contaminated with the two most common nematode parasites in US cattle, yi on a th to 26th week was found to be 0.155. FEC was adjusted by the general linear model; y = c + s + βx + e, in which y is an individual phenotype (BC-MFEC), c is a contemporary group of an individual, s is sex of an individual, β is the covariate coefficient of age, x is age of animal, and e is the residuals. Birth year of animal (x) and contemporary group (c) effect were included in the statistical model to correct for any contemporary group effects. Using the same model above, the heritability (h2) of BC-MFEC was estimated using a mixed model including contemporary group, sex, animal age, an individual animal relationship, and random error effects. Serum pepsinogen levels were measured as described previously and log-transformed to normalize their values [In order to reduce skewness and kurtosis, Box-Cox transformation was applied to raw FEC data for each animal in the challenge experiment, resulting in 487 records of transformed FEC. Transformed values of mean FEC (BC-MFEC) were obtained by analyzing the response variable n family , in whicr values .p<0.0001) were discarded. Any SNPs with <50% call rate across the population were also excluded. A total of 31,165 SNPs were retained for a single marker-trait association tests. For haplotype analysis, SNP marker genotypes were increased to 39,273 by lowering MAF (<0.02) thresholds in an attempt to better identify regions under the influence of inbreeding and/or positive selection. DAVID analysis was used in an attempt to elucidate common function among the candidate genes residing within genomic regions containing suggestive and significant BC-MFEC SNP associations [A total of 586 Angus produced between 1964 and 2007 were genotyped using the Illumina BovineSNP50  according to the manufacturer’s protocol. Illumina’s Genome Studio was used to determine 45,632 SNP genotypes with an average call rate over 99%. Genome coordinates of each SNP were determined according to UMD 3.1 genome assembly , 32. SNPciations . Annotatciations , InnateDciations , and theciations , 37.e is vector of individual error terms. Then u is a polygenic (random) effect using individual relationships, which was assumed to be distributed u~, where A is the additive individual relationship among animals based on pedigree. R with the kinship package [p = 0.01; 3.0 x 10-5).Association between SNP and BC-MFEC was evaluated with a linear mixed model including the effects of SNP genotype and a polygenic effect. A regression analysis was performed to detect the additive genetic effect of SNP across the genome. The association between haplotype and adjusted data was evaluated using a generalized linear model, y = μ+ βG + u + e, where y is the vector of residuals of BC-MFEC of individuals adjusted for sex, age, and contemporary group, μ is mean, and β is a vector of additive genetic effect. The adjusted value (y) is used as independent variable in the mixed model. G is an indicator variable for the additive genetic effect of an individual, and  package  was used package . The genIn addition to the above GWAS, GenSel  was usedDRB1 haplotype of the MHC locus or any traits corresponding to regions associated with resistance to GI nematodes, we determined signatures of selection using an EHH analysis. Haplotypes were determined using Fastphase [To examine selection for a specific astphase , and eviastphase . This teastphase . Raw iHSastphase  and R [3astphase  were useRuns of homozygosity (ROH) were calculated across the autosomes to investigate the effect of autozygosity relative to the marker-trait association results. ROH was defined by stretches of continuous homozygous SNPs spanning at least 1.5 Mb with 100 or more SNPs, which enables calculation of local autozygosity most likely derived from recent common ancestors. The threshold for ROH was decided based on the expected length of an autozygous ROH segment (0.5 Mb with 50 SNPs) caused by a common ancestor born 30 generations ago . Next, gIn order to ascertain the effect of local autozygosity on BC-MFEC, the associations between ROH and GWAS results for each animal were analyzed using a linear regression . In our p<0.05). Using a mixed model, the narrow-sense heritability of BC-MFEC was estimated at h2 = 0.42, which was higher than estimated h2 of GI nematode burden in previous cattle studies [p<0.0001).Initially, the heritability of GI nematode burden was estimated in 487 Angus animals. The original fecal egg count was zero-skewed data; therefore, the data was Box-Cox transformed (BC) to ensure the validity of normal distribution and related assumptions . The Sha studies , 15. Whip≤0.05) were detected on 19 chromosomes. The majority of these associations (N = 69) were located on BTA 3, 6, 9, and 15 , and these markers were located in six regions on BTA 3, 5, 8, 15, and 27  appeared to be under significant positive selection with |iHS|>3.Although the genomic regions under selection were sometimes located nearby regions associated with parasitic disease resistance , no signature regions entirely overlapped with our GWAS results. Interestingly, two markers associated with mean FEC (62 and 64 Mb) were also found to be under moderate levels of selection (|iHS|>2) on BTA 15 . CollectTo identify potential correlations between autozygosity and resistance to disease, the genomic homozygosity was measured using ROH detected across sampled Angus genomes with a threshold set at 1.5 Mb with 100 or more SNPs. The typical size of a homozygous genomic region ranged between 2–5 Mb, and the longest haplotype homozygosity of an individual extended over 50 Mb encompassing almost half of BTA 7. The average autozygosity of a marker locus defined by ROH was 0.21, varying from 0.02 to 0.73 ; S3 Fig.-log10p>10), resulting in high levels of ROH and a strong selection signature on BTA 23   . In partp<0.001)  appear tTo investigate potential effects of local autozygosity across the genome, we examined the genomic co-localization of BC-MFEC GWAS results, selection signatures (|iHS|), and ROH-derived genomic autozygosity . This plA management program based on DNA tools that help identify resistance and/or susceptibility to GI nematode parasites in cattle has potential to decrease the dependence on anthelmintics, reducing risk of environmental residues and creation of drug-resistant parasites. Availability of a bovine reference genome , genome-Although only a few QTL for GI nematode resistance had been identified in cattle, our study is the first to report genomic locations using a GWAS based on dense genome-wide SNP genotypes. We found 12 genomic regions containing multiple SNP associations, which accounted for about 13% of the variation in FEC in our closed breeding population. We also confirmed the results for our previous microsatellite-based QTL mapping by validating BC-MFEC associations on BTA 8 and 12 . We stilBarH-like homeobox 2 (BARHL2). This gene encodes a transcription factor involved in cell fate commitment and neuron differentiation; however, there is no bovine gene expression data to provide clues as to the tissue expression pattern of BARHL2 [Growth factor independent 1 transcription repressor (GFI1), which is a transcription repressor induced by T cell activation and IL-4/Stat6 signaling that enhances Th2 cell expansion [ABCC4, which is implicated in multi-drug resistance and platelet degranulation [To provide some additional support, we compared our GWAS findings to those found in three recent reports using genome-wide SNP-based methods to detect genomic regions affecting host resistance to GI nematodes in sheep –48. In af BARHL2 . Althougxpansion , is locanulation . Comparinulation , 48 founth week are categorized as susceptible possibly due to an improper immune response to infection [p≤0.05) were detected to distinguish susceptible animals from innate and adaptive resistant animals (data not shown). We considered this an unfortunate result, because selection against susceptible animals may be the most effective disease control strategy for parasite infection. Like sheep [In our challenge studies, truly resistant animals shed very few worms based on raw FEC while animals with a continually increasing FEC up to the 26nfection . Cases wke sheep , the higThere are a number of characteristics for our population, which may have hindered detection of QTL for parasite indicator traits. First, a previous pedigree analysis of our population revealed more than 90% of the animals are paternally inherited from a Wye bull born 70 years ago . This poDRB1 allele in our population may also have diminished our ability to detect marker:FEC associations across the genome. The closed mating system substantially reduced the amount of variation across the entire genome, where we observed that more than 20% of the whole region consists of long haplotypes (>1 Mb) at high frequency (>0.5) based on 50 or 100-SNP windows. As a consequence of increasing genomic homogeneity in our closed herd, reduced polymorphism may have decreased our power to detect associations with parasite-related traits.Furthermore, the selection for a single Haplotype-based association tests have also been suggested  as an alTLR1 and TLR6. These genes belong to a class of genes found to be highly expressed in GI nematode resistant sheep [Due to the above characteristics of our experimental herd, we attempted to exploit signature of selection and genomic autozygosity analyses as alternatives to find potential genomic regions under selection and affecting parasite infection. Positive selection can increase the rate at which deleterious mutations accumulate directly, when the effect of the advantageous mutation outweighs the effects of linked deleterious mutations , or indint sheep . Recent nt sheep . OverallFor the signature of selection analysis, we could only detect moderate or weak positive selection for loci associated with FEC, which suggests the methods used for selective mating between animals with similar values of FEC were not effective. For example, environmental effect explained ~60% of total variation of FEC, but the selection of animals with high or low FEC was not dependent upon a fully adjusted genetic value, which likely decreased the accuracy of selection. The standardized value of iHS is optimized for detecting evidence of allelic selection with frequency 0.7–0.9 , but thiTNFα, IL-1β, and MIP-1α in fundic and pyloric abomasa 7 days post infection [In sheep, comparison of gene expression differences between genetically resistant or susceptible lines has also been used as a method to identify genes that contribute to an effective immune response against GI nematodes . Even winfection . These fnfection . Distincnfection , some ofnfection .Availability of bovine genome-wide SNP genotyping platforms has been and will continue to be a useful tool for developing selection programs in cattle . HoweverS1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 File(ZIP)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Common forms of atherosclerosis involve multiple genetic and environmental factors. While human genome-wide association studies have identified numerous loci contributing to coronary artery disease and its risk factors, these studies are unable to control environmental factors or examine detailed molecular traits in relevant tissues. We now report a study of natural variations contributing to atherosclerosis and related traits in over 100 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP). The mice were made hyperlipidemic by transgenic expression of human apolipoprotein E-Leiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). The mice were examined for lesion size and morphology as well as plasma lipid, insulin and glucose levels, and blood cell profiles. A subset of mice was studied for plasma levels of metabolites and cytokines. We also measured global transcript levels in aorta and liver. Finally, the uptake of acetylated LDL by macrophages from HMDP mice was quantitatively examined. Loci contributing to the traits were mapped using association analysis, and relationships among traits were examined using correlation and statistical modeling. A number of conclusions emerged. First, relationships among atherosclerosis and the risk factors in mice resemble those found in humans. Second, a number of trait-loci were identified, including some overlapping with previous human and mouse studies. Third, gene expression data enabled enrichment analysis of pathways contributing to atherosclerosis and prioritization of candidate genes at associated loci in both mice and humans. Fourth, the data provided a number of mechanistic inferences; for example, we detected no association between macrophage uptake of acetylated LDL and atherosclerosis. Fifth, broad sense heritability for atherosclerosis was much larger than narrow sense heritability, indicating an important role for gene-by-gene interactions. Sixth, stepwise linear regression showed that the combined variations in plasma metabolites, including LDL/VLDL-cholesterol, trimethylamine N-oxide (TMAO), arginine, glucose and insulin, account for approximately 30 to 40% of the variation in atherosclerotic lesion area. Overall, our data provide a rich resource for studies of complex interactions underlying atherosclerosis. While recent genetic association studies in human populations have succeeded in identifying genetic loci that contribute to coronary artery disease (CAD) and related phenotypes, these loci explain only a small fraction of the genetic variation in CAD and associated traits. Here, we present a complementary approach using association analysis of atherosclerotic traits among inbred strains of mice. A strength of this approach is that it enables in-depth phenotypic characterization including gene expression and metabolic profiling across a variety of tissues, and integration of these molecular phenotypes with coronary artery disease itself. A striking finding was the large fraction of atherosclerosis that was explained by genetic interactions. Association analysis allowed us to identify genetic loci for atherosclerotic lesion area as well as transcript, cytokine and metabolite levels, and relationships among the traits were examined by correlation and network modeling. The plasma metabolites associated with atherosclerosis in mice, namely, LDL/VLDL-cholesterol, TMAO, arginine, glucose and insulin, overlapped with those observed in humans and accounted for approximately 30 to 40% of the observed variation in atherosclerotic lesion area. In summary, our data provide a detailed overview of the genetic architecture of atherosclerosis in mice and a rich resource for studies of the complex genetic and metabolic interactions that underlie the disease. Inheritance plays an important role in the pathogenesis of coronary artery disease (CAD), the leading cause of death in the developed world –4. RecenA complementary approach to studying common forms of CAD is to use naturally occurring variations in experimental organisms such as rats or mice. Important advantages include the ability to control the environment and to monitor both clinical and molecular phenotypes in detail. Over the past 20 years, quantitative trait locus (QTL) analysis has identified hundreds of loci for common disease traits. Unfortunately, this has led to the identification of relatively few genes and novel pathways, primarily because of the low resolution of linkage analysis . HoweverWe now report association analysis of atherosclerosis and related traits in a population of over 100 common inbred strains, termed the Hybrid Mouse Diversity Panel (HMDP). The panel allows mapping of complex traits by association analysis, providing resolution at least an order of magnitude better than that achieved by traditional linkage analysis . The appApoe-/-) [Ldlr-/-) mice [Fig 1A). For this purpose, we employed transgenes for human APOE-Leiden and CETP, both of which had been previously used to promote atherosclerosis development in mice in a dominant manner [Fig 1B). We also examined various diets and chose a diet containing 1.0% cholesterol since it significantly enhanced lesion development in females although no significant effect was observed in the smaller lesions observed in male mice .One difficulty in applying an association strategy to atherosclerosis in a panel of mice is that a sensitizing mutation resulting in hyperlipidemia is required for the development of substantial lesions. The most widely used models are apolipoprotein E null (poe-/-) , 19 and /-) mice . Both oft manner , 22. We Fig 1A. We generated mice heterozygous for the APOE-Leiden transgene and homozygous for the CETP transgene on a C57BL/6J background and bred these to each of about 100 HMDP mouse strains with selection of F1 mice expressing both transgenes. Ideally, donor mice homozygous for both transgenes would have been used in these studies, but we found that homozygous APOE-Leiden mice were poor breeders. The F1 mice (which we term “Ath-HMDP”) were maintained on a chow diet until 8 weeks of age and then challenged with a “Western style” high-fat cholesterol containing diet for an additional 16 weeks, at which time they were euthanized and trait phenotypes examined. In addition to atherosclerosis, we examined a variety of metabolic traits that have been associated with atherosclerosis in human populations, including plasma lipid levels, insulin/glucose levels, blood cell levels, obesity and TMAO levels [Our overall experimental design is summarized in O levels . We thenS1 Fig). In fact, for individual mice at 24 weeks, HDL-cholesterol levels ranged from undetectable levels to 220 mg/dl, VLDL/LDL-cholesterol levels ranged from 10 to 2,800 mg/dl, and triglyceride levels ranged from 10 to 2,700 mg/dl. As expected, the diet greatly elevated the levels of total- and LDL/VLDL-cholesterol in all the strains, but there remained a strong correlation between values on the chow and Western diets . The correlations of each class of lipids with atherosclerotic lesion area in females are shown in S3 Fig. Atherosclerotic lesion size was negatively associated with HDL-cholesterol levels  and positively associated with LDL/VLDL-cholesterol levels (p = 0.004) . Triglyceride levels were not significantly associated with lesion area (p = 0.41) . The strengths of associations in male mice are shown in S1 Table.We measured levels of plasma lipids in the Ath-HMDP mice on a chow diet at 8 weeks of age as well as after feeding the mice the high fat high cholesterol diet from 8 weeks to 24 weeks of age. Strain-average plasma levels of total cholesterol, LDL/VLDL-cholesterol, HDL-cholesterol and total triglyceride levels varied considerably among the strains  and a measure of insulin resistance (HOMA-IR)[Panel E in S3 Fig and S1 Table). HOMA-IR, a measure of insulin resistance calculated from glucose and insulin values, ranged between 1.2 and 54. Correlation of HOMA-IR with atherosclerotic lesion size was modest (p = 0.06) in females but not significant in males (p = 0.44) .The levels of plasma glucose, insulin (presented in (HOMA-IR), 24, wer. There was no significant relationship between adiposity and the extent of atherosclerosis among the male mice  but a significant inverse relationship among female mice  (S1 Table). This inverse relationship is consistent with studies in genetically obese (ob/ob and db/db) mice which have reduced atherosclerosis [The levels of body fat, examined using nuclear magnetic resonance (NMR) spectroscopy, ranged from <10% to nearly 50% adiposity (clerosis . This maclerosis .2 SNP correlations greater than 0.8) and exceeding the FDR cutoff of 5%, corresponding to the association p-value of 1.3×10−5 [. A list of all significant loci (FDR < 5%) is provided in S3 Table, but we highlight several of the associations for plasma lipids identified in females. A significant association was identified for VLDL/DL cholesterol on Chr 18 at 21 Mb , HDL cholesterol on Chr 5 at ~120 Mb , and triglycerides on Chr 1 at ~135 Mb  were those in strong linkage disequilibrium with the peak SNP  strains to less than 2,500 μm2 for resistant strains . Strain-average lesion areas in males tended to be much lower but also varied widely, from about 170,000 μm2 to negligible . Strain C57BL/6J mice, with lesion sizes of about 150,000 μm2/section for females and 19,000 μm2/section for males, were approximately intermediate in the panel of strains. The sizes of lesions correlated significantly between males and females   but, clearly, the ratio of lesion sizes in the sexes varied widely, indicating the existence of gene-by-sex interactions. The range of lesion-areas is consistent with previous results from a small number of strains on either Apoe-/- or Ldlr-/- backgrounds as well as genetic crosses [ crosses , 31. We −5 [Fig 5A and 5B). The locus on Chr 9, encompassing about 1 Mb between 46 and 47 Mb of the chromosome, was observed in both sexes  and corresponds to a locus previously identified in genetic crosses between strains C57BL/6J and C3H/HeJ [S5 Fig. The two loci on Chr 2 are clearly distinct because conditioning on one does not affect the association for the other . Due to differences in breeding, the number of F1 mice differed considerably between strains (S2 Table). When we performed association analysis using only strains with 3 or more mice, the significant regions exhibited similar p-values but a suggestive peak on Chr 10 became significant .We next sought to determine the loci contributing to lesion development. Using an FDR cutoff of 5%, corresponding to the association p-value of 1.3×10−5 , we were C3H/HeJ . High-re. The percentage of white cells that were monocytes was significantly and positively correlated with atherosclerosis  (S4 Table).Recent studies have highlighted the relationship between blood cells and cardiovascular disease–35. We qS3 Table), and identified multiple loci affecting these traits. We detected an association for the percentage of white blood cells that were monocytes on Chr 8 at 36 Mb and total monocyte counts were regulated by loci on Chr 9 and 13 (S3 Table).We mapped major loci contributing to the blood cell levels  . KC levels mapped to a locus on Chr 1  that has been previously shown to be associated with multiple cardiovascular risk factors [Cytokines are clearly important in atherosclerosis as indicated by transgenic studies in mice as well as human GWAS studies . Althoug5 cells/well, washed, and the fluorescence level quantitated using a plate reader. In our initial studies, we observed reproducible differences in the uptake of AcLDL labeled with the fluorescent dye DiI . Moreover, we observed that uptake was relatively linear over a 24-hour period . Representative cultures are shown in Fig 7A. The uptake varied greatly among the strains over an approximately 5-fold range , and we identified two significant loci controlling the loading of macrophages by AcLDL, on Chr 6 at 149 Mb, rs30709278 (p = 3.82 x10-7) and on Chr 7 at 13 Mb, rs37775929 (p = 8.22 x10-7) . These loci do not contain the candidate genes Cd36 or Msr1, previously implicated in cholesterol loading of macrophages, nor did we observe significant correlation between the mRNA levels of Cd36 and Msr1 with AcLDL loading [Fig 7D), suggesting that AcLDL is not a good surrogate for the modified LDL found in lesions, or that cholesterol loading is not a limiting process in lesion development.The uptake of modified LDL and VLDL particles by macrophages to produce cholesterol-loaded foam cells is a key aspect of atherosclerosis. To examine whether genetic differences in macrophage loading might contribute to lesion development, we quantitated the uptake of acetylated LDL (AcLDL) by peritoneal macrophages from the HMDP mice. Peritoneal macrophages appear to be a suitable surrogate since they show expression of surface markers typically found in atherosclerotic lesion macrophages such as SR-A, CD36, CD68, F4/80, MOMA-2, Ly6C, and CD11b . Macroph loading . MoreoveFig 8A–8J. Three histological sections from each of 5 female mice were quantitated for fraction of lesion area that shows staining for SM-α actin or CD68 and the results are plotted in Fig 8K and 8L, respectively. There were significant differences in CD68 staining, presumably indicating alterations in inflammatory pathways. We did not detect significant differences in the percentage of lesions staining positive for alpha actin staining. Genetic contributions to lesion morphology have been noted in some previous studies in mice [To determine whether genetic factors contributed to lesion morphology as well as lesion size, we examined lesions with antibodies specific for macrophages (CD68) or smooth muscle cells (SM-α actin). We chose to examine the five progenitors of the four recombinant inbred strain-sets that comprise the HMDP  since they provide much of the power for the association analyses. Representative sections are shown in  in mice –44.Fig 9. Most notably, the recently discovered risk factor for atherosclerosis, TMAO [Fig 9C). On the other hand, the levels of choline, a metabolic precursor of TMAO [Fig 9A).We quantitated the levels of 31 polar metabolites in plasma of a subset of female strains. Of these, butyryl-carnitine, choline, TMAO, arginine, citrulline and ornithine exhibited significant correlations with atherosclerosis, and their strain average levels and correlations with atherosclerosis are presented in is, TMAO , was pos of TMAO , were neFig 9F), is a breakdown product of arginine via the enzyme arginase I. Levels of arginine were positively correlated with lesion area   as were levels of citrulline  . As discussed below, we quantitated global gene expression in livers of female mice from 96 strains in the Ath-HMDP using Affymetrix HT-MG 430 PM microarrays. Several genes known to regulate these metabolites were highly associated with plasma concentrations. For example, hepatic expression of Arg1, encoding arginase 1, was highly negatively correlated with ornithine  and positively correlated with arginine levels . This suggests either that the ornithine and arginine in plasma are not of hepatic origin or that there is feedback regulation of Arg1. It is noteworthy that the levels of arginase I in macrophages are a marker of M2 (anti-inflammatory) as compared to M1 (pro-inflammatory) macrophages [The inter-related metabolites arginine, ornithine and citrulline are involved in the formation of nitric oxide (NO), an important regulator of vascular tone, blood flow and pressure –48. Ornirophages , 50.−6) and on Chr 16 at 94 Mb (p = 1×10−6) (S3 Table). Arginine was associated with a major locus on Chr 7 at 44Mb, rs32257964 (p = 2×10−8) but also exhibited complex regulation with additional loci on Chrs 1, 4, 11 and 13 (S3 Table). There are 13 hepatic genes with eQTL that are in strong LD with the associated SNP at the Chr 7 locus. Of these, Nosip is an interesting candidate as it has a strong cis-eQTL (p = 8.7×10−22) and overexpression of Nosip has previously been shown to reduce NO synthesis in-vitro [We performed association analyses to identify loci regulating circulating metabolites associated with atherosclerosis. Notably we identified loci for TMAO levels on Chr 10 at 98 Mb, .S6 Table lists genes whose expression is most strongly positively or negatively correlated with atherosclerotic lesion size. These genes were analyzed using DAVID to test for enrichment of Gene Ontology (GO) categories. Overall there were broad differences in the function of genes correlated with atherosclerosis in the liver and the aorta. The genes most highly correlated with atherosclerosis were enriched for immune response genes in the liver and for defects in smooth muscle cell function and structure in the aorta (Table 1).−5) contained 155 genes (. This module was significantly enriched for the Gene Ontology “acyl-CoA metabolic process” category (enrichment p = 2.6×10−6). Among the genes in the module, Wdr73 was a hub, exhibiting the strongest connections with all the other genes (. Wdr73 has been implicated in a human GWAS of periodontitis [−6) contained 56 genes ( and was highly enriched for the Gene Ontology “defense response to virus” category (enrichment p = 5.2×10−23). The hub gene of the module was Mx1 which encodes a guanosine triphosphate (GTP)-metabolizing protein that is induced by type I and type II interferons , Ifit2 and Ifit3 (interferon-induced protein with tetratricopeptide repeats 2 and 3).We also analyzed the expression data using the weighted gene co-expression network analysis (WGCNA)  to modeldontitis  and in Gdontitis . However−4 and 1.5×10−6, respectively, corresponding to 1% FDR. In liver, we identified a total of 3,599 local eQTLs and 3,912 distant eQTLs at p-values of 9.0×10−4 and 1.6×10−6, respectively, corresponding to 1% FDR. Plots in which the location of the significantly associated SNP is graphed against the location of the regulated gene are presented in  and .To model causal interactions, help prioritize candidate genes at GWAS loci and examine gene-by-gene interactions, we mapped loci controlling gene expression traits (eQTL) in aorta and liver. We utilized the FaST-LMM algorithm to perform association while correcting for population structure . ApproxiS7 Table). For example, in the Chr 9 locus, examination of hepatic eQTL identified 4 candidates; Nnmt (p = 1.27×10−8), 2310030G06Rik (p = 5.42×10−7), Alg9 (p = 1.05×10−6), and 1110032A03Rik (p = 2.27×10−6), whose eQTL are regulated by a SNP in high LD (r2>0.5) with the associated SNP for atherosclerosis. The less stringent cutoff for r2 was chosen to ensure that that no strong eQTL contributing to the atherosclerosis phenotype would be missed, based on the rationale that the causal gene might reside immediately outside the LD block but be regulated by an enhancer within the block. The expression of all four of these genes had significant correlations with lesion size . Three of the genes also exhibited significant eQTL in aorta; Nnmt (p = 1.01×10−11), Bco2 (p = 1.61×10−4), 1110032A03Rik (p = 2.18×10−4), and their expression levels were correlated with atherosclerosis  . In both tissues, Nnmt had the most significant local eQTL . In the Chr 5 locus, there are 4 hepatic genes with eQTL that are in strong LD with the atherosclerosis associated SNP at the locus: Nub1 (p = 1.65×10−16), Nos3, (p = 3.08×10−6), Cdk5 (p = 2.48×10−5), and Abcb8 (p = 7.46×10−4) . Of these genes, only Nub1 replicates in the aorta (p = 1.37×10−15). While the distal Chr 2 locus was associated with the expression level of two genes in liver, Dtwd1 (p = 1.43×10−13) and Bub1 (p = 4.62×10−4), their abundances were not correlated with lesion size . None of the loci appear to exhibit interactions with any risk factors examined in this study, including plasma lipids, metabolites, or the cytokines.To prioritize candidate genes in the atherosclerosis loci discussed above, we examined each of the annotated genes for local eQTL. Three of the 4 loci were associated with the expression levels of local genes (2Mb on either side of the locus) in aorta and liver , suggesting that non-additive factors such as gene-by-gene interactions are important. Simple inspection of the F1 atherosclerosis data supports this conclusion. Since all the mice are F1 heterozygotes with C57BL/6J  as one parent, an additive model would be inconsistent with lesion areas less than about 75,000 μm2 for females and 9,000 μm2 for males. Yet, some strains have lesions areas less than a few thousand μm2. Similar evidence of non-additive inheritance was also observed for plasma lipids and a number of other traits studied.  For certain traits, the narrow sense heritability exceed the broad sense heritability (Table 2). This undoubtedly reflects errors in the estimates. In particular, certain traits were examined in only a subset of the mice, compromising estimates of broad sense heritability that are based on reproducibility of measurements within a strain. Overall, these heritability estimates are somewhat lower than observed in some previous HMDP studies [The resulting heritabilities are shown in S8 Table). For example, the GUCY1B3 gene locus has been associated with atherosclerosis and its role has been validated by studies of rare variants in human families [Ath-HMDP, Gucy1b3 has a strong cis-eQTL in aorta (p = 9.7x10-6) as well as other tissues and its expression in aorta is negatively associated with atherosclerosis , consistent with human studies. In some cases, our data suggest that a novel gene at a GWAS locus might be responsible. For example, human SNP rs2075650 in Chr 19 has been associated with increased CAD risk and is near the APOE/APOC1 genes whose roles in atherosclerosis are well established. However, our data show a very significant correlation between the aortic expression of another gene located at the same locus, Pvrl2, and lesion size  . There is also a weaker but significant correlation in the liver in the opposite direction  (S6 Table). This gene encodes a protein that is part of the adherens junctions [Cct7 and atherosclerosis  .Human GWAS studies employing tens of thousands of individuals are highly powered to identify loci for complex traits, including CAD and its risk factors. To date, about 150 genome-wide significant and suggestive loci have been identified for CAD in approximately 200,000 individuals . Most offamilies –62. In tunctions , and PVRunctions  and lesiunctions , 61. OurTable 3). These metabolites explain a very significant fraction of disease variance, 31.8% in females and 38.9% in males. One striking difference between the sexes is the lack of impact of TMAO in males. This is likely explained by the fact that male mice show greatly reduced TMAO levels due to repression by testosterone of FMO3, the enzyme that metabolizes TMA to TMAO [Our systems genetics approach, in which a variety of plasma metabolites were measured across a population varying for atherosclerosis while holding environmental factors relatively constant, enables the identification of diagnostic markers based on genetic variation. Using a series of plasma metabolites that exhibited at least suggestive correlation with atherosclerosis, we carried out stepwise linear regression  to estim to TMAO . The exp to TMAO . It is a to TMAO .Over the last 35 years several groups, including ours, have investigated common genetic variations affecting atherosclerosis in mice. In the current study we have examined susceptibility to atherosclerosis and a number of related traits on a hyperlipidemic background in >1,800 mice across 100 strains. The results provide a broad view of the physiological and molecular interactions underlying atherosclerosis in this model organism. A number of conclusions have emerged, as discussed below, and the data reported here should serve as a resource for future gene discovery and mechanistic studies. Toward this end, all of our data can be accessed at our website (systemsgenetics.ucla.edu) or from the authors.Using association rather than linkage analysis, we have mapped loci for a number of clinically relevant traits with excellent resolution. Of particular interest are traits such as TMAO levels which have proven difficult to address using human GWAS studies, presumably due to the major impact of environmental factors . AlthougUsing our systems genetics approach we identify several important characteristics of atherosclerosis. One is its sheer complexity. We carefully phenotyped the Ath-HMDP for a variety of traits and prioritized these based on their correlation to atherosclerosis. These included plasma lipids, metabolites, and cytokines and cellular uptake of modified lipids by thioglycollate-elicited macrophages. Each of these phenotypes are themselves complex traits and we identify significant loci for each of them. Our results are consistent with an infinitesimal model of common diseases, in which genetic variations in hundreds or thousands of genes determine genetic susceptibility to disease.There were several surprising findings in this study. The first is the importance of non-additive genetic variance in atherosclerosis and some other traits. This was clearly reflected in the F1 data and in the comparison of broad sense as compared to narrow sense heritability. This indicates the existence of important non-additive interactions, such as gene-by-gene and gene-by-environment interactions, which have been difficult to pursue in human studies , 72. OurNOS), a strong candidate at the Chr 5 locus, and plasma levels of arginine, the substrate for NO production, are correlated with atherosclerosis and are regulated by a locus on Chr 7 that contains the gene Nosip1. Further understanding how NO metabolism is regulated may identify novel targets for atherosclerosis treatment. A number of human epidemiologic studies have revealed associations between arginine and asymmetrical dimethyl arginine, an endogenous NO inhibitor, and cardiovascular events, including myocardial infarction and stroke [Consistent pathways were identified in our gene set enrichment analyses, a primary one being the NO pathway. The endothelial nitric oxide synthase gene  would be a more suitable ligand for these studies, we used AcLDL for our experiments because there are large variations in uptake between batches of oxLDL as well as variations in the same oxLDL batch over time. A previous study of AcLDL loading of macrophages from two strains differing markedly in atherosclerosis susceptibility (DBA/2J and AKR/J) observed some striking differences in transcriptional responses associated with lysosome and ER stress pathways [ormation . The stuormation , 83 but ormation . Furtherpathways .Cxcr2 and encoded by the gene Cxcl1, located on Chr 5. The homolog of KC in humans is IL-8, a critical cytokine that is elevated in CAD patients [.Inflammation is a hallmark of atherosclerosis  and cytopatients  and alsopatients . Mechanipatients , probablpatients , 92.Apoa2 gene has been identified as the causal variant underlying the lipid variations at this locus [ApoA2 is directly responsible for all of these phenotypes. We note several positional candidates for this locus including Slamf7 and Slamf6  and Ifi203 and Ifi204 (interferon activated genes 203 and 204). Of these, only Ifi203 has an eQTL (3.2×10−24) regulated by a SNP in high linkage disequilibrium (r2>0.5) with the associated SNP for KC. It also has expression levels significantly correlated with KC levels (p = 0.003).GWAS analysis of KC/IL-8 levels in the Ath-HMDP mice identified a locus on Chr 1 with a large effect within a locus previously identified for a variety of cardiovascular risk factors including atherosclerosis, body weight, and plasma levels of HDL, glucose and triglycerides. The is locus  but it iWe also analyzed blood cell levels in the Ath-HMDP mice and observed that the number of monocytes as percent of leukocytes was significantly associated with atherosclerosis. This is consistent with human population studies showing that increased monocyte numbers are associated with disease . We alsoPvrl2, a positional candidate from human GWAS studies, is highly correlated with atherosclerosis. Another example is the strong association of expression levels of Cdkn2b with atherosclerosis (S8 Table). This gene resides in the human chromosome 9p21 region that is the most strongly associated with atherosclerosis and our data suggest that it may have a causal role [Finally, our data constitute a resource for elucidating pathways contributing to atherosclerosis. For example, they should be useful in prioritizing candidate genes at human GWAS loci for atherosclerosis and related traits. More than a third of the genes present on the arrays used here exhibited significant genetic variation in expression in aorta and liver. For example we show that the vascular expression of sal role . The reshttp://systems.genetics.ucla.edu/data.In conclusion, our study provides a comprehensive systems genetic analysis of traits relevant to atherosclerosis in a population of common inbred strains of mice. The results are generally consistent with human epidemiologic studies, as many of the factors associated with atherosclerosis in human populations were replicated in mice. They also identify a number of novel factors and candidate gens that can now be experimentally examined. These data as well as other HMDP studies, including gene-gene and gene-trait correlations and clinical trait and clinical trait and transcript mapping, can be accessed in a user-friendly web-based interface at http://systems.genetics.ucla.edu/dataResults can be accessed at http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE66570.All microarray data from this study are deposited in the NCBI GEO (see S9 Table). After 16 weeks on this diet, animals were euthanized for the collection of tissue. Animals were maintained on a 12hr light-dark cycle, 6AM-6PM with ad libitum access to water and chow or experimental diet. Euthanasia of all mice was carried out using deep anesthesia with isoflurane vapor followed by cervical dislocation, a procedure consistent with recommendations of the AVA.Mice carrying the human transgene for cholesteryl ester transfer protein (CETP) on a C57BL/6 background were obtained from The Jackson Laboratory (Stock Number:003904). Mice carrying the human ApoE3 Leiden variant were kindly provided by Dr. L. Havekes . For purAll animal work was conducted according to relevant national and international guidelines and was approved by the UCLA Animal Research Committee, the UCLA IACUC.Plasma was collected from the retro orbital plexus under isoflurane vapor anesthesia immediately before starting the experimental diet at about 8 weeks of age and again at the time of euthanasia at approximately 24 weeks of age. In both cases, the animals were fasted for 4h beginning at 6AM. Blood was collected using heparinized glass capillary tubes into plasma collection tubes with EDTA (Becton Dickerson). Blood was kept on ice until centrifuged and the separated plasmas were frozen at -80°C in aliquots for subsequent analysis.Plasma lipid profiles were measured by colorimetric analysis as previously described , 99. Quard section through the proximal aorta. Lesion size was not normally distributed and was transformed using the Yeo-Johnson transformation [Lesion area in the proximal aorta was carried out as previously described , 100. Brormation .For immunohistochemical staining, primary antibody for α-smooth muscle actin (α-SMA)  or CD68  was applied on tissue sections from frozen, OCT-embedded proximal aortas. Frozen tissue sections were fixed in acetone (15min at 4°C), washed, blocked with 5% normal goat serum in 3% BSA (3h at room temperature), washed with PBS and incubated with the primary antibody for 1 h at room temperature and then overnight at 4°C. After washing with PBS, secondary antibody (α-SMA: Goat anti rabbit (Vector cat # BA-1000) or CD68: Goat anti rat (Vector cat # BA-9401)) was added for 1h at room temperature. With intervening washes, slides were then treated with ABC solution , Vector red substrate , Hematoxylin , Bluing reagent  and 0.01% fast green  and then dried and mounted with glycerol gelatin solution from Sigma (Cat# GG1). Slides stained for CD-68 macrophages, or for α-smooth muscle actin were analyzed for percent of lesion area with positive immuno-reactivity based on visual scoring of a superimposed grid.We measured the following metabolites using MS/MS TOF as described : acetyl-Stable isotope dilution LC/MS/MS was used for quantification of plasma analytes. Four volumes of methanol containing isotope-labeled internal standards were added to 1 volume of plasma to precipitate protein. The supernatant after centrifugation was analyzed by injection onto a silica column interfaced with an API 4000 Q-TRAP mass spectrometer  . A discoBody composition was measured a day or two before euthanasia by NMR using the Brüker minispec  and software from Echo MRI  .Whole aorta from the arch to the mid-abdomen was cleaned of peri-adventitial adipose and snap-frozen at the time of euthanasia, and total RNA isolated using the Qiagen  RNeasy kit, as described . Genome -16 for each strain) similar to a recently published comparison of microarray and RNA-seq data [The liver was carefully dissected and a 50-μg aliquot from the left lobe was immediately frozen at the time of euthanasia and total RNA isolated using the Qiagen  RNeasy kit cat# 74104), as described [, as descseq data .5 cells per well in DMEM with 20% FBS at 37°C and 5% CO2. After overnight culture, cell media was replaced with 1% FBS DMEM media for controls or with media plus 10μg/mL DiI-acetylated LDL , or media plus 10μg/mL DiI-acetylated LDL and 200μg/mL unlabeled acetylated LDL . Four hours later, these media were removed and wells were washed 3 times with PBS and measured for DiI fluorescence .Primary macrophages were harvested from four mice per strain, by intraperitoneal lavage four days following intraperitoneal injection of 1.5ml 4%Thioglycollate . All mice were injected with the same batch of thioglycollate. Cells from each strain were pooled, and plated in replicate wells (n≥ 4) of 96-well black plates  at a cell density of 3×10http://www.genetics.ucla.edu/labs/horvath/Co-expressionNetwork/ and this method has been extensively used to create co-expression networks [th and 6th power for aorta and liver, respectively. The power was selected using the scale-free topology criterion, which is determined by the function “pickSoftThreshold” in the WGCNA package [Network analysis was performed using the WGCNA R package . An extenetworks , 106–110 package , 111. Ne package . The par package .We performed a Gene Ontology (GO) enrichment analysis for network modules using the Database for Annotation, Visualization and Integrated Discovery (DAVID) using the functional annotation clustering option . FunctioThe Mouse Diversity Genotyping Array  was usedAssociations were performed using FaST-LMM , a lineaThe biweight midcorrelation statistic is analogous to the Pearson correlation coefficient, but has the advantage of being robust to outliers. We used the bicor function implemented in the WGCNA R package  to calculocal or cis if the peak association was within a 4Mb interval, flanking 2Mb on either side of the genomic start site of the gene. eQTL were defined as distant or trans by selecting the peak association per chromosome per gene, excluding loci that mapped in cis.eQTL were defined as qvalue package in R. For each gene, we selected all association p-values in the 4 Mb interval, and calculated q-values using all the p-values for all genes. We estimated the FDR separately for each treatment and selected FDR<5%. We estimated the FDR separately for each tissue and selected FDR<5% as follows: aorta eQTL, the median 1% FDR cutoff was 8.4×10−4, and 5% FDR cutoff was 6.4×10−3. The corresponding results for the liver data were 1% FDR = 9.4×10−4 and 5% FDR = 6.7×10−3.We calculate false discovery rates using the q-values for over 2 billion p-values, we computed the FDRs by taking the median FDR for 100 samples, each containing 100 million randomly selected p-values from the original calculated association p-values [−6, and 5% FDR cutoff was 1.3×10−5. The corresponding results for the liver data were 1% FDR = 1.6×10−6 and p ≤ 5% FDR = 1.30×10−5. We set 1.3×10−5 as our threshold of significance for all molecular and clinical phenotypes.Due to the computational complexity associated with evaluating p-values . We estiWe used forward stepwise regression  to identS1 FigIn each panel, strains are arranged in rank order by strain-average lipoprotein levels ± SEM after 16 weeks on high-fat diet. A) VLDL + LDL cholesterol (mg/dl) in females. B) VLDL + LDL cholesterol (mg/dl) in males. C) HDL cholesterol (mg/dl) in females. D) HDL cholesterol (mg/dl) in males. E) Total cholesterol (mg/dl) in females. F) Total cholesterol (mg/dl) in males. G) Triglycerides (mg/dl) in females. H) Triglycerides (mg/dl) in males. (PDF)Click here for additional data file.S2 FigIn each panel, strain average plasma lipoprotein levels (mg/dl) for females are plotted for mice on a chow diet at 8 weeks of age  or after an additional 16 weeks on high-fat diet. A) VLDL + LDL cholesterol, B) Total Cholesterol, C) HDL Cholesterol, D Triglycerides.(PDF)Click here for additional data file.S3 FigA) plasma LDL/VLDL-cholesterol (mg/dl), (B) plasma HDL-cholesterol (mg/dl), (C) plasma triglycerides (mg/dl), (D) plasma insulin (pg/ml), (E) plasma glucose (mg/dl) and (F) HOMA-IR. Individual points indicate strain-averages for atherosclerotic lesion area (μm2/section) and clinical traits.Correlation of atherosclerosis with levels of ((PDF)Click here for additional data file.S4 FigIn each panel, strains are arranged in rank order by strain-average glucose or insulin levels ± SEM after 16 weeks on high-fat diet. A) Glucose (mg/dl) in females. B) Glucose (mg/dl) in males. C) Insulin (pg/ml) in females. D) Insulin (pg/ml) in males. (PDF)Click here for additional data file.S5 FigA) Chromosome 2 at 93.3 Mb, (B) chromosome 2 at 126.6 Mb and (C) Chromosome 5 at 24.6 Mb. Significance for association of SNPs with the atherosclerosis phenotype is indicated by–log p-value. Purple diamond indicates position of peak SNP. Other SNP positions are marked as circles with color indicating R2 as shown in color scale. Positions of nearby genes are mapped below. LocusZoom plots  of ather(PDF)Click here for additional data file.S6 FigA) Top plot shows dual peaks in absence of conditioning. Middle plot shows association conditioned on peak SNP (rs27381267) at 93.3 Mb while bottom plot shows association conditioned on peak SNP (rs32754652) at 126.6 Mb. In each case, conditioning suppresses association for the local peak but not at the second peak, suggesting that the two association peaks are independent. (B) For QTL on chromosome 5, top plot shows broad peaks in absence of conditioning. Bottom plot shows association conditioned on peak SNP (rs32008039) at 24.6 Mb. In this case, conditioning suppresses association across the full region consistent with a single associated locus. Similarly, for QTL on chromosome 9, top plot shows broad peaks in absence of conditioning for both males (C) and females (D). Bottom plot shows association conditioned on peak SNP (rs33738357) at 49.6 Mb. Again, conditioning suppresses association across the full region consistent with a single associated locus. To determine if the two QTL peaks on chromosome 2 are independent, association analysis was repeated but conditioning on the peak SNP in each peak. ((PDF)Click here for additional data file.S7 FigGenome wide association plot for atherosclerotic lesion-area restricted to data from strains for which 3 or more females were available. In addition to the previously observed three peaks on chromosomes 2, 5 and 9, there is an additional locus on chromosome 10 that reaches significance.(PDF)Click here for additional data file.S8 FigA) GM-CSF, (B) IL-10, (C) MCP-1 (D) MIG, (E) MIP-1α and (F) MIP-1β. In each panel, strains are arranged in rank order by strain-average metabolite levels (pg/ml) in females +/- SEM after 16 weeks on Western Diet. ((PDF)Click here for additional data file.S9 Fig(A) Measurements on different dates for strains AXB13, BXD33, BXD40 and SM. Open circles indicate replicate mice measured on the same day. The closed circles for BXD40 indicate results for replicate mice measured on different days. (B) Relative fluorescence after varying concentrations of DiI–Ac-LDL after 4h or DiI-AcLDL plus 200 ug/ml unlabeled-AcLDL (C) Relative fluorescence after varying concentrations of DiI–Ac-LDL after 24h or DiI-AcLDL plus 200 ug/ml unlabeled-AcLDL. (PDF)Click here for additional data file.S10 Fig(A) Butyryl-carnitine, (B) Choline, (C) Trimethylamine N-oxide (TMAO). (D) Arginine, (E) Citrulline, (F) Ornithine. In each panel, strains are arranged in rank order by strain-average metabolite levels (uM) in females ± SEM after 16 weeks on Western Diet. (PDF)Click here for additional data file.S11 FigA) Transcript levels in liver of HMDP mice were profiled and significant associations are plotted according to chromosomal position (x-axis) versus the location of the structural gene (y-axis). The strong diagonal line represents cis-eQTL, whereas the remainder are trans-eQTL signals. (B) Genome-wide association results in the HMDP demonstrating a strong association for Nnmt transcript levels in liver on chromosome 9. (C) Correlation between Nnmt mRNA levels and atherosclerosis. ((PDF)Click here for additional data file.S12 FigA) Transcript levels in aorta of HMDP mice were profiled and significant associations are plotted according to chromosomal position (x-axis) versus the location of the structural gene (y-axis). The strong diagonal line represents cis-eQTL, whereas the remainder are trans-eQTL signals. (B) Genome-wide association results in the HMDP demonstrating a strong association for Nnmt transcript levels in aorta on chromosome 9. (C) Genome-wide association results in the HMDP demonstrating a strong association for Nub1 transcript levels in aorta on chromosome 5.((PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableFor each strain and trait, the number of animals (n), trait average (avg), standard deviation (sd) and standard error of the mean (sem) are given. Separate worksheets are provided for clinical and metabolic traits and for male and female animals. (XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableSeparate worksheets are presented for genes showing positive or negative expression correlation in aorta or liver. (XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.


The associated region and its close neighbourhood harbours interesting candidate genes such as LETM1 and GAPDH that are important in glucose metabolism and have previously been implicated in the aetiology of diabetes mellitus. To further explore the genetics of this breed specificity, we screened the genome for reduced heterozygosity stretches private to the Belgian shepherd breed. This revealed a region with reduced heterozygosity that shows a statistically significant interaction (p = 0.025) with the association region on chromosome 3. This region also harbours some interesting candidate genes and regulatory regions but the exact mechanisms underlying the interaction are still unknown. Nevertheless, this finding provides a plausible explanation for breed-specific genetic effects for complex traits in dogs. Shepherd breeds are at low risk of developing diabetes mellitus. The findings in Belgian shepherds could be connected to a protective mechanism against the disease. Further insight into the regulation of glucose metabolism could improve diagnostic and therapeutic methods for diabetes mellitus.Diabetes mellitus is a serious health problem in both dogs and humans. Certain dog breeds show high prevalence of the disease, whereas other breeds are at low risk. Fructosamine and glycated haemoglobin (HbA1c) are two major biomarkers of glycaemia, where serum concentrations reflect glucose turnover over the past few weeks to months. In this study, we searched for genetic factors influencing variation in serum fructosamine concentration in healthy dogs using data from nine dog breeds. Considering all breeds together, we did not find any genome-wide significant associations to fructosamine serum concentration. However, by performing breed-specific analyses we revealed an association on chromosome 3 ( Diabetes mellitus constitutes a major human public health problem  and is aCanis familiaris) has been accompanying humans for several thousand years [The domestic dog , Sweden , Finland  and Denmark . In France, data was obtained before the creation of the Local Ethical Committee dedicated to clinical research (ComERC-ENVA). As the data were from client owned dogs undergoing normal veterinary exams, there was no animal experiment according to legal definitions in France. However, all local regulations related to clinical procedures were observed. In all countries, informed owners consent was obtained for use of samples and data for scientific research. All undertaken procedures were part of routine veterinary clinical exam in all countries and the local responsible co-investigator, who was also licensed to practice veterinary medicine within the EU, was responsible for collection of the samples.On examination day, all dogs were fasted and without access to water for at least two hours before the examination. Each dog underwent a physical examination, blood pressure measurement by high-definition oscillometry, ECG recording and a standard echocardiographic examination, as part of the LUPA protocol.Urine samples were collected by natural micturition and we performed standard urine analysis by dipstick and refractometer. We performed blood sampling by venipuncture and collected blood into 5-ml EDTA and serum tubes. In order to further verify the health status, we performed routine analysis of haematology and biochemistry including parameters of liver and kidney function, total serum protein concentration and serum electrolyte concentrations. Serum tubes for analysis of metabolic variables were centrifuged within 30 minutes of blood sample collection. Next, we harvested serum, transferred it into plastic cryotubes and froze it along with EDTA tubes for genetic analysis. Samples were kept at −80°C, later transported frozen to accredited laboratories , where they were analysed in batches by trained personnel.We analysed serum concentrations of glucose, insulin and fructosamine, using the following assays: glucose—Konelab 60i ; insulin—RIA ; fructosamine—Hariba ABX assay  analysed using Konelab 60i .We collected samples from 501 healthy dogs representing 9 breeds: Belgian shepherd, Boxer, Cavalier King Charles spaniel, Dachshund, Doberman pinscher, Finnish lapphund, German shepherd, Labrador retriever and Newfoundland .χ2 test to determine whether fructosamine concentrations significantly deviated from normality. Neither the overall distribution of fructosamine concentrations , nor the breed-specific distributions showed any significant departures from normality. Since serum fructosamine concentration is affected by total serum protein concentration [r.We performed all analyses and visualisations using the R software framework for statistical computing and visualisation . First, ntration , we analAll individuals have been genotyped using the Illumina 170k CanineHD Bead Chip  and we imported the raw data into GenABEL-native format  for furtBefore proceeding to association analyses, we performed an iterative genotyping quality control (QC) procedure. The original dataset contained genotyping information for 185,295 SNP markers. We removed 33,004 (17.8%) of these as non-informative markers with minor allele frequency below 5% and 13,600 (7.3%) markers due to call rate below 0.95. Thus, after quality control, 139,092 (75.1%) markers remained for further genetic analysis.Using 2,000 randomly selected markers, we calculated the average allelic-frequency-weighted identity-by-state (IBS) for all pairs of individuals in the population. All pairwise IBS values were below 0.95, thus we did not remove any individuals due to excessive relatedness. Next, we computed an IBS-based distance matrix using all autosomal SNPs that passed quality control. We then used the matrix to perform multidimensional scaling (MDS) in order to to visualise the genomic-kinships between individuals in two dimensions .λ and visualised observed vs. expected distribution of p-values using quantile-quantile (QQ) plots. We computed λ by using the highest 75% observed and theoretical p-values, expected under the null hypothesis. When testing for genotype-phenotype associations, we started by performing a basic association scan, as implemented in the GenABEL function qtscore. If there were indications of p-value inflation, we fitted a linear mixed model using the polygenic function implemented in GenABEL. For the across-breed analysis, the observed population structure , Belgian shepherds (N = 118) and German shepherds (N = 65). For the across-breed analyses, we used the same approach and thresholds as outlined in the preceding paragraphs. r2 and visualised using the LDheatmap function from the package LDheatmap [Subsequently, we evaluated the linkage disequilibrium (LD) structure in regions surrounding the strongest associated significant markers. LD was measured using Dheatmap . Gene anDheatmap . When reDheatmap  to map bfdiff = |fBS−fremainingbreeds|, where fBS and fotherbreeds are the allele frequencies in the Belgian shepherd breed and the remaining breeds respectively. We then evaluated the statistical significance of the observed differences using Fisher’s exact test to compare the observed reference-allele counts in BS and other breeds against respective allele counts under the null hypothesis .To identify regions where the Belgian shepherd breed alone shows reduced heterozygosity, we performed an across-breed scan for extreme differences in allele frequency. To this end, for every SNP, we calculated the absolute difference in the allele frequency of an arbitrarily chosen reference allele between BS and the remaining breeds: FST for three different sets of populations: BS vs. all the remaining breeds, pooled BS and GS vs. all the remaining breeds as well as for pooled BS, GS and BOX vs. all the remaining breeds. Next, for each set, we averaged FST values over a sliding window of n = 21 markers . Window size has been chosen empirically, and provides a good tradeoff between noise level and signal dilution. Non averaged FST per SNP are shown in In addition to the allele frequency analyses outlined in the previous paragraph, we performed in-depth analyses of fixation on chromosome CFA5. We computed fixation index, r2 = 0.082, p = 0.074) and hence we did not further consider serum protein concentration in our analysis. The median fructosamine concentration for all dogs was 289 μmol/l (inter-quartile range (IQR) 263–312 μmol/l). The upper reference value for the method was a fructosamine concentration of 380 μmol/l. The distribution by breed is shown in All included dogs had passed a general health examination without abnormal findings, we did not detect any ECG abnormalities, and all echocardiographic variables were within reference range. We did not detect any clinically relevant abnormalities in the urine analysis of included dogs. Furthermore, we did not detect any clinically relevant abnormalities in the haematological or serum biochemistry variables, including glucose, insulin, fructosamine and total serum protein concentrations. We did not find any significant correlation between protein and fructosamine concentrations (NBS = 118) identified an association on chromosome 3 (FructoCFA3) that reached the genome wide Bonferroni-based significance threshold  as an indication of potential cryptic relatedness in this breed and therefore also conducted a linear mixed model-based analysis incorporating the BS IBS-matrix to correct for this . The standard GWAS results and permutation results are shown in We performed a Genome Wide Association Study (GWAS) using fructosamine concentration as a continuous phenotype. When analysing all breeds together, we did not find any genome-wide significant associations to fructosamine concentrations  Figs. Infor this . The sigr2 > 0.8, transforming acidic coiled-coil containing protein 3 (TACC3), transmembrane protein 129 (TMEM129), stem-loop binding protein (SLBP) and family with sequence similarity 53 (FAM53A). Also, located ∼ 80kb from the leading SNP and outside the LD-block, we found the gene fibroblast growth factor receptor 3 (FGFR3), ∼ 88kb from the leading SNP leucine zipper-EF-hand containing transmembrane protein 1 (LETM1), ∼ 125kb from the most associated SNP (r2 = 0.23) we found a highly similar (98% similarity to canine GAPDH) paralog of the gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and ∼ 170kb from the most associated SNP, we found the Wolf-Hirschhorn syndrome candidate 1 (WHSC1) gene.The most associated SNP shows strong LD  . We also performed additional analyses of the entire CFA5 comparing fixation index (FST) between different subsets of BS, GS and BOX vs. the remaining breeds. The fixation index analyses results (kinesin family member 1B (KIF1B), meiosis-specific nuclear structural 1 (MNS1), PR domain containing 7 (PRDM7), urate (hydroxyiso-) hydrolase pseudogene (URAHP), growth arrest-specific 8 (GAS8), dysbindin domain containing 1 (DBNDD1), AFG3-like AAA ATPase 1 pseudogene (AFG3L1P), CENPB DNA-binding domains containing 1 (CENPBD1), differentially expressed in FDCP 8 homolog (DEF8), tubulin, beta 3 class III (TUBB3), melanocortin 1 receptor (MC1R), transcription factor 25 (TCF25), spire-type actin nucleation factor 2 (SPIRE2), Fanconi anemia, complementation group A (FANCA), 3-hydroxyisobutyrate dehydrogenase (HIBADH), zinc finger protein 276 (ZNF276), VPS9 domain containing 1 (VPS9D1), spermatogenesis associated 2-like (SPATA2L), cyclin-dependent kinase 10 (CDK10), spermatogenesis associated 33 (SPATA33), charged multivesicular body protein 1A (CHMP1A), renal dipeptidase 1 (DPEP1), copine VII (CPNE7) and spastic paraplegia 7 (SPG7).To search for potential genetic determinants of the observed breed specificity, we performed an across-breed scan for extreme differences in allele frequency to identify regions where the BS breed alone shows reduced heterozygosity. Our working hypothesis was that such regions might contain alleles that modulate the effect of the FructoCFA3 locus through epistatic interactions. For every SNP, we calculated the absolute difference in an arbitrarily chosen reference allele frequency between BS and the remaining studied breeds  5 CFA5, , S9 Fig ele = G) , and alm results , S10 Figp = 0.025). To explore the epistatic genotype-phenotype relationship in more detail, we stratified the data by CFA5 genotype and estimated the effect of FructoCFA3 in each of the three genotype-classes independently (n = 43) and G/G (n = 75) genotype classes, the results show that the effect of the FructoCFA3 locus is larger in the presence of the G-allele that is predominant in the BS breed.To evaluate whether this locus on CFA5 interacted with the main effect locus FructoCFA3, we fitted a linear regression model including both loci using the data from the non-Belgian shepherd populations, i.e. where the BS-allele on CFA5 segregated at a lower frequency and where FructoCFA3 did not have an effect in the earlier GWAS analyses. An F-test comparing the fit of a model with additive effects only for the two loci to a model including also the interaction between them shows that the interaction model provides a significantly better fit to the data , possibly suggesting a selection against the G/G (CFA5 locus) T/T (FructoCFA3) genotype combination. This signal is largely driven by GS dogs, since 64 out of the 65 GS dogs included in this study were homozygous at the CFA3 locus, having the C/C genotype.We observed that the distribution of individuals within the G/G interactor class was highly skewed compared to the A/A and A/G classes . Using PAlthough the aetiology of diabetes mellitus is probably multifactorial, genetic factors may influence susceptibility , 25. DiaSeveral studies have reported a low prevalence of diabetes mellitus in the German shepherd breed , 27, 28.Since fructosamine is formed in a non-enzymatic, irreversible reaction between glucose and free amino groups on serum proteins , serum cThis study provides evidence that a particular locus might be correlated to a trait in some breed(s) while not in others. A possible explanation for such observations is that the causal variant(s), linked to the SNP(s) detected in the GWAS, might be present in one breed or population while not in others. Another possibility is that the different genetic backgrounds of the breeds influence the causal variant, making its effect penetrant only in some of them. We hypothesise that the detected interaction between the main effect locus FructoCFA3 and the chromosome 5 (CFA5) locus explains why FructoCFA3 only shows a significant effect in the BS breed. The interacting locus on CFA5 then acts as a capacitor of the allele at FructoCFA3, as illustrated in χ2 test. This showed a highly significant difference (p = 3.4 × 10−16), supporting the observation that the high fructosamine FructoCFA3 genotype seem to be selected against, when in combination with the G/G interactor genotype.Following our epistatic hypothesis, one would expect FructoCFA3 to show an effect also in the German shepherd and Boxer breeds, since the G allele at the interacting locus is also fixed, or very close to fixation, in these breeds. In the Boxers we do, despite the small sample size (n = 15), observe a nominally significant (p = 0.0019) association to fructosamine levels at the CFA3 locus which agrees with the epistatic pattern we observe between these two loci. The lack of association in German shepherds is explained by the fact that all but one of the German shepherd dogs carry the low fructosamine FructoCFA3 genotype C/C. The high fructosamine FructoCFA3 genotype T/T is apparently rarely observed in combination with the G/G interactor genotype. This trend can also be observed in β-cells [NADH), which acts as a coenzyme in redox reactions, is generated. This step is catalysed by glyceraldehyde-3-phosphate dehydrogenase (GAPDH). A highly similar (98% similarity to canine GAPDH) GAPDH-paralog is localised approximately 125 kb from the main effect locus FructoCFA3. The NADH shuttle system transfers NAD+, the oxidized form of NADH, into the inner matrix of the mitochondria, the location for the citric acid cycle. Mitochondria consist of two membranes, a smooth outer membrane and a greatly convoluted inner membrane. The gene controlling Leucine Zipper-EF-Hand Containing Transmembrane Protein 1 (LETM1), localised approximately 88 kb from FructoCFA3, encodes a mitochondrial Ca2+/H+ antiporter on the inner mitochondrial membrane [β-cell and experiments on LETM1 knock-out mice have shown altered glucose metabolism in surviving heterozygous mice [LETM1 was shown to be down-regulated in obese mice, and it was suggested that it may participate in impaired insulin signalling in the adipose tissue. Other factors necessary to generate mitochondrial signaling leading to insulin secretion are being investigated, one candidate factor being NADH [NADH shuttles contribute to a sufficient amount of ATP being generated to trigger glucose-induced insulin secretion. Failure to generate mitochondrial metabolic signals through NADH shuttles might contribute to impairment of glucose-induced insulin secretion seen in diabetes mellitus [Glycolysis, citric acid cycle and oxidative phosphorylation play central roles in the metabolism of most cell types and are crucial to glucose-induced insulin secretion from pancreatic β-cells . In the membrane , 34. Sufous mice , LETM1 wing NADH . The NADmellitus .r2 to the most associated SNP > 0.8) to the FructoCFA3 signal are: TACC3, TMEM129, SLBP and FAM53A. Little is known about the potential link between glucose concentration and any of these genes.The four genes within the haplotype the most tightly linked  that overlaps with the strongest association signal remains to be characterised. The charged multivesicular body protein 1A (CHMP1A) gene located in the vicinity of the 5-th most significant signal in the scan for allele frequency difference is a particularly interesting candidate gene that has been implicated in diabetes mellitus. In their recent study, Liu et al. [CHMP1A gene might result in the disorders of epigenetic modules specific to type 2 diabetes.The reduced heterozygosity region on CFA5 harbours several other genes  that migu et al.  used netGAPDH and LETM1 are both potential candidate genes that have been previously indicated both in glucose metabolism and impairment of insulin signalling. The results presented here might be a starting point for future studies, giving further insights into the susceptibility to diabetes mellitus. Further studies are however required to identify the causative genetic variants influencing fructosamine concentration.We detected an association to serum fructosamine concentration on chromosome 3 (CFA3), specific to the BS breed. Further analysis revealed a reduced heterozygosity region private to Boxers and the shepherd breeds on CFA5. This region was found to interact with the main effect region on CFA3, possibly explaining the lack of association in the non BS breeds. The nature of the potential interaction between FructoCFA3 and the reduced heterozygosity region on CFA5 is intriguing and will require further investigation. This interaction also presents a plausible hypothesis that might explain the lack of reproducibility of GWAS results across breeds for other traits as well. S1 FigThe top, bottom and line through the middle of each box correspond to the 75th percentile (top quartile), the 25th percentile (bottom quartile) and the 50th percentile (median), respectively. The whiskers extend from the bottom 2.5th percentile to the top 97.5th percentile. Outliers, which are represented by black circles were included in the statistical analyses.(PNG)Click here for additional data file.S2 FigThe color indicates the breed and the symbol the country of origin. The plot includes 528 genotyped dogs, out of which 501 had serum fructosamine concentration measured.(JPEG)Click here for additional data file.S3 Fig(PNG)Click here for additional data file.S4 FigNo genome wide significant associations were found in this setup.(PNG)Click here for additional data file.S5 FigThe red line shows the expected profile under the null (slope = 1).(PNG)Click here for additional data file.S6 FigThe lower panel shows the p-values from the 10k permutation test, using the GRAMMAR+ transformed mixed model residuals.(PNG)Click here for additional data file.S7 FigThe red line shows the profile expected under the null hypothesis (slope = 1) and the observed profile is indicated by the black line (slope = 1.19).(PNG)Click here for additional data file.S8 FigPanel (A) shows a 0.5 Mb segment surrounding the associated region on CFA 3. The leading SNPs in the GWAS analysis are indicated in blue. Panel (B) shows the region of reduced heterozygosity on CFA5. Indicated in blue are the SNPs showing the greatest difference in reference allele count when comparing Belgian and German shepherds vs. remaining breeds.(PNG)Click here for additional data file.S9 FigThe difference at the leading SNP in the CFA5 locus is highlighted in red.(PNG)Click here for additional data file.S10 FigThere is a clear divergence between all three breeds  and all other breeds between 66–67Mb. Belgian shepherds alone show also high divergence around 81Mbp. The results are consistent with our analyses of allele frequency differences.(PDF)Click here for additional data file.S1 TableThe table shows the number of markers removed due to low minor allele frequency (row 1) and low call rate (row 2). The first column shows the quality control results when analyzing all breeds together, and successive columns display the results from the breed specific analysis.(PDF)Click here for additional data file.