Abstract
Bovine respiratory disease is a leading health problem in feedlot cattle. Identification of affecting genes is essential for selection for decrease sensitivity. Chromosome X is a special attractive target for gene mapping in light of reports on both sexual dimorphism in immunity and higher susceptibility of males to this disease. However, diagnosis is challenging and clinical signs often go undetected. Kosher scoring of lung adhesions was used as a cost-effective proxy diagnosis. Selective DNA pooling was applied for cost-effective mapping of regions associated with sensitivity to the disease on chromosome X in Israeli Holstein male calves. A total of 9 regions were found, more than twice of any of the autosomes. All regions overlapped or were very close to previously reported regions. Bioinformatics survey found candidate-by-location genes in these regions. Functional analyses identified candidates-by-function among these genes. Network analyses connected the genes and found possible relations of the genes and the networks with morbidity, and specifically with sensitivity to bovine respiratory disease. The relatively large number of affecting regions and the candidate genes on the sex chromosome may explain part of the higher susceptibility of males and provide genomic and management targets for mitigating this disease.
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Introduction
Sexual dimorphism in immunity is widely reported in the literature1,2,3. Although in some systems females happen to be more susceptible to infection4, in many vertebrates, females have higher immunocompetence than males5. Indeed, various studies indicate that males suffer more severe symptoms than females6, exhibit higher susceptibility7, and diminished ability to cope with infections8.
Different factors have been shown to contribute, independently, to the sex-based disparity in immune responses. Hormonal mediators such as testosterone and estrogen were reported to be immunosuppressive substances and modulators of differentiation, maturation and lifespan in various innate immune cell lineages9, with fundamental differences in male and female life histories10. In addition, the higher susceptibility to infections observed in the heterogametic males from birth to adulthood, suggests that sex chromosomes have also a major role in sexual dimorphism of the innate immunity.
Innate immunity, the first line of defense against pathogens, also plays a fundamental role in the activation, regulation, and orientation of the adaptive immune response9. Interestingly, several genes encoding innate immune molecules are located on the X chromosome (e.g2), and this may have significant differential consequences on their expression in both genders. All the more so, in spite of the random X chromosomes inactivation in females11,12, which should assure an equal gene expression with males, about 15% of the X-linked genes escape inactivation (e.g2,12), and 10% have variable degrees of inactivation, that may promote overexpression of some X-linked genes in females13. Consequently, if innate immune genes are affected by this silencing escape, it may explain some of the immunological genetic advantage in improved resistance to infectious inflammatory diseases of females over males.
Respiratory diseases provide specific examples for the inferior immunological capability of human males compared to females to combat infectious diseases2. It is now acceptable that this difference is likely a consequence of excessive and damaging inflammatory response, rather than microbial burden within host tissues. To this end, Toll-Like Receptors (TLRs) and other X-linked innate immune genes potentially implicate this sex bias2,14.
Bovine respiratory disease (BRD) is the most economically impacting infectious feedlot disease15,16,17,18. Immature functionality of the respiratory system causes higher and more severe morbidity in young cattle19. Various strategies have been used in farm practices to prevent or minimize the BRD impact in feedlots, but so far, the approaches were unsuccessful20.
As in humans, previous studies repeatedly reported that bovine males too were more sensitive than females to BRD21,22,23,24,25,26. Thus, it is reasonable to assume that the bovine chromosome X (BTX) may be involved in differential vulnerability to BRD.
In spite of intensive research over years, diagnosis of BRD is still a major challenge. To date, detecting morbid cattle is based on visual signs, thus depending on the stage, severity of symptoms and extent of the disease. As a result, diagnostic is subjective and clinical signs of BRD are often undetected27. To overcome this obstacle, we reported previously that kosher slaughtering, involving a close detailed examination of the lungs for adhesions, provides a proxy objective classification of individuals experienced BRD episodes earlier in life28. Lungs completely clear of adhesions are designated “Glatt” kosher (GK), indicating a BRD free animal (and thus, the control group); severe pulmonary adhesions indicate severe BRD, determining a classification of inspected animals as “non-kosher” (NK) (and thus, the case group)29. This strategy offers a low-cost BRD phenotyping.
Quantitative traits are polygenic, affected by relatively large number of genomic elements distributed throughout the genome, and to a large degree by the environment30. Regions that harbor genomic elements affecting quantitative traits were termed Quantitative Trait Luci, QTL30. Mapping QTL regions (QTLRs), is a vital step toward identification of the genomic elements affecting a trait, and for a genetic selection to improve that trait30.
Mapping QTLRs could be a laborious and expensive operation. One way to save cost and increase statistical power is the design of Selective DNA Pooling (SDP), where the study sample size is saved by using only individuals from the extreme phenotypic tails of the population, and the number of genotypes is saved by pooling the DNA of those individuals30.
BRD is a quantitative trait. Previously, we successfully used kosher phenotyping and SDP to map QTLRs affecting BRD on the bovine autosomes28.
Given the potential importance of chromosome X in the observed sexual dimorphism of BRD sensitivity, the goal of the present study was to efficiently map BRD QTLRs on BTX using cost effective kosher phenotypes as proxy of BRD morbidity, and SDP design as a way to save individuals and genotypes, and to identify genes harbored in the QTLRs that may be related to the disease sensitivity.
Results
Single marker tests and QTLRs mapping
Affected, case none-kosher (NK) were compared to control, BRD free, Glatt kosher (GK) individual male calves. SDP was used for a cost-effective mapping of BRD QTLRs on BTX. A total of 894 markers had PFP ≤ 0.4 (Table 1), distributed over the entire X chromosome (Fig. 1), suggesting the presence of numerous QTLRs located throughout BTX. Nine QTLRs were identified (Table 2; Fig. 1), averaged 1.64 Mb in size, more than 6 times larger than the average of 0.26 Mb found on the autosomes28. Likewise, the range of sizes of the BTX QTLRs was 0.13 to 4.06 Mb, much wider range than the 0.12–0.48 Mb found on the autosomes. The QTLRs covered a total of 10.37 Mb, 7% of BTX (ARS-UCD1.2).
Results of marker tests on BTX. Blue diamonds, -Log10(P) values of the markers; black curve, moving average of -Log10(P) values of windows of 23 markers (≈ 100 Kb genome wide, autosomes and BTX). QTLRs are presented by black horizontal lines and numbers on the upper side of the chart. Three horizontal bars from top down: significance thresholds for individual markers at PFP of 0.05, 0.10 and 0.20, respectively; red horizontal line on the upper right side of the chart, pseudo-autosomal region12. Locations are on ARS-UCD1.2 build.
SNPs and genes in the QTLRs
The 9 QTLRs included a total of 713 SNPs (Table S1). Only three SNPs were in a coding sequence, 1 synonymous and 2 missense variants. Thus, a priori, most of the genotyped SNPs are expected to be neutral, just markers, without any direct causal effect on BRD expression.
Following Lipkin et al.28, to account for the limit power of the mapping design, genes were annotated for each QTLR ± 0.5 Mb. A total of 83 annotated genes were found, including 76 protein coding genes, 6 tRNAs and 1 miRNA (Table 2, Table S2). Over the sum of the QTLR length ± its 0.5 Mb flanks, the distances between these genes and the top window – the window with the highest value of mAvg (moving average of the markers’ -Log10P), distributed from 0.002 Mb (the gene DYNLT3 in QTLR4) to 2.89 Mb (the gene PNPLA4 in QTLR9). The genes closest to the top window are bolded in Table 2 as top candidates-by-location to be the causative quantitative gene (QTG) and are detailed below.
Genes’ networks
Out the 83 QTLR genes, 59 were recognized and annotated by DAVID database (Table S3). These genes belong to 41 GO terms in 7 categories. Four terms in 3 categories were significant (P ≤ 0.05), namely spermatogenesis, intracellular signal transduction, metal ion binding and oocyte meiosis (Table 3).
Of the genes recognized by DAVID, 49 (83%) appeared in more than one term (Table 4). For example, the gene AR (Androgen Receptor; Table S2) appeared in 12 terms. The 19 genes in the significant terms appeared in almost as twice terms than the 40 genes in the none-significant terms, averages of 4.9 compare to 2.9 terms, respectively. The difference was significant by t-test (P = 0.007). Thus, these genes are indeed candidates that may have multiple significant involvements in the sensitivity to BRD.
Comparisons with previous studies
Seven of the QTLRs (26 genes) found herein overlapped or were within 3 Mb from previous reports31,32,33,34,35 (Table 5). Among Neibergs et al.20 top 30 SNPs for GBLUP analyses (their Supplementary Table 3), 7 were on BTX; among the 41 SNPs that achieved genome-wide FDR ≤ 0.05 for lung lesion severity, Keele et al.32 found 4 on BTX. Tizioto et al.33 found a total of 212 BTX genes differentially expressed in response to BRD pathogens. Vukasinovic et al.34 identified 13 genes within QTLRs 6 and 7 found here, suggesting their potential involvement in the respiratory health of animals.
Keele et al.32 identified 85 genome-wide significant SNPs, 8 of which mapped on BTX. Notably, one of these SNPs, rs135502881, falls within QTLR 4.
The BMX gene, located within QTLR 8, was recognized as a gene with differential expression between animals with and without BRD both in the studies conducted by Li et al.35 and Tizioto et al.33. Green et al.31 identified differentially expressed genes associated with BRD that are located within 7 of the QTLRs identified here (Table 5).
QTLR gene closest to the top window
The approach of Lipkin et al.28 resulted in high-resolution mapping, allowing thorough bioinformatic analysis to identify candidate genes in the proximity of those QTLRs. We assumed that the gene closest to the top window of each QTLR (bolded in Table 2), is a good candidate-by-location to be the QTG. Hence, for each QTLR that closest gene was identified and detailed blow (along with other QRLR genes).
QTLR 1 The top window was located at bp 42,151,422 (Table 2), within the gene FAM133A (Family with Sequence Similarity 133 Member A; Table S2). In Human, this gene was associated with the bacterial infectious diseases (e.g36). FAM133A (alias CT115) harbors one of the 2 missense SNP genotyped in all QTLRs, namely SNP rs136444760 located bp 42,168,537 (Table S1). This SNP is a nucleotide substitution of Tgg/Cgg, causing a substitution between the hydrophobic amino acid (AA) Tryptophan and the positive charged hydrophilic AA Arginine. Thus, FAM133A could be a fine candidate gene by location near the top window. Furthermore, it is a fine candidate by function by involvement in infectious diseases and by a functional polymorphism. However, that missense SNP rs136444760, though a part of the top window, was not significant itself (p = 4.4E-01). In fact, the window top marker was intergenic (SNP rs137146544 at 42,270,130 bp, p = 4.4E-09; Table 2). Obviously further studies are needed to identify the causative element in QTLR 1.
QTLR 2 The closest gene to the top window was HDX (Highly Divergent Homeobox; Table S2). Homeobox genes play a fundamental role in the morphogenesis of the embryo, regulate gene expression and control morphogenesis differentiation37. In this manner, they can affect organs’ maturity, which in the case of the respiratory system may cause higher and more severe morbidity from BRD in young cattle19. Genetic selection for high productivity has produced cattle with small cardiopulmonary systems relative to metabolic O2 requirements38. Thus, BRD episodes, which cause pulmonary lesions and adhesions, may further limit the uptake of O2 from the alveoli into the pulmonary circulation39. In fact, genetic selection of cattle for traits related to pulmonary functionality (i.e., efficacious alveolar-arterial O2 uptake) may reduce the deleterious effect of BRD on calf growth.
QTLR 3 The top window was located at bp 82,576,433, within the gene OPHN1 (Oligophrenin 1; Table S2), encodes a Rho-GTPase-activating protein, important for intracellular signal transduction and cell migration and cell morphogenesis. In human, this gene has been associated with chronic obstructive pulmonary disease40.
QTLR 4 DYNLT3 (Dynein Light Chain Tctex-Type 3) was the closest to the top window. The gene encodes protein homodimerizes and is a component of the cytoplasmic dynein motor protein complex. In human, this gene was associated with various diseases (e.g41).
QTLR 5 LOC529626 (MAGEB10: melanoma-associated antigen B10) was the only gene found here, even this only in the upstream region of the QTLR (Table 2), thus inevitably the closest gene to the top window. In human, this gene was linked to Huntington-Like Neurodegenerative Disorder 1 and Melanoma (e.g42).
QTLR 6 The closest gene to the top window was ARX (Aristaless Related Homeobox). As mentioned above, Homeobox genes play a fundamental role in the embryo morphogenesis, gene expression and morphogenesis differentiation.
In addition, a missense variant (rs136972567) mapped in the PCYT1B gene in QTLR 6 (Table S1). This SNP is a nucleotide substitution (cTg/cCg) causing a Leucine/Proline substitution. Both amino acids are non-polar and hydrophobic. Thus, the effect of the substitution is not expected to be substantial. Indeed, the SNP was not significant (p = 3.3E-01). PCYT1B gene was linked mainly to reproduction traits43.
QTLR 7 The top window was located at bp 121,935,404, within the gene CNKSR2 (Connector Enhancer Of Kinase Suppressor Of Ras 2). The gene was related to neuronal diseases in human (e.g44).
QTLR 8 The top window was located at bp 127,870,867, within the gene CLTRN (Collectrin, Amino Acid Transport Regulator (transmembrane Protein 27). In human the gene was associated with metabolic, nephrological, neuronal, and skin diseases45.
Next to CLTRN (23,120 bp downstream), locate the most interesting gene ACE2 (Table S2), interacting with CLTRN (Fig. 2 and below). In human, ACE2 (Angiotensin Converting Enzyme 2), was linked to various respiratory diseases, including Covid-19, Severe Acute Respiratory Syndrome (SARS), Influenza, and Long Covid (e.g46), ACE2 is highly expressed in the pulmonary vascular endothelium, also in cattle47. It is considered one of the most relevant regulators of the renin-angiotensin system (RAS) and plays an important role in body homeostasis, through the blood pressure regulation and electrolyte balance48. Indeed, 3 of its markers were highly significant: rs41575887 (127,928,903 bp, p = 2.8E-06), rs132708238 (127,934,719 bp, p = 6.6E-10), and rs136864039 (127,944,097 bp, p = 1.5E-06). Thus, ACE2 could be a prime candidate gene by location, function and association. However, all its markers were intronic (Table S1), thus less likely to be the causative nucleotide (QTN).
ACE2 Gene networks. Genes interacting with ACE2 found by Genemania tool implemented in Cytoscape. (a) QTLR genes interact with ACE2 directly; (b) co-expressed genes found in the entire Cytoscape database (literature in Cytoscape database, Homo sapiens species as a background); black full circles, QTLR genes identified in the present study; gray full circles, genes identified in other studies; green empty circled around the gene, associated with immunity; red empty circled, associated with fertility; numbers near the circles, QTLRs serial numbers (Table 2). References are provided in Table S4.
Using Genemania tool49 implemented in Cytoscape50, a network of 31 genes interacting or co-expressed with ACE2 was found (Fig. 2a and b, respectively). No less than 18 of these genes were identified in the present study (black full circles in Fig. 2b), distributing over all QTLRs but QTLR 5 (note that only one gene was found in this QTLR (Table 2), and even this was mapped in the upstream flank). Interestingly, 6 of the 18 QTLR genes shared QTLR 8 with ACE2. Thirteen other co-expressed genes were found in other studies (in gray full circles). All co-expressed genes are primarily associated with immunity (circled in green in Fig. 2) and fertility (circled in red).
QTLR 9 The closest gene to the top window was PNPLA4 (Patatin Like Phospholipase Domain Containing 4). In human, this gene was related to various diseases, including Cardiomyopathy, Hypogonadism with Anosmia and Ichthyosis51.
Cross QTLRs networks on BTX with possible relation to BRD
Numerous databases and analyses were employed to functionally categorize genes in and around the QTLRs. ClueGo gene network was used to identify possible cross QTLRs relations between genes, that might highlight genes on BTX with apparent relevance to BRD. Three significant networks were found, characterized by distinct annotation ontologies (Fig. 3). CORUM, Comprehensive resource of mammalian protein complexes52, found significant network comprises of genes associated with functional categories such as cellular import, vesicular cellular import, endocytosis and animal development (Fig. 3a). KEGG database53 found significant genes network associated with oocyte meiosis (Fig. 3b). Finally, Wiki-Pathway, an open-source biological pathway database, identified significant genes network related to amino acid metabolism (Fig. 3c).
To this end, a total of 10 genes (some of which are detailed below), distributed over 4 QTLRs (QTLRs 3, 4, 7, 8), were found in the 3 networks:
-
i
Vascular endothelial growth factor-D (VEGFD) is a secreted protein that can promote the remodeling of blood vessels and lymphatics in development and disease54. The VEGFD gene (Fig. 3a), has been implicated in humans with various pulmonary disorders, including lung lymphangioleiomyomatosis (LAM)55, pulmonary vasculopathy and pulmonary edema56.
-
ii
The SH3KBP1 gene (Fig. 3a), is important for the proper signaling of the T- and B-cell receptors (TCR and BCR, respectively). Using the theoretical protein–RNA recognition code, it has been suggested that SH3KBP1 can be repressed by the SARS-CoV-2 envelope protein, which in turn leads to enhanced TCR signaling and diminished BCR signaling and B-cell activation57.
-
iii
Due to the contribution of structural differences of the lung between the sexes, neonate males are at major risk for the development of respiratory distress syndrome and bronchopulmonary dysplasia58,59. However, sex differences in lung diseases are also dependent on the action of sex hormones, which activity is mediated by binding to androgen receptor (AR)60. In the lungs, many immune cells express ARs and are responsive to androgens, and their immunoregulatory properties appear to be dependent also on the amount of cellular expression of AR. Interestingly, the AR gene, identified herein in association with oocyte meiosis (KEGG sub-network; Fig. 3b), was considered a modulator of the inflammatory response during acute wound healing61. This activity was also highlighted in our previous study, mapping QTLs on autosomes that affect susceptibility to BRD28.
-
iv
Pyruvate dehydrogenase E1 component subunit alpha (PDHA1), a critical component of a pyruvate dehydrogenase complex, catalyzes the overall conversion of pyruvate to acetyl-CoA and CO2, and thereby links the glycolytic pathway to the tricarboxylic acid cycle62. In addition, PDHA1 gene, associated in the Wiki-Pathway subnetwork with amino acid metabolism (Fig. 3c), has been found to over-express in different type of cancers, including lung cancer63. In the mouse model of pulmonary fibrosis, PDHA1 was among the genes related to cuproptosis promotion, as it was gradually downregulated in the process of fibroblast differentiation from resting fibroblast to myofibroblast64.
Discussion
Numerous past studies reported sexual dimorphism in immunity, usually with female’s higher immunocompetence and higher sensitivity of males to diseases. Indeed, there are repeated reports that male have higher BRD morbidity than females. Thus, BTX is a special attractive target to map genomic elements affecting BRD morbidity. In fact, several genes encoding innate immune molecules are located on BTX. However, reports on BRD QTLRs on BTX are scarce.
In the current study, the approach of Lipkin et al.28 was used to efficiently explore possible BRD QTLRs located on BTX. Given the limit number of animals used, and given that only males were used, the results of this study should be taken with caution. Nevertheless, the results concord well with previous studies and with the known gender dimorphism of the disease. Furthermore, the finding of genes candidates by location, by function and by networks, strongly verify these present results. Yet another verification is that more than three-quarters of the mapped QTLRs (7/9) were in the proximity of previous studies, who used a various of different methods and population.
A total of 9 QTLRs were found, distributed throughout the chromosome. This is the largest number of QTLRs we found among all chromosomes, more than twice of what was found on the autosomes. The next largest number of QTLRs on a single chromosome in our study was only 4 QTLRs on BTA228. The difference between BTX and the autosomes could be merely a result of sampling variation. A possible alternative explanation would be that this study was performed on males only, where all genes but of the pseudo autosomal region are phenotypically exposed.
Combining kosher slaughtering, SDP, moving average and Log drop 1.0 resulted in high-resolution QTLR mapping, allowing thorough bioinformatic analysis to identify candidate genes in the proximity of those QTLRs. Obviously, but certainly not necessarily, the gene closest to the top window of each QTLR is an obvious good candidate-by-location to be the QTG. Hence, for each QTLR that closest gene was identified. Indeed, most of the closest genes had functional relation to respiratory disease, making them good candidates by both location and function to affect BRD sensitivity.
The cross QTLRs networks analysis unraveled genes linked to respiratory failure or implicated in the immune system response, and thus might be considered as candidates to affect BRD. The field of “Immunological Infertility” is rapidly expanding in both basic and clinical research, underlying the strict connection between immunity and fertility. The link between them has been already highlighted in several studies, both in humans and cattle. The reproductive hormones play a role in the development and modulation of the immune system65, and the immune status plays a crucial role in influencing reproductive health66. Indeed, there are evidence demonstrating the negative impact of certain infections on fertility. As much as 20% of unexplained infertility in both women and men may be ascribed to immune dysfunction67. Moreover, it has been evidenced that SARS-CoV-2 virus infection, mediated by the ACE2 receptor, exerts a negative impact on fertility, especially in males53. These studies support the interconnection of these two classes of functional traits, linked through both, direct interaction or mutual co-expression.
Thus, the network analyses strongly support the association of the mapped QTLRs to BRD.
Conclusions
A highly efficient experimental design was employed. Kosher scoring allowed cost effective BRD diagnosis. Selective DNA pooling allowed cost effective genotyping. Bioinformatics survey found candidate-by-location genes in the 9 QTLRs. Functional analyses identified candidates-by-function among these genes. Network analyses connected QTLRs genes and found possible relations of the genes and the networks with morbidity and specifically with BRD. These results validate the mapped QTLRs and present prime BRD candidate genes. Further study is needed to identify the actual causative quantitative trait genes (QTGs) and their causative nucleotides (QTNs).
Methods
Samples and genotyping
The same animals and pools used to scan the autosomes by Lipkin et al.28, were used here to map BRD QTLRs on the BTX, using the same SDP design68. Briefly, blood samples of male Holstein calve were collected immediately after kosher slaughter from a commercial slaughterhouse (Adom Adom abattoir, Israel). Based on their kosher classification, 122 male claves were used to construct 5 case pools of GK individuals representing BRD free animals (21 to 28 heads in a pool), and 62 males to construct the 2 control pools of NK individuals representing affected animals (31 heads each pool).
Technical and chip replicates were built according to Strillacci et al.69. Briefly, the pooled DNA samples were each genotyped in two duplicates, on two independent microarrays for a total of 14 microarray positions, using the Illumina BovineHD BeadChip (14,278 SNPs on BTX). The genomic position of SNPs was according to the bovine ARS-UCD1.2 genome assembly. Frequency in the pools of the allele defined by Illumina as B (pB) was obtained by Illumina software.
Single marker tests and QTLRs mapping
Frequencies of the two SNP alleles in the pools, frequency differences (D) of allele B between groups of cases (NK) and controls (GK), empirical estimate of the standard error of D, p-values of the association test between the marker and the disease, PFP correction for multiple tests, QTL identification and QTL region (QTLR) boundaries, were all obtained as in Lipkin et al.28. Based on the frequencies of the B allele as obtained by Illumina software, a single marker trait association test was then carried out, where the p-value is twice the area of the standard normal curve to the right of Z = D/SE(D). As in Lipkin et al.28, SE(D) was calculated, assuming under the null hypothesis of no QTL effect, sampling variance among individual pools across tails is the same as the sampling variance among individual pools within tails (see details in Lipkin et al.28).
Mixes of highly significant and non-significant markers in the same area are a typical result of GWAS. Nevertheless, clusters of significant markers are clearly seen in certain chromosomal regions [Lipkin et al.28 and Fig. 1 here]. We take these clusters as putative QTLRs. To account for the mixed markers, instead of using a single significant marker to declare a QTL, a moving average of the markers’ -Log10P (mAvg) was employed. As on the autosomes28, window size of about 100 Kb was used to calculate the SNP window moving average (mAvg). Given the genome average markers density (autosomes and BTX together), this window comprised 23 markers. As by Lipkin et al.28, a mAvg ≥ 2.0 was used to identify QTLs. Then boundaries of the QTLR were identified by a Log drop of 1, starting from the top window (that is, after the top window was identified, windows of both sides were scored, until the mAvg value was 2 below the value of the Top window).
Bioinformatics
SNPs and genes in the QTLRs
The Variant Effect Predictor (VEP) tool implemented in Ensembl online database (http://www.ensembl.org/Bos_taurus/Tools/VEP) was employed to annotate all the QTLRs’ SNPs. SNPs position in a gene were verified using the link to NCBI (dbSNP) available within the Ensembl online database. The full gene set (Bos taurus: Annotation Release 105) was downloaded from NCBI (https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Bos_taurus/105/), and genes were catalogued within and near the 9 QTLR.
Genes’ networks
A gene ontology (GO) functional annotation and KEGG pathway53 analyses using the DAVID Bioinformatics Resources software version DAVID 2021 (Dec. 2021) (https://david.ncifcrf.gov/tools.jsp) was performed. NCBI and GeneCards (http://www.genecards.org) databases were further used to identify candidate genes according to their function. Following Lipkin et al.28, we choose somewhat arbitrary flanks of ± 0.5 Mb to annotate genes for each QTLR.
The Genemania package of Cytoscape 3.10.150 was employed to construct gene interaction networks, to identify functional association within the candidate genes. The Cytoscape plugin ClueGo70, was employed to identify potential biological connections among candidate genes. The network construction relied on information from GO and KEGG database53. These analyses were conducted based on the human-related data, with consideration given exclusively to cattle orthologous genes in the generated networks.
Data availability
All data generated or analysed during this study are included in this published article [and its supplementary information files].
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Acknowledgements
We thank Dr. Wassim Geraisy, the Chief Veterinarian in “Adom Adom” abattoir for his collaboration, which enabled the collection of kosher phenotypes.
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A.S. conceived the idea and initiated the project. E.L., M.G.S., C.F., H.E. and M.Y. did the actual work and analyses. A.B. was responsible for the genotyping, bioinformatics, and overall direction. M.S. and E.L. were responsible for the statistical analyses. A.S., E.L., M.G.S., C.F., M.C-Z, A.B. and M.S contributed to writing.
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This study used data of animals reared in commercial herds and culled in a commercial slaughterhouse. Hence, no ethical approval was needed.
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Lipkin, E., Strillacci, M.G., Cohen-Zinder, M. et al. Mapping genomic regions affecting sensitivity to bovine respiratory disease on chromosome X using selective DNA pooling. Sci Rep 15, 4556 (2025). https://doi.org/10.1038/s41598-025-89020-1
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DOI: https://doi.org/10.1038/s41598-025-89020-1
