Bovine respiratory disease complex (BRDC) is an infectious disease of cattle that is caused by a combination of viral and/or bacterial pathogens. Selection for cattle with reduced susceptibility to respiratory disease would provide a permanent tool for reducing the prevalence of BRDC. The objective of this study was to identify BRDC susceptibility loci in pre-weaned Holstein calves as a prerequisite to using genetic improvement as a tool for decreasing the prevalence of BRDC. High density SNP genotyping with the Illumina BovineHD BeadChip was conducted on 1257 male and 757 female Holstein calves from California (CA), and 767 calves identified as female from New Mexico (NM). Of these, 1382 were classified as BRDC cases, and 1396 were classified as controls, with all phenotypes assigned using the McGuirk health scoring system. During the acquisition of blood for DNA isolation, two deep pharyngeal and one mid-nasal diagnostic swab were obtained from each calf for the identification of bacterial and viral pathogens. Genome-wide association analyses were conducted using four analytical approaches . The most strongly associated SNPs from each individual analysis were ranked and evaluated for concordance. The heritability of susceptibility to BRDC in pre-weaned Holstein calves was estimated.The four statistical approaches produced highly concordant results for 373 top ranked SNPs that defined 126 chromosomal regions for the CA population. Similarly, in NM, 370 SNPs defined 138 genomic regions that were identified by all four approaches. When the two populations were combined  and analyzed, 324 SNPs defined 116 genomic regions that were associated with BRDC across all analytical methods. Heritability estimates for BRDC were 21% for both CA and NM as individual populations, but declined to 13% when the populations were combined.Four analytical approaches utilizing both single and multi-marker association methods revealed common genomic regions associated with BRDC susceptibility that can be further characterized and used for genomic selection. Moderate heritability estimates were observed for BRDC susceptibility in pre-weaned Holstein calves, thereby supporting the application of genomic selection to reduce the prevalence of BRDC in U.S. Holsteins.The online version of this article (doi:10.1186/1471-2164-15-1164) contains supplementary material, which is available to authorized users. Arcanobacterium pyogenes, Manheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma spp.[Bovine respiratory disease complex (BRDC) is the most common infectious disease and the leading natural cause of death among cattle in the United States , with thasma spp..Bovine respiratory disease complex is commonly observed in beef and dairy calves, with both acute and long term effects on health and performance –5. AcuteDespite the availability and use of vaccines for common respiratory pathogens, the prevalence of BRDC in dairy and beef animals is not declining , 14, 15.Prior to GWAA, seven of the pre-weaned California (CA) calves n = 2021) were removed due to conflicts with their reported disease status and health scores . Likewise, 73 (7.2%) of the control calves  were later reclassified as BRDC cases prior to weaning , and were subsequently removed from the analysis of the distribution of health scores  (Table P < 0.001 in both populations) may simply reflect local differences in BRDC disease progression, possibly due to environmental differences or differences in BRDC pathogen(s) at the two study sites  Table . The detHeritability Estimates. Implementation of EMMAX [h2 = 0.21) for BRDC susceptibility in CA Holstein calves. The pseudo-heritability, as previously defined h2 = 0.21) for BRDC susceptibility in the California calves. EMMAX-GRM analyses performed using an additive genetic model also produced a moderate pseudo-heritability estimate (h2 = 0.21) for BRDC susceptibility in the NM calves, which was nearly identical to the GBLUP REML estimate (h2 = 0.20). In a final analysis, we combined the NM and CA calves into a single case–control cohort. EMMAX-GRM pseudo-heritability for BRDC susceptibility within the combined cohort was again moderate (h2 = 0.13), but considerably lower than the estimates produced for the individual populations. This result was supported by the GBLUP REML estimated heritability for the combined population (h2 = 0.13), which was very similar to EMMAX-GRM , cystic ovaries (0.05), ketosis (0.01-0.14), metritis (0.01-0.07) and lameness (0.02-0.03), but lower than estimates for displaced abomasum (0.32) and retained placenta (0.02-0.36) previously reported in dairy cows –26. BoviModerate heritability estimates for BRDC susceptibility strongly suggest that there is ample opportunity to use genomic selection to reduce the prevalence of BRDC in Holstein dairy cattle. The reduction in heritability estimated for the combined CA and NM cohort, in comparison to the individual population estimates, is a complex phenomenon that likely reflects environmental differences as well as differences in the host genetic basis for differential susceptibility to a variety of BRDC pathogens that were detected at variable frequencies within the two populations. However, it should also be noted that the NM calves also tended to be older and presumably more clinically ill (as defined by the McGuirk Scoring System)  at samplP = 2.95 × 10-6, Additional file P = 3.74 × 10-7, Additional file th among SNPs most strongly associated (P = 2.14 × 10-5) with BRDC susceptibility using EIGENSTRAT  gene is proximal and adjacent to this GWAA signal.When the top 2,000 SNPs  for CA were compared across the four analytical approaches for the case–control phenotype, a strong concordance of association with BRDC susceptibility was observed. Among the top 2,000 SNPs from each analytical approach, each SNP (or sliding window of 7 SNPs centered on the third SNP within each window for GBLUP) was given a ranking  and these rankings were summed across all analytical approaches for each population Tables , 4 and 5PVRL1 is a member of the immunoglobulin superfamily that is known to mediate entry of several herpes viruses, including bovine herpes virus 1 (BHV-1), the causative agent of infectious bovine rhinotracheitis (IBR), which is also a known etiological agent of BRDC [BHV-1 was not diagnostically detected in the mid-nasal or pharyngeal swabs from the CA Holstein calves, but previous experimental challenge and pathology studies have demonstrated that many respiratory viruses, including BHV-1, are only transiently present in the pharynx prior to settling into the lung . Therefore, it is unsurprising that BHV-1 was not detected by our diagnostic procedures, although the host genomic signature of this pathogen was clearly detected and well supported statistically by all GWAA approaches. Our failure to detect BHV-1 likely reflects the temporal and/or transient differences in viral expression and pathology , including latency of the virus in the trigeminal ganglion, or perhaps other factors, both known and unknown [BHV-1 causes a lifelong latent infection and sporadic shedding of the virus which can lead to horizontal transmission to other animals in the herd. In addition to BRDC, IBR also has the potential to cause rhinotracheitis, vaginitis, balanoposhitis, abortion, conjunctivitis, and enteritis [BHV-1 infected animals often occur in the mouth and nose, with mortality as high as 10% [ of BRDC . Notably unknown . BHV-1 cnteritis . Ulcers h as 10% .th ranked SNP by EIGENSTRAT , a gene that is involved in many biological processes, and that is most highly expressed in both lung and skeletal muscle [AZIN1 has been associated with Fanconi’s anemia, tumorogenesis, cirrhosis of the liver, and regulation of polyamine synthesis [AZIN1 protein (Az1) regulates ornithine decarboxylase stability, which degrades polyamines, and in turn, up-regulates AZIN1. Interestingly, ornithine carboxylase activity and elevated polyamine levels are detected when cells are transformed by viral agents [AZIN1  may be more likely to become infected with viruses, such as hepatitis C [AZIN1 may also enhance susceptibility to other viruses, such as those implicated in the clinical manifestation of BRDC. Flanking genes within this region include brain and acute leukemia cytoplasmic , and ATPase, H+ transporting, lysosomal 42 kDa, V1 subunit C1 . Mutations in BAALC are implicated in myeloid leukemogenesis, and are associated with a poor prognosis in acute myeloid leukemia as well as acute lymphoblastic leukemia [The most highly ranked SNP in this region across all analyses was proximal to l muscle . AZIN1 hynthesis –37. The l agents . Individatitis C . Some muleukemia . The ATPleukemia . In drosleukemia . It is aleukemia .th, 7th, and 9th, respectively), while EMMAX-GRM ranked this locus 65th  and EIGENSTRAT (P = 1 × 10-4), respectively. The BTA23 SNPs identified as being associated with BRDC susceptibility are within an intron and exon of the dystonin gene . Interestingly, the previously described positional candidate gene PVRL1 and DST share similar functions, as they both play a role in herpes virus infections [The fourth most significant locus for CA across all analyses was a 10 kb region on BTA23 between 3 and 4 Mb  by EIGENSTRAT , and 156th by EMMAX-GRM (P = 0.003). The associated SNP lies within intron two of mastermind-like 2 (MAML2) which has been associated with mucoepidermoid carcinoma, and height in humans [The locus ranked fifth across all GWAA approaches in the CA population was on BTA15 between 14–15 Mb Table . This lon humans –49.P = 2.93 × 10-6, Additional file P = 1.39 × 10-5, Additional file st ranked SNP for EIGENSTRAT (P = 2.99 × 10-5). Investigation of the associated region (105 kb) revealed two uncharacterized loci  [Manhattan plots for the NM calves for all analyses are shown in Figure TRNAG-CCC, Entrez gene: 100126533) and transfer RNA cysteine (anticodon ACA)  genes, respectively. This region contained the 5th ranked SNP for EMMAX-GRM .Across all GWAA analyses, the second most highly ranked locus for NM was located on BTA14 between 7 and 8 Mb and was characterized by 17 SNPs (Table th ranked SNP for GBLUP (PVE = 0.06), the 58th ranked SNP for EMMAX-GRM (P = 0.001), the 62nd ranked SNP for FvR (P = 5.69 × 10-5) and the 90th ranked SNP for EIGENSTRAT (P = 7.4 × 10-5). This QTL spanning 7.2 kb involved 4 SNPs located in introns 1–3 of the zinc finger protein 850  gene. At present, little is known about the function of this gene and protein, which precludes specific biological inferences in relation to BRDC.The third ranked QTL for NM was on BTA18 (65–66 Mb) Table . This reth ranked locus for NM was located on BTA12 (77–78 Mb) , and 175th by FvR (P = 0.0002). Heparan sulfate 6-O-sulfotransferase 3  is the only annotated gene in this region. One previous study suggests that HS6ST3 may perform diverse functions [HS6ST3 with obesity in humans [The 4unctions , and morn humans , 53.th ranked locus for NM was located on BTA5, between 23 and 24 Mb , and ranked 192nd with EIGENSTRAT (P = 0.0002). No genes or obvious biological candidates were found in this genomic region, although an expressed sequence tagged site (EST Bt.86019) [The 5t.86019)  detectedPVRL1, see Table P = 1.95 × 10-5, Additional file P = 6.42 × 10-6, Additional file  for the combined population  were compared across the four analytical approaches for the case–control phenotype, we noted that the results were skewed toward those initially reported for the CA population . This SNP was ranked 188th by GBLUP (PVE = 0.09). The BTA19 SNP associated with BRDC susceptibility is located within intron 7 of the myeloperoxidase  gene. The protein encoded by MPO is central to the activity of neutrophils, and sputum myeloperoxidase is recognized as a putative biomarker for neutrophil activity in pulmonary disease [Across all analyses, the second strongest association n = 1 SNP) in the combined cohort was observed on BTA19 (9–10 Mb) Table . This SN SNP in t disease .th ranked SNP by EIGENSTRAT , and the 299th ranked SNP by FvR (P = 0.003). At present, no annotated genes have been described within 50 kb of this SNP, thereby precluding the identification of one or more suitable positional candidate genes.For the combined cohorts (CA and NM), the third highest ranked locus across all analyses was represented by a single SNP located on BTA15 (31–32 Mb) , 103rd using GBLUP (PVE = 0.09), and 123rd using EMMAX-GRM (P = 0.0002). The most proximal gene to this SNP is retinol dehydrogenease 14  , which is located approximately 13 kb away. This gene encodes a mitochondrial protein that recognizes retinoids as substrates and may protect the mitochondria from oxidative stress associated with exposure to retinaldehyde [The fourth ranked genomic region for the combined cohort (CA + NM) was located on BTA11 (80–81 Mb), and was ranked 64aldehyde .th, P = 1.32 × 10-5 Additional file th, PVE = 0.10; Additional file th, P = 6.02 × 10-5, Additional file th by EIGENSTRAT (P = 0.0004). The most strongly associated SNPs were located within introns 2 and 10 of the slit homolog 3 ) gene [SLIT3 is a very large gene spanning 727 kb. The protein encoded by SLIT3 has numerous functions, one of which includes the down regulation of inflammatory mediators such as IL-1β, Il-6 and IL-8 [SLIT3 has also been implicated in cell migration, axon guidance, inflammation, angiogenesis and organogenesis [SLIT family of proteins features a tandem of four leucine-rich repeat domains which mediate binding to the immunoglobulin-like domains of the Roundabout (Robo) family of genes [SLIT3 function may also influence susceptibility to BRDC, making it a plausible functional candidate gene for future investigations.The fifth ranked locus identified by all methodological approaches revealed a strong positional candidate gene on BTA20 0–1 Mb) that is supported by 13 SNPs and spans 19 kb Table . This QT Mb that 7)) gene . SLIT3 iand IL-8 . SLIT3 hogenesis . The SLIof genes . SLIT-Roof genes . Airway of genes . TherefoDifferences existed in the CA and NM Holstein calf populations in the ages of the calves when sampled, the average McGuirk health score  of the cPVRL1) and BTA23 (DST) that are known to mediate herpes virus entry into host cells, a gene involved in viral susceptibility on BTA14 (AZIN1), and a gene that modulates inflammation on BTA20 (SLIT3). Studies in Bos taurus feedlot cattle are currently underway to determine if some of the same loci are associated with enhanced susceptibility to BRDC in older cattle from different breeds.The BRDC heritability estimate of 0.21 for CA and NM calves was consistent across the two populations, but dropped to 0.13 when the populations were combined. This decline is most likely due to pathogen and environmental differences at the two study sites. However, the moderate heritability (0.13) estimated for BRDC susceptibility in the combined Holstein population is sufficient to support a nation-wide approach to reduce the prevalence of BRDC via genomic selection. Although each of the methods used to conduct the GWAA have strengths and weaknesses, the concordance between the loci associated with BRDC susceptibility across the four approaches has resulted in the identification of loci that are strong candidates for harboring causal mutations, and these QTL signals are less likely to have arisen solely by type 1 error. Several biologically meaningful positional candidate loci were identified in this study. Among these positional candidates are genes on BTA15  with the causal mutations for genomic selection, the accuracy of utilizing causal mutations to predict susceptibility will not decay over time, and likely will have utility across breeds, which enhances the value and longevity of the final marker panel to the industry. Identification of the causal mutations underlying BRDC QTL will ensure that the initial work of this study is sustainably translated to industry, with QTLs associated with enhanced BRDC susceptibility providing the foundation for identifying causal mutations with the largest effects on susceptibility, so that these may be incorporated into predicted transmitting abilities (PTA) for commercial dairy artificial insemination (AI) sires. As greater than 70% of the dairy calves in the U.S. are conceived by AI annually  this wilThis study was conducted with an approved animal use protocol from the Institutional Animal Care and Use Committees at the University of California at Davis 16431), Washington State University (04110), New Mexico State University (2011–010), Texas A&M University (2014–0032) and the University of Missouri (7505). California Holstein calves were enrolled in a case–control study from July 2011 through January 2012. Collectively, 2,014 calves were enrolled as cases  and controls , with 1,257 males and 757 females. Animals in New Mexico were enrolled from August 2011 through June 2012 in a case control study at three calf raising facilities within the Clovis and Portales areas of New Mexico. All study animals were identified as heifers in the field, and consisted of 372 controls and 376 cases. The animals were sampled such that the proportions of cases and controls were almost identical across seasons of the year and locations in order to eliminate these effects as sources of variation in the experimental design. Details on the calf raising facilities, and the management and vaccination of the calves are specified in Additional file 6431, WasCalves treated with antibiotics within 10 days prior to study onset were not considered for enrollment since treatment could facilitate negative bacterial cultures. Calves younger than 22 days of age were not enrolled to avoid potential false positive results during viral diagnostic tests  due to the modified-live vaccines administered at 1 and 8 days of age . Calves 3, HEPES, Gentamycin, Amphotericin B). Pharyngeal recess swabbing utilized a 27-inch sterile guarded swab with a polyester fiber tip . The distance from the nose to the medial canthus of the eye was measured and marked as a gauge on the guarded swab. The swab was inserted through a nostril into the ventral meatus and then advanced toward the pharyngeal recess until reaching the gauge mark at the external naris. The swab was then pushed to open the guarding cap and advanced approximately two inches, rotated against the pharyngeal recess surface for 15 seconds, retracted back into the guarding sheath, and removed from the nose. The swab was cut two inches above the polyester tip, and placed into the same tube of viral transport media containing the nasopharyngeal swab. A second deep pharyngeal swab was placed into 1 ml of bacterial transport media (Brucella broth with 15% glycerol) for the detection of bacteria. All samples were stored on wet ice in the dark until completion of sampling and delivery to the lab. Scissors used to cut individual swabs were disinfected with a dilute Povidone iodine solution, and dried before each use. Bacterial samples were submitted daily for bacterial respiratory pathogen culture to either the California Animal Health and Food Safety lab system  for the CA samples, or to the Washington Animal Disease Diagnostic Laboratory at Washington State University  for the NM samples. Viral qPCR samples from CA and NM were stored at -80°C until submission and processing at the CAHFS Davis Branch. Whole blood and serum were also collected for use in the GWAA studies.Each day after completion of respiratory scoring, with identification of cases and matched controls, a veterinarian and facility personnel returned to those animals to collect samples. All case and control calves had nasopharyngeal and pharyngeal recess swabs collected for qPCR diagnostics, and a second pharyngeal recess swab was collected for aerobic bacteria and mycoplasma respiratory pathogen culturing. Samples collected from the nasopharyngeal region utilized a six-inch sterile unguarded polyester swab that was inserted five inches into a clean naris and rotated against the surface for 15 seconds. Thereafter, the swab was removed and cut ¼ inch above the polyester tip, and placed into 3 ml of viral transport media  and be chronically affected (P = 0.03) with BRDC than females, and Cusack et al. [P = 0.03) to die in the feedlot due to BRDC than were cows. In contrast, Ribble et al. [To date, conflicting results have emerged regarding the potential influence of gender on susceptibility to BRDC. Male beef calves have been reported to be more susceptible to BRDC than female calves, both at pre-weaning (14.4% compared to 9.5%) and post-weaning (17.2% compared to 12.5%)  found thk et al.  found the et al.  found the et al.  reportedHistophilus somni, Pasteurella multocida, Bibersteinia trehalosi, Mannheimia haemolytica. The culture and qPCR of bacteria and mycoplasma for samples derived from the CA calves were conducted at the California Animal Health and Food Safety Laboratory System . Similar samples obtained from the NM calves were processed using standardized procedures at WADDL . Additional aerobic culture methods are presented in the Additional file Culturing for aerobic respiratory pathogens allowed for the identification of the following important bacterial species: Diagnostics for bovine corona virus, bovine respiratory syncytial virus, bovine viral diarrhea virus and bovine herpes virus type I were conducted from an aliquot of 3 ml of transport media (previously described) contained in 5 ml cryotubes that housed a mid-nasal and deep pharyngeal swab taken from each calf. Swabs were placed into the cryotubes immediately post collection and kept on ice until they reached the laboratory where they were frozen until analyzed. For the diagnostic analysis, samples were thawed and an aliquot of the transport media was removed for use in the qPCR diagnostics. Diagnostics were performed at the University of California Animal Health and Food Safety Laboratory System in Davis California.P = 0.05.The estimated odds ratio for BRDC pathogens and case status  were calculated in R. A two-tailed Fisher’s exact test was used to determine if the odds ratios differed significantly from 1, with a significance threshold of Bovine DNA was isolated from 3 ml of whole blood collected in EDTA tubes using the Puregene DNA extraction kit, according to the manufacturer’s instructions . DNA samples were quantified using NanoDrop , spectrophotometry and purity was estimated using the 260/280 ratio. Samples with 260/280 ratios between 1.8 and 2.0 were diluted to 50 ng/μl, and 250 ng of each calf’s DNA was genotyped using the Illumina BovineHD Genotyping Beadchip at GeneSeek . The Illumina  BovineHD Beadchip assay  containeP < 0.0005 here). At this point, GBLUP estimates of SNP effects were estimated as the regression of SNP effects on additive genetic merits as described by Taylor [For the GBLUP analyses, custom developed software was used to generate the genomic relationship matrix  and to fy Taylor . Pseudo- Taylor [ was simi Taylor [, with RE Taylor [.Susceptibility to BRDC was treated as a binary trait based on the classification of the health scores obtained from the McGuirk  scoring y = Xβ + Zu + ϵ, where y is an n × 1 vector of the observed phenotypes, X is a n × q matrix of fixed effects, β is a q × 1 vector representing the levels of the fixed effects, and Z is a n × t matrix relating the instances of the random effect to the phenotypes. Herein, we assume that Z is simply the identity matrix I, and K is the matrix of pairwise genetic relationships among all samples. To solve the mixed model equations using the generalized least squares solution, the variance components http://doc.goldenhelix.com/SVS/7.7.6/mixed_models.html; http://doc.goldenhelix.com/SVS/latest/mixed_models.html).Using the binary BRDC case–control phenotype, the EMMAX-GRM analyses were conducted using an additive model and consisted of single marker tests of association , with coescribed , with stescribed  computedstimated 1–22. We The FvR regression analysis utilized the binary BRDC case–control phenotype as the dependent variable within an additive model, with all genotypes recoded numerically based on the copy number of the minor allele. Recoding also included the same ploidy-based gender correction for X-linked loci described for the EMMAX-GRM procedure above. We corrected for population stratification using principal components analysis (PCA), with PCA-based correction of the input data using established methods , 76. To P-value is calculated by: P-value = P(X > F*) where X ~ , with m and r being the rank of the full and reduced model coefficient matrices, respectively, and n being the number of observations. All relevant formulae for PCA correction of input data, the FvR method, and recoding procedures have previously been described [http://doc.goldenhelix.com/SVS/latest/formulas_theories.html#ftfullvreduced; http://doc.goldenhelix.com/SVS/latest/principal_component_analysis.html; http://doc.goldenhelix.com/SVS/latest/principal_component_analysis.html?highlight=pca%20corrected%20input%20data).The escribed , 78. Forescribed  and FvR ijk = μ + Sexi + βAgej + ak + eijk where yijk is a binary variable set to 1 for cases and 0 for controls of the ith sex , jth age (in days) and with additive genetic merit for susceptibility to BRDC of ak. The eijk are residuals assumed identically and independently distributed with null mean and for e = {eijk} we assumed Var(e) =k} we assumed Var(a) = In the GBLUP analyses, we fit the model yp matrix , 70. Varn Taylor . The GBLn Taylor . Previoun Taylor  and McCln Taylor .gijadjusted) for each principal component is computed by:EIGENSTRAT  is a metgij denotes genotype of individual j at SNP i and aj denotes ancestry of individual j along a given principal component. The phenotypic adjustment is similarly computed for each principal component as:where Finally, the association between the adjusted genotypes and phenotypes is tested using the test statistic:ri is the correlation between the adjusted genotype at SNP i  and the adjusted phenotype , N is the number of individuals, and K is the number of principal components used for adjustment. This test statistic is χ2 distributed with one degree of freedom.where In order to correct for the stratification in the genotypic data, the first (top) 100, 5, and 80 principal components were used in the EIGENSTRAT analysis of California, New Mexico, and the combined data sets, respectively. The use of these principal components also corrected for the relatedness  that was found among the calves . For comparisons of the top SNPs and loci identified across the four analytical approaches, the top ranked 2,000 SNPs from each of the four analytical approaches were identified and compared. SNPs were ranked either by P value  or by PVE (GBLUP) from 1 (most significant SNP) to 2,000. For comparisons of SNPs that were associated with BRDC susceptibility across all statistical methodologies  as shown in Tables Significance for all association tests  was based on the recommendation of the Wellcome Trust Case Control Consortium  where unQTL were defined by the number of top ranked SNPs that were associated with BRDC susceptibility for a genomic region within each statistical method. For example, if a single SNP within a region was among the top ranked SNPs, the locus would be defined by the single SNP. However, if several highly ranked SNPs were in close proximity to one another, the QTL would be defined by the chromosomal location of the outermost SNPs in that region. An example of this would be the QTL on BTA4 (48–49 Mb) in the CA + NM population where the QTL is defined by 7 SNPs within the region 48,052,484 to 48,149,487 bp.The Bovine Respiratory Disease Complex Coordinated Agricultural Project Research Team - Team members include: David Anderson, Noah Cohen, Allan Dabney, Scott Dindot, Mark Enns, Laurel Gershwin, Joseph S Neibergs, Tim Ross, Loren Skow, Milton Thomas, Cassandra Tucker, Curt Van Tassell, and Aldroaldo Zanella.Supplementary Information.Table S1. Distribution of pre-weaned Holstein calf health scores (McGuirk 2008). Table S2. Top 30 ranked bovine SNPs by the EMMAX-GRM analyses of the California, New Mexico and combined populations. Table S3. Top 30 ranked bovine SNPs for GBLUP analyses of California, New Mexico and combined populations. Table S4. Top 30 ranked bovine SNPs resulting from the FvR analyses of California, New Mexico and the combined populations. Table S5. Top 30 ranked bovine SNP for EIGENSTRAT analyses of California, New Mexico and combined populations. (PDF 817 KB)Additional file 1: Additional file 2: Figure S1: EMMAX-GRM P-P plots with age and sex in the model for: A. California, and B. NM. In panel C., sex, age and population of origin were included in the combined CA-NM population. For each of the plots in panels A-C, the observed –log10 P-value (on the Y axis) is plotted against the expected –log10 P-value (on the X axis). (PDF 126 KB)Additional file 3: Figure S2: FvR P-P plots with sex and age in the model for: A. California with correction for 53 principle components, and B. NM calves with correction of 9 principle components. In panel C. sex, age and population of origin were included in the combined CA-NM population which included correction of 91 principle components. For each of the plots in panels A-C, the observed –log10 P-value (on the Y axis) is plotted against the expected –log10 P-value (on the X axis). (PDF 128 KB)Additional file 4: Figure S3: The first four EIGENSTRAT principal component analyses plots showing the distribution of presumed half-siblings (based on the genomic relationship matrix) of ten sires with the most offspring within the California calf study population. Each sire’s offspring are coded with a different color and tend to cluster together within the study population. In panel A., the first principal component (PC1) (plotted on the X axis) is compared against the second principal component (PC2)( on the Y axis). In panel B., PC1 is again plotted on the X axis but is now compared against the third principal component (PC3) on the Y axis. Principal component 2 (PC2) (on the X axis) is plotted against the third principal component (PC3)( on the Y axis) in panel C. In Panel D., PC1 (on the X axis) was plotted against principal component 4 (PC4) (on the Y axis). In panel E., PC2 (on the X axis) was compared to PC4 (on the Y axis). Finally, in panel F., PC1 was plotted (on the X axis) and compared to PC4 (on the Y axis). (PDF 816 KB)Additional file 5: Figure S4: The first four EIGENSTRAT principal component analyses plots showing the distribution of presumed half-siblings (based on the genomic relationship matrix) of the ten sires with the most offspring within the New Mexico calf population. Each sire’s offspring are coded with a different color and tend to cluster together within the study population. In panel A., the first principal component (PC1) (plotted on the X axis) is compared against the second principal component (PC2)( on the Y axis). In panel B., PC1 is again plotted on the X axis but is now compared against the third principal component (PC3) on the Y axis. Principal component 2 (PC2) (on the X axis) is plotted against the third principal component (PC3)( on the Y axis) in panel C. In Panel D., PC1 (on the X axis) was plotted against principal component 4 (PC4) (on the Y axis). In panel E., PC2 (on the X axis) was compared to PC4 (on the Y axis). Finally, in panel F., PC1 was plotted (on the X axis) and compared to PC4 (on the Y axis). (PDF 360 KB)10 P-value (on the Y axis) is plotted against the expected –log10 P-value (on the X axis). (PDF 169 KB)Additional file 6: Figure S5: EIGENSTRAT Q-Q plots: A. CA with correction of 100 principle components and age and sex included in the model, B. NM calves with correction of 5 principle components with only age included in the model, and C. the combined CA-NM population with correction of 80 principle components and sex and age included in the model. For each of the plots in panels A-C, the observed –log


Here we show that adult A. suum is RNAi competent, and highlight the induction, spread and consistency of RNAi across multiple tissue types. This platform provides a new opportunity to undertake whole organism-, tissue- and cell-level gene function studies to enhance target validation processes for nematode parasites of veterinary/medical significance.The sustainable control of animal parasitic nematodes requires the development of efficient functional genomics platforms to facilitate target validation and enhance anthelmintic discovery. Unfortunately, the utility of RNA interference (RNAi) for the validation of novel drug targets in nematode parasites remains problematic.  Ascaris suum is a gastrointestinal parasite of pigs which impacts significantly on pig health through reductions in finishing weights, feed conversion efficiencies and carcass quality. Recent publications have highlighted the zoonotic potential of A. suum and there is now evidence to suggest that A. suum is more than a close relative of the human pathogen, Ascaris lumbricoides  is an appealing reverse genetics tool and, whilst its utility for the validation of drug targets in veterinary parasites is widely accepted, issues with their variable sensitivity to standard RNAi approaches have undermined the development of robust gene silencing platforms see . AlthougA. suum is well established as a parasite model system and more is known about the basic biology of A. suum than any other veterinary parasite. Amongst nematode parasites, A. suum has the most genomic/transcriptomic resources and it offers unrivalled utility for cell biology/physiology/biochemistry studies  RNAi is readily achievable in adult A. suum. We have developed a method for the induction of RNAi in adult A. suum through the injection of double-stranded RNA (dsRNA)  into the pseudocoelomic cavity of female worms . Worms were maintained in Ascaris Ringers Solution  for a maximum of 24 h prior to use, and for the duration of the RNAi experiments (for specific methodology see Here we describe the development of an RNAi platform in adult orms see A. Adult P < 0.05) knockdown across multiple targets (A. suum elongation factor 1a (As-eft-1); Elongation factor 1b (As-eft-2); GMP reductase (As-gmpr), troponin C (As-tnc-1), Ras-related protein (As-rab-3), haemoglobin (As-hb-1), and two nicotinic acetylcholine receptor subunits (As-unc-29 and As-unc-38)) at ⩾3 days post-RNAi trigger delivery; (b) occurs relatively quickly ); (c) is consistent and reproducible ); and (d) persists   the majority of studies only targeted one or two genes (see We have achieved target transcript knockdown that: (a) is robust (e.g. ⩾60.0% (y 8) see . Our finmmon see . One stuet types , providienes see , making A. suum. Our RNAi trigger delivery approach involves the injection of dsRNA into the pseudocoelomic cavity at a position approximately 1 cm anterior to the gonopore on the ventral side, where worms were injected at an angle (approximately 20°) to avoid piercing the gut , and also in muscle bag cells  RNAi is capable of spreading in adult  gut see A. We havints see B. The siA. suum RNAi is in contrast to that described for other APNs   . As suggA. suum tissue-specific targets are susceptible to RNAi including ‘neuronal’ genes. We have shown that a range of targets are susceptible to RNAi in adult A. suum including those expressed in: multiple tissues ; muscle (As-tnc-1); nerve (As-rab-3); gut/body wall (As-hb-1); and the neuromuscular system   , nerve, hypodermis, and systems associated with reproduction and excretion, and it is therefore an ideal vehicle to carry dsRNA cargo to its target, offering a systemic delivery system.(iii) Multiple ans) see . Samaras. C. elegans , providc-38 see .As-unc-29 and As-unc-38) are delivered as a cocktail (A. suum.(iv) A multi-target dsRNA cocktail delivery approach is efficient in inducing RNAi. Here we demonstrate that effective gene silencing is achievable when two dsRNA species, targeting different transcripts  RNAi is consistent across geographical isolates of  to RNAi , and thatic RNAi , and empA. suum. We have shown that RNAi is achievable across multiple targets and tissues and present a novel, efficient RNAi delivery approach. The appeal of the A. suum RNAi platform described here is enhanced by the unique experimental tractability of the adult worm and the availability of a number of post-RNAi functional bioassays at the whole worm , tissue  and cell (muscle and neuron electrophysiology) levels (see We believe that this study is the first report of successful gene silencing in adult vels see . This pr


Single single-nucleotide polymorphism (SNP) genome-wide association studies (SSGWAS) may fail to identify loci with modest effects on a trait. The recently developed regional heritability mapping (RHM) method can potentially identify such loci. In this study, RHM was compared with the SSGWAS for blood lipid traits (high-density lipoprotein (HDL), low-density lipoprotein (LDL), plasma concentrations of total cholesterol (TC) and triglycerides (TG)). Data comprised 2246 adults from isolated populations genotyped using ∼300 000 SNP arrays. The results were compared with large meta-analyses of these traits for validation. Using RHM, two significant regions affecting HDL on chromosomes 15 and 16 and one affecting LDL on chromosome 19 were identified. These regions covered the most significant SNPs associated with HDL and LDL from the meta-analysis. The chromosome 19 region was identified in our data despite the fact that the most significant SNP in the meta-analysis (or any SNP tagging it) was not genotyped in our SNP array. The SSGWAS identified one SNP associated with HDL on chromosome 16  and one on chromosome 10 . The results further confirm that RHM can have better power than SSGWAS in detecting causal regions including regions containing crucial ungenotyped variants. This study suggests that RHM can be a useful tool to explain some of the ‘missing heritability' of complex trait variation. Blood lipoprotein concentration is an important risk factor for coronary heart disease and stroke  because The aims of this study were to identify regions and SNPs that contribute to variation in blood lipid traits using RHM and SSGWAS analysis in three isolated Croatian populations and to compare the effectiveness of the RHM approach in capturing the heritability of blood lipid traits with SSGWAS in these populations. We compared our results with those of a large meta-analysis for validation.A total of 2246 adult volunteers from three Southern European populations in the city of Split and the islands of Vis and Korcula on the Dalmatian coast of Croatia participated in 3 epidemiological field studies. All cohort studies were approved by the Ethical Committee of the Medical School, University of Zagreb and the Multi-Centre Research Ethics Committee for Scotland. All participants gave written informed consent. Fasting plasma levels of four blood lipid traits  were recorded. P<10−8) and call rate <0.98 were discarded. Individuals were excluded with a call rate of <0.98. After these quality control stages, 2246 individuals remained, and 268 651 autosomal SNPs were common to all the population samples and were used in our analysis. Genotypic quality control was done using the GenABEL library version 1.6-5  CNV370 for Korcula and Split). Quality control procedures were performed per SNP and per individual. SNPs with MAF <0.001, Hardy–Weinberg equilibrium  as described by In the RHM method, the genome was divided into regions using sequential windows of genotyped markers to assess the contribution from each region to the trait variance. A random effects model was applied to the adjusted and rank-transformed phenotypes. For this study two random effects were included: the regional genomic polygenic and whole genome polygenic matrices, as described by y represents the adjusted and rank-transformed phenotype of interest, n is the number of individuals, μ is the overall mean, a is the whole genome additive genetic effect, matrix Zw is the whole genome relatedness matrix, r is the regional additive genetic effect, matrix Zr is the regional relatedness matrix and e is the vector of residuals. The regional and whole genome genetic values were estimated using a relationship matrix derived from markers as described by where fij is the genomic relationship between individuals i and j, gik (gjk) is the genotype of the individual i (j) at the k-th SNP , pk is the frequency of the allele B of k-th SNP and m is the number of SNPs used for relationship estimation, representing the total number of autosomal SNPs for the whole genome relationship matrix or the number of SNPs in the region for the regional genomic relationship matrix. Obs(#hom)k and E(#hom)k are the observed number and expected number of homozygous genotypes under Hardy–Weinberg equilibrium at the k-th SNP. Whole genomic, regional genomic and residual variances were where In the RHM analyses, the genome was scanned using regions (windows) spanning 100 consecutive genotyped SNPs. Between the sequential windows we allowed for a 50-SNP overlap as explained by P-value threshold of 6.73, as 268 651 autosomal SNPs were used in this analysis. For the RHM method, in 100-SNP windows with 50-SNP overlap and 10-SNP windows with 5-SNP overlap, 5373 and 53 732 windows were tested, respectively, and half that number was used to estimate the Bonferroni corrected significance thresholds and account for overlapping windows  was 45.8% (s.e. 6.6%). Two significant windows associated with HDL were detected using the 100-SNP window analysis at the 5% significance level after Bonferroni correction, one on chromosome 15 and the other on chromosome 16 . Fine-maUsing SSGWAS, no SNP associated with LDL was detected at the genome-wide significance level. The estimated heritability of LDL averaged over the RHM genome scan using 100-SNP windows was 47.4% (s.e. 6.5%). RHM identified a region on chromosome 19 from 49 455 904 to 50 999 246 base pair associated with LDL. This region encompassed two genome-wide significant overlapping 100-SNP windows explaining 4.8% and 6.2% of the trait variance for LDL, respectively. The fine-mapping analysis using 10-SNP windows narrowed the region down to a single 10-SNP region with an estimated 7.3% heritability.P-values of >5.00 for TC and TG.Using the RHM genome scan, the estimated heritability for TC was 42.6% (s.e. 6.4%) and for TG was 18.8% (s.e. 6.3%). However, both the RHM and SSGWAS analyses failed to detect SNPs and/or regions associated with these traits at the genome-wide or suggestive significance levels. http://www.genome.gov/gwastudies) for HDL. This result may suggest that SSGWAS detected a false positive and RHM method may have avoided this problem. Furthermore, on chromosome 15, the suggestive SNP (‘rs1532085') by SSGWAS explained 1.2% of total trait variance. However, the 100-SNP window containing this SNP was found to be highly significant by the RHM method, explaining 2.7% of total trait variance. Comparing these results with the meta-analysis of P-value of 379.14; this SNP explained 4.5% of the trait variance based on its reported allele frequency and estimated effect in the meta-analysis. The current study suggests that the RHM method performs better than the SSGWAS method in capturing the effective variants.We estimated that the heritability explained by the 47 loci detected in the meta-analysis would be 12.2% for HDL using reported MAFs and effects in the meta-analysis data set. In addition, 13 of these SNPs genotyped in the current study would explain 6.4% of the total variance of HDL. Although just one of the 13 SNPs available out of the 47 was significant in the SSGWAS in the current study, these loci together explained 4.4% of total trait variance for HDL. Using the RHM method, regions harboring these 13 SNPs explained 8.5% of the total trait variance for HDL in our data. The results suggest that the RHM method has the potential to explain a larger proportion of variance in traits than the SSGWAS method in the regions for which meta-analysis hits are available in our data set. For associating SNPs reported by meta-analysis that are available in our data set, the MAF in meta-analysis and estimated allele frequency in the current study is plotted in r2 >0.90) with it was genotyped in the current study; therefore, the region was not detected by SSGWAS. This confirms the analysis of For LDL, the most significant region detected by RHM was that harboring the SNP (‘rs4420638') that was most significantly associated in the meta-analysis of The 37 loci detected in meta-analysis explain 12.1% of total trait variance for LDL. Only 6 of the 37 SNPs were genotyped in the current study, and these explained 0.8% of total variance in meta-analysis, 0.8% using the SSGWAS method and 0.6% using the RHM method.No significantly associated SNP or region was detected in the current study for either TC or TG. Three significant regions detected by RHM provide evidence on the power of RHM under three different conditions. First, RHM can detect trait-associated regions that cannot be detected by the SSGWAS method by taking into account the nearby SNP effects. Second, this study suggests that when the SNP is not available in the data set, the RHM method can detect causative regions as this method uses effects of all SNPs in the region through the construction of a regional relationship matrix. Finally, the RHM method may be able to reduce the false discovery rate, a common problem with SSGWAS studies in populations that are characterized by a high inbreeding coefficient .Summary statistics for SSGWAS and summary statistics for Regional Heritability Mapping are available from the Dryad Digital Repository:


Female fertility is important for the maintenance of the production in a dairy cattle herd. Two QTL regions on BTA04 and on BTA13 previously detected in Nordic Holstein (NH) and validated in the Danish Jersey (DJ) and Nordic Red (NR) were investigated further in the present study to further refine the QTL locations. Refined QTL regions were imputed to the full sequence data. The genes in the regions were then studied to ascertain their possible effect on fertility traits.10 was 19.41 at 33,903,159 bp. Two regions were identified: Region 1: 33,900,143-33,908,994 bp and Region 2: 34,051,815-34,056,728 bp. SNPs within and between these two regions were annotated as intergenic.BTA04 was screened for number of inseminations (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the interval from calving to first insemination (ICF) in the range of 38,257,758 to 40,890,784 bp, whereas BTA13 was screened for ICF only in the range from 21,236,959 to 46,150,079 with the HD bovine SNP array for NH, DJ and NR. No markers in the DJ and NR breeds reached significance. By analyzing imputed sequence data the QTL position on BTA04 was narrowed down to two regions in the NH. In these two regions a total of 9 genes were identified. BTA13 was analyzed using sequence data for the NH breed. The highest –logScreening BTA04 and BTA13 for female fertility traits in NH, NR and DJ suggested that the QTL for female fertility were specific for NH. A missense mutation in CD36 showed the strongest association with fertility traits on BTA04. The annotated SNPs on BTA13 were all intergenic variants. It is possible that BTA13 at this stage is poorly annotated such that the associated polymorphisms are located in as-yet undiscovered genes. Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile. Therefore it is not expected that there is a simple explanation with an obvious candidate gene but it is more likely a network of genes and intragenic variants that explain the variation of these traits.The online version of this article (doi:10.1186/1471-2164-15-790) contains supplementary material, which is available to authorized users. The cow´s ability to reproduce is essential for milk production. Impaired reproduction will result in additional inseminations, higher replacement rate and increased culling rate. Recently genome sequencing technologies and bioinformatics analysis approaches have advanced tremendously. The availability of full genome sequence data can help to identify causal mutations underlying variation in female fertility. Application of these tools has led to remarkable increases in the numbers of trait markers available and thus enhanced precision of QTL mapping.Genome-wide association studies (GWAS) examine common genetic variants in large numbers of individuals to determine whether an association with quantitative traits exists. GWAS have identified thousands of single nucleotide polymorphisms (SNPs) across the cattle genome associated with economically important traits in cattle (e.g.). HoweveIn addition to revealing the genetic architecture that underlies the physiological and biological process of female reproduction, this information could also be practically applied to genomic selection schemes. Genomic prediction helps to select breeding animals for the next generation more efficiently. Introducing high density SNP arrays (777 k) did not substantial increase accuracy of genomic predictions in cattle 0.5 to 1%) as compared to medium density 50 k SNP arrays[ to 1% asPreviously QTL areas influencing female fertility traits have been identified in the Nordic Holstein cattle population using the 50 k SNP array. In thisBTA04 and BTA13 were screened with the bovine HD SNP array in the NH, NR and DJ breeds, based on the positions on the genome which were chosen due to high significance from Höglund et al.. BTA04 w10. The highest –log10 were 15.22 for AISC (Chr4:40298743), 16.14 for IFLC (Chr4:39213491), 7.18 for NRRC (Chr4:40728978) and 5.39 for ICF (Chr4:40599222).Based on the results using the HD array Figure : middle 10 was 19.41 at 33,903,159 bp. Based on the analysis using sequence data two regions were identified  (Table http://www.sequenceontology.org/miso/current_release/term/SO:0001583). CD36 has been suggested to be involved in numerous potential physiological functions; cell adhesion processes, binds long chain fatty acids and may function in the transport and/or as a regulator of fatty acid transport and act as receptor for thombospondins, THBS1 AND THBS2, mediating their antiangiogenic effects. This in turn involves many physiological processes[In the QTL region 9 genes were annotated Table . Based o) Table , CD36 isrocesses.http://www.uniprot.org/uniprot/A7MB70). However, the SNP markers located in these genes did not reach genome wide significance for the traits AISC, IFLC, and NRRC . SEMA3C binds to members of the plexin family and plays an important role in the regulation of developmental processes. Furthermore it is required for normal cardiovascular development during embryogenesis and plays an important role in axon growth and axon guidance by functioning as attractant for growing axons . Eight of these genes were in the 33.2 Mb to 34.4 Mb range. The sequence analysis revealed two regions within the 33.2 Mb to 34.4 Mb range. Even though we have used sequence data it is still a challenge to disentangle whether the peaks harbor the same genetic variation influencing ICF, or are in reality a combination of two separate genetic variations. Therefor the two peaks were analyzed further and were determined to be in high LD. This suggests that it is the same genetic variation causing the two peaks. The SNPs annotated in these two regions were all annotated as intergenic variants. It is possible that BTA13 at this stage is poorly annotated such that the associated polymorphisms are located in as-yet undiscovered genes.Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile, another challenge is the way the phenotypes are defined, they do not distinguish between different biological functions as they are defined more for a breeding purpose than from a biological point of view. Therefore it is not expected that there is a simple explanation with an obvious candidate gene, it is more likely a network of genes and intragenic variants that explain the variation of these traits. A number of genes and their potential functions in the vicinity of the two peaks are presented in Table The two regions on BTA4 and BTA13 were chosen based on previous studies by the authors, 8. The 10 due to high LD among themselves. Therefore, it is not possible to pick a few from them based on this analysis.We have been able to narrow down the regions on BTA04 and BTA13 by adding many more markers. However in our analysis we could not with certainty pinpoint the causal polymorphism for the fertility QTL. This might be due to the fact that half of the total genetic variants identified in the whole genome sequencing (WGS) were filtered out for various reasons. All the variants which were not bi-allelic were dropped due to limitations in the imputation software. Therefore, the actual causative polymorphism may be missing from the data analyzed here. Furthermore, there were hundreds of SNPs with very high –logRecently it has been argued that an increase in recessive lethals play a part in the decline of fertility as elite sires obtain a very large number of offspring in cattle breeding, and the effective population size is around 50 in the Holstein breed. The devhttp://www.ncbi.nlm.nih.gov/assembly/GCF_000003055.4/;[http://www.ncbi.nlm.nih.gov/assembly/GCF_000003055.4/), the known set sites of gene transcription, initiation, termination as well as differential splicing remains incomplete. Therefore, information on genomic structure of organisms which are better annotated like mouse and human, are used as an information source to cover these gaps in the knowledge of the genome structure. However, identifying regulatory elements and non-protein coding regions annotation remains more challenging[Tools and resources for annotation and gene discovery in the bovine genome are available . Even tllenging, 9.It is a challenge to identify all regulatory elements in the genome also those which control gene expression. The question is how to relate genes and their products function. Even though we have access to the cattle sequence which facilitates the comprehensive identification of these transcriptional regulatory factors there is still a long way to go. The expressions of eukaryotic protein coding genes are regulated in different steps which include elements like; transcription initiation and elongation, mRNA processing, transport, translation and stability. Most regulation is believed to occur at the level of transcription.Directly measured phenotypes and genotypes of cows are necessary to provide a more direct link between phenotype and genotype, it can also enable mapping of non-additive effects which affect fertility traits in cattle. This waIdentification and analysis of phenotypic measures that reflect more directly the physiologic background of the reproduction traits could also be helpful in determining the precise physiological background represented by a specific QTL. Analyzing expression data from reproductive organs might help to identify the temporal-specific aspects of gene expression.The results from screening BTA04 and BTA13 of NH, NR and DJ using the bovine HD SNP array for female fertility traits suggested that the QTL for female fertility located on these chromosomes were specific for the NH breed. The subsequent screening using the imputed sequence variants for NH narrowed down the QTL on BTA04 into two regions of 288,314 bp and 1.1 Mb respectively, while on BTA13 the QTL region for ICF was narrowed down into two regions of 8,851 bp and 4,913 bp, respectively. A candidate gene search for these QTL regions revealed that a missense mutation in the Platelet glycoprotein 4 (CD36) gene showed the strongest association with fertility traits and therefore is a strong candidate for the QTL on BTA04, whereas the annotated SNPs on BTA13 were all intergenic variants. Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile. Therefore it is not expected that there is a simple explanation with an obvious candidate gene but it is more likely a network of genes and intragenic variants that explain the variation of these traits.Phenotypic data were obtained from routine records of cattle farms. Semen and blood samples for genotyping were collected in previous studies, 16 usinNo data was collected for the purposes of this study. All DNA data were obtained through the analysis of materials collected as part of routine operation of cattle breeding programs. Farm management and breeding programs in the EU and EEA are subject to the “European Convention for the Protection of Animals kept for Farming Purposes” as implemented in national law.The animal population has been described before in Höglund et al.. In shorst parity animals  and 2nd and 3rd parity animals . For details regarding the phenotypes recorded and models used in routine breeding value prediction, seehttp://www.nordicebv.info.The traits evaluated included: number of inseminations per conception (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the length in days of the interval from calving to first insemination (ICF). With exception of ICF, single trait breeding values (STBVs) from the national evaluation were available for both 1The sequences for the reference population used for imputation of Nordic animals consisted of the whole genome sequence carried out at Aarhus University and in the 1,000 Bull Genome project. The seqpers. comm.), the 50 k genotypes  for 12,322 Nordic bulls were imputed to HD genotypes  using the software IMPUTE2[The sires used in this study were genotyped using the 50 k SNP array as described earlier. These 5 IMPUTE2. The ref2 value below 0.9 were removed. This left a total of 16,374,063 sequence markers and 629,028 HD markers for chromosomes 1–29. Chromosomes were divided into chunks of about 20,000 consecutive sequence markers with an overlap of 500 markers to minimize imputation error at ends of the chunks. Imputation was done using BEAGLE v3.3.2[In the second step of imputation, the 12,322 bulls imputed to HD genotypes were further imputed to the full sequence level, using a reference of 242 sequenced dairy bulls . The 242 dairy cattle sequences originated from a combination of sequences processed at Aarhus University and sequences from the 1,000 Bull Genomes dataset. Only polymorphisms identified both in the Aarhus University dataset and the 1,000 Bull Genomes dataset were included. For positions containing both a SNP and an INDEL, the INDEL was deleted. SNPs at positions with disagreements between alleles from sequence and HD data were deleted. The reference data was pre-phased with BEAGLE v3.3.2[The association between the SNP and the phenotype was assessed by a single-locus regression analysis for each SNP separately, using a linear mixed model. The mody is the vector of phenotypes (de-regressed EBV), 1 is a vector of ones with length equal to the number of observations, μ is the general fixed mean, m is a vector of genotypic indicators  associating records to the marker effect, g is a scalar value of the additive fixed allele substitution effect of the SNP, Z is an incidence matrix relating phenotypes to the corresponding random polygenic effect, u is a vector of the random polygenic effect with a multivariate normal distribution NA is the additive relationship matrix ande is a vector of random environmental deviates with a normal distributionW is the diagonal matrix containing weights of the de-regressed estimated breeding values. The weight for the ith animal was estimated as wi = r2/(1 - r2), where r2 was the reliability of the de-regressed EBV of the ith animal and r2 > 0.98 was set to 0.98 to avoid very large weights for sires with very large number of progeny records. The model was fitted by REML using the software DMU[0: g = 0.where ware DMU. The sta-6 in the 50 k data (previous study). It was 0.05/734,077 ~ 6.8 × 10-5 SNPs in the 700 k data and 5.5 × 10-8 in the WGS data. The significance level using region wise bonferoni correction was 0.05/3716 ~ 1.3 × 10-6on BTA04 and on BTA13 0.05/3944 ~ 1.3 × 10-4.The significance level using genome wide bonferoni correction was 0.05/38,545 ~ 1.3 × 10Variants were annotated using Variant Effects Predictor version 2.8. The undTo distinguish whether the two peaks in the sequence analysis on BTA13 were due to two separate mutations or one mutation the linkage disequilibrium of BTA13 was examined for these peaks. The top 100 markers in each peak were examined using HAPLOVIEW.ftp://ftp.ensembl.org/pub/release-73/fasta/bos_taurus/dna). All variations used in the mapping study have been submitted by the 1000 Bull Genomes project for inclusion in dbSNP (http://www.ncbi.nlm.nih.gov/SNP). All annotations were obtained from a publicly available source (http://www.ensembl.org) downloadable including through Variant Effect Predictor (http://www.ensembl.org/info/docs/tools/vep/script/index.html).All DNA sequences used were taken from a publicly available assembly. The assembly is available for download , 56-day non-return rate (NRR), days from first to last insemination (IFL), and the length in days of the interval from calving to first insemination (ICF) using the bovine HD SNP array.(TXT 148 KB)Additional file 1:Results of BTA13 in the Danish Holstein breed for the length in days of the interval from calving to first insemination (ICF) using the bovine HD SNP array.(TXT 242 KB)Additional file 2:Results of the single-marker analysis of BTA04 in the Danish Holstein breed for number of inseminations per conception (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the length in days of the interval from calving to first insemination (ICF) using sequence data. Only significant P values are mentioned for each trait. NA: marker not significant. (TXT 70 KB)Additional file 3:Results of the single-marker analysis of BTA13 in the Danish Holstein breed for the length in days of the interval from calving to first insemination (ICF) using sequence data. Only region-wide significant P-values are presented. (TXT 91 KB)Additional file 4:Manhattan plots of the GWAS results using imputed DNA sequence on BTA13 for ICF in the range of 33.2 Mb to 34.4 Mb. The y axes show –log10 of single-marker associations tests. The x axes show marker positions in base pairs (bp). The blue dots indicate test results using sequence data. The red triangles on the x-axis indicate the position of the annotated genes in the region. (PDF 26 KB)Additional file 5:


Plasmodium vivax, one of the five species of Plasmodium parasites that cause human malaria, is responsible for 25–40% of malaria cases worldwide. Malaria global elimination efforts will benefit from accurate and effective genotyping tools that will provide insight into the population genetics and diversity of this parasite. The recent sequencing of P. vivax isolates from South America, Africa, and Asia presents a new opportunity by uncovering thousands of novel single nucleotide polymorphisms (SNPs). Genotyping a selection of these SNPs provides a robust, low-cost method of identifying parasite infections through their unique genetic signature or barcode. Based on our experience in generating a SNP barcode for P. falciparum using High Resolution Melting (HRM), we have developed a similar tool for P. vivax. We selected globally polymorphic SNPs from available P. vivax genome sequence data that were located in putatively selectively neutral sites . From these candidate SNPs we defined a barcode consisting of 42 SNPs. We analyzed the performance of the 42-SNP barcode on 87 P. vivax clinical samples from parasite populations in South America , Africa (Ethiopia) and Asia (Sri Lanka). We found that the P. vivax barcode is robust, as it requires only a small quantity of DNA (limit of detection 0.3 ng/μl) to yield reproducible genotype calls, and detects polymorphic genotypes with high sensitivity. The markers are informative across all clinical samples evaluated . Population genetic and statistical analyses show the barcode captures high degrees of population diversity and differentiates geographically distinct populations. Our 42-SNP barcode provides a robust, informative, and standardized genetic marker set that accurately identifies a genomic signature for P. vivax infections. Plasmodium vivax malaria is a major global public health problem, with nearly 2.5 billion people at risk for infection and approximately 132–391 million clinical infections annually. It has a wide geographical range, with a high disease burden in Asia, Central and South America, the Middle East, Oceania, and East Africa. Advances in sequencing technology and sample processing have made it possible to characterize the genetic diversity of P. vivax populations. This genetic variation provides a means to identify parasites by unique genetic signatures, or “barcodes.” We developed such a genetic barcode for P. vivax, composed of 42 robust and informative variants. Here we report its development and validation based on 87 clinical samples identified by microscopy to contain P. vivax from geographically diverse parasite populations from South America , Africa (Ethiopia) and Asia (Sri Lanka). We show that the SNP barcode provides a genotyping tool that can be performed at low cost, providing a means to uniquely identify parasite infections and distinguish geographic origins, and that barcode data may offer new insights into P. vivax population structure and diversity. P. vivax is a significant disease threat and the most widely distributed human malaria parasite ) on that chromosome. Genomic information for each SNP type  the Gene ID where the indicated SNP is located, and the reference and alternate alleles.(XLSX)Click here for additional data file.S2 Tablem (°C) for the reference and alternate allele.The forward and reverse primer sequences are listed with their corresponding assay concentration and T(XLSX)Click here for additional data file.S3 TableThe PCR efficiency and the detection limit of both the alternate and reference allele is listed for each assay in the 42-SNP barcode. All assays were performed in duplicate.(XLSX)Click here for additional data file.S4 TableThe limit of detection for both the alternate and reference allele in mixed genome samples is listed each assay in the 42-SNP barcode. All assays were performed in duplicate.(XLSX)Click here for additional data file.S5 Tablem (°C) value and standard deviation (SD) for all monogenomic calls in duplicate is listed. All genotype calls (504 out of 504) were concordant across duplicates and on both genotyping platforms. Note, (*) indicates one clinical sample that was polygenomic and was excluded from the study, CTL refers to control sample, CS refers to clinical samples: B to Brazil I, I to India VII, and NK to the North Korea P. vivax strains. The column labeled number of replicates shows the number (including duplicates) in the study as well the corresponding number of clinical samples and controls.The 42 assays were screened using three clinical samples and three sequenced controls. The assays were run in duplicate on two different genotyping platforms: the PCRmax Eco (48-well format) and the Applied Biosystems ViiA 7 (384-well format) Real-Time PCR Systems. The mean T(XLSX)Click here for additional data file.S6 Tablem (°C) values and standard deviation (SD) is listed for 87 clinical samples  and control samples. All assays were performed in duplicate. Note, CTL refers to control sample, CS refers to clinical samples, B to Brazil I, I to India VII, and NK to the North Korea P. vivax strain. The column labeled number of replicates shows the number (including duplicates) in the study as well the corresponding number of clinical samples and controls.The mean T(XLSX)Click here for additional data file.S7 TableThe 42 SNPs captured high degrees of diversity for the four distinct geographic populations: Brazil, Sri Lanka, Ethiopia, and French Guiana, with the average minor allele frequency (AMAF) value for each SNP > 0.1. The minor allele frequency (MAF) for each assay across the four populations is also listed. All assays were performed in duplicate.(XLSX)Click here for additional data file.S1 FileThe barcode assay protocol is described in detail: including HRM method, supplies, and primer sequences.(DOCX)Click here for additional data file.