Genome-Wide Association Studies for Immunoglobulins in Colostrum and Serum in Chinese Holstein

doi:10.21203/rs.3.rs-40831/v1

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Genome-Wide Association Studies for Immunoglobulins in Colostrum and Serum in Chinese Holstein

https://doi.org/10.21203/rs.3.rs-40831/v1

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posted 10 Jul, 2020

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Background

Immunoglobulins (Igs) are important components of the innate immune system, and fight pathogens as a part of the first defense line. Newborn dairy calves get maternal antibodies from colostrum. Therefore, contents of immunoglobulins in colostrum and serum of cows are essential traits when estimating potential natural disease resistance of calves. In this study, a genome-wide association study (GWAS) was performed to identify candidate genes that are responsible for the observed genetic variation of immunoglobulins contents in colostrum and blood in Holstein cows.

Results

Colostrum, blood and hair follicle samples were collected from the 620 Chinese Holstein cows within 24 hours after calving. The concentration of IgG, IgG1, IgG2, IgA and IgM in both colostrum and serum were detected via ELISA methods, respectively. Using GCTA software, GWASs were performed with 88,934 SNPs genotyped by using Illumina 50K (54,609 SNPs) and GeneSeek 150K (140,668 SNPs) chips in which 50K chip were imputed to 150K SNPs with BEAGLE 3.0.4 software. As a result, 20 and 5 SNPs were detected genome-wide significantly associated with contents of the IgG and IgM in colostrum and serum (P<3.16E–6). In addition, 57, 11 and 10 SNPs were suggestive significantly associated with IgG, IgA and IgM traits (P<6.32E–5). Next, a total of 1,083 functional genes were identified that included or adjacent to these significant SNPs with a distance less than 1 Mb. Functional enrichment analysis showed that these genes were involved in immune related pathways, such as immune response, Fc gamma R-mediated phagocytosis, negative regulation of immunoglobulin secretion, humoral immune response, Fc-epsilon receptor and NF-kappaB signaling pathways. By integrating analysis of the functional enrichment and the known QTL data, we identified 21 candidate genes associated with contents of immunoglobulins in colostrum and serum, including ABR, TIMM22, CRK, MYO1C, RILP, SERPINF2, AKT1, BCL11B, HHIPL1, DYNC1H1, HSP90AA1, TRAF3, KLC1, IL6, PYCARD, ITGAM, TGFB1I1, GUSB, CRCP, RABGEF1 and SBDS.

Conclusions

In this study, we identified 21 candidate genes associated with immunoglobulins level in colostrum and serum in dairy cattle. This founding demonstrated the possibility of increasing immunity through selective breeding and provided an important information for molecular breeding of dairy cattle.

Animal Science

Biotechnology and Bioengineering

Genome-wide-association study

immunoglobulins

SNP

Immune capacity

Chinese Holstein

Maternal antibodies, especially immunoglobulin (Ig) G, are critical to early survival of domestic animals[1]. In dairy cattle, it is important for calves to gain maternal antibodies in the first 24 hours after born because the autoimmune system of newborn calves is too weak to resist various diseases.

According to the method for transmission of immunoglobulin from mother to young , mammals are divided into three categories, including only in utero (human and rabbit), only after birth (pig, horse, sheep and cow), or by both mechanisms (dog, rats and mice)[2]. Cow serum and lacteal secretions contain three major classes of immunoglobulin antibodies: IgG, IgM and IgA. In a newborn calf, the IgG, IgM and IgA are absorbed from the colostrum into the circulation within 24-36 h after birth via a non-selective macromolecular transport system [3].

IgG molecule in dairy cattle occurs predominantly in two subclasses: IgG1 and IgG2. In the colostrum of cow which is defined as the secretion from the mammary gland during the first 24 h after calving, immunoglobulins make up 70–80% of the total protein content, whereas in mature milk, immunoglobulins account for only 1–2% of the protein [4, 5]. IgG1 comprises over 75 % (46.4 mg/ml) of the immunoglobulin antibodies in colostral whey, followed by IgM (6.8 mg/ml), IgA (5.4 mg/ml) and IgG2 (2.9 mg/ml) [4]. Immunoglobulins are components of the innate immune system that can be found in animals without prior exposure to antigens, which have broad specificity [6]. Immunoglobulins participate principally the secondary immune response and possess a multitude of functions such as activate complement-mediated bacteriolytic reactions, augment the recognition and phagocytosis of bacteria by leucocytes (opsonization), prevent the adhesion of microbes to surfaces, inhibit bacterial metabolism, agglutinate bacteria, and neutralize toxins and viruses [7].

Heritability estimates for IgG concentrations in blood ranged from 0.27 to 0.64 in human [8-12]. In dairy cows, heritability estimates of immunoglobulins measured in blood or milk ranges from 0.08–0.45 [13-16], where IgM generally has the highest heritability estimates (0.18–0.45) and IgG has lower heritability estimates (0.08–0.31). Additionally, immunoglobulins level in serum had higher heritability than that in milk [13]. Previous GWASs have been performed for immunoglobulins content in serum or mature milk, the first study of these in 2,247 individuals from four European cohorts (CROATIA-Vis, CROATIA-Korcula, Orkney Complex Disease Study and Northern Swedish Population Health Study) identified 4 loci encoding glycosyltransferases associated with IgG N-glycans [17]. The GWAS for immunoglobulin G glycosylation patterns in human indicated that RUNX family transcription factor 3 (RUNX3) was associated with decreased galactosylation and involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis [18]. In pig, 4 significant SNPs were identified by GWAS performed for four traits, including interferon-gamma (IFN-c) and interleukin 10 (IL-10) levels, the ratio of IFN-c to IL-10 and IgG blocking percentage to CSFV in serum [19]. Especially, a previous GWAS for blood natural antibodies in Canadian Holstein cows found the 23 SNPs were significantly associated with IgG [12]. Another GWAS for milk natural antibodies in Dutch Holstein-Friesian cattle identified candidate genes on chromosome 17, 18, and 21 that related to immunoglobulin structure and early B cell development [20]. However, no study on immunoglobulin in colostrum have been reported in dairy cattle so far. In the present study, our objective is to identify candidate genes related to contents of immunoglobulins in colostrum and serum in dairy cattle and provide molecular information for genetically improving calves’ disease resistance.

Animals and phenotypes

The animals used in this study consists of 620 Chinese Holstein cows, the daughters of 44 sires with average 13.78 cows per sire. All the cows were from 10 dairy farms in the Beijing Dairy Cattle Center and the Beijing Sanyuan Lvhe Dairy Farming Center. The blood serum and colostrum samples were taken from each cow during the first milking within 24 h after calving for measurement of immunoglobulins. Also, hair follicle samples were collected from each animal for SNP chip genotyping. The cows were on average parity of 2.52, ranging from 1 to 4. The whole procedure for collection of the samples (blood, hair and colostrum) was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (Permit number: DK996).

The commercially available, ELISA kits were used to measure the antibodies levels of each colostrum sample and serum sample according to the manufacturer’s instructions, including IgG (Bovine IgG ELISA Quantitation Set E10-118, Bethyl Laboratories, Montgomery, TX, USA), IgG1 (Bovine IgG1 ELISA Quantitation Set, E10-116), IgG2 (Bovine IgG2 ELISA Quantitation Set, E10-117), IgA (Bovine IgA ELISA Quantitation Set, E10-131) and IgM (Bovine IgM ELISA Quantitation Set, E10-101). Then, the phenotype values for the concentration of IgG, IgG1, IgG2 in colostrum or serum were square root transformed to accomplish normality, while phenotype values for IgA and IgM traits were log-transformed corrected. Thus, the adjusted phenotypic values were used for further statistical analysis.

Genotyping

Genomic DNA was extracted from hair follicles of each cow by using the QIAamp® DNA Mini Kit (QIAGEN, Valencia, CA, USA). Of all the cows, 588 individuals were implemented with the 150k chip (including 140,668 SNPs, GeneSeek, Lincoln, NE, USA) and 32 cows were genotyped with Illumina Bovine SNP50 BeadChip (including 54,609 SNPs, Illumina, San Diego, CA, USA).

Data imputation and quality control

To make most use of SNPs from high density chip, the total 32 samples genotyped with 50k chip were imputed to 150k chip based on our previously developed reference population that included 1198 Chinese Holstein cows by using the BEAGLE 3.0.4 software [21]. Allelic R² was estimated as an indicator of imputation accuracy based on the genotype probabilities.

Then we performed PLINK [22] for SNPs quality control and removed the SNPs with call rates < 90%, minor allele frequencies < 0.1, a deviation from Hardy-Weinberg equilibrium (HWE) P values < 10^-6 and > 10 % missing genotypes [23]. After considering all the quality control measures, totally 88,934 SNPs were included in the genome-wide association study (Additional files 1 and 2). All SNP positions were determined according to the Bos taurus UMD 3.1 genome assembly.

Statistical analysis

Mixed model based single locus regression analyses (MMRA)

The association of the individual phenotype with each individual SNP was performed via following a mixed linear model:

y = Xβ +g + Aσ_g² +Iσ_ε²

Where y is an n × 1 the vector of root square or log transformed corrected concentration immunoglobulins of all cow after calving with n being the sample size of 619; β is the vector of fixed effects, including herd, parity and season of calving, X is an incidence matrix relating elements of β to y; g is an n × 1 vector of the total genetic effects of the individuals with g ~N (0, Aσ_g²), and A is interpreted as the genetic relationship matrix (GRM) between individuals. We can therefore estimate σ_g² by the restricted maximum likelihood (REML) approach [24], relying on the GRM estimated from all the SNPs. I is an n × 1 identity matrix, and ε is a vector of residual effects with ε~N (0, Iσ_ε²).

GWAS was carried out by GCTA 1.90.2 software [25]. GCTA is the method which can estimate variance explained by all SNPs, and extend the method to partition the genetic variance onto each of the chromosomes and also to estimate the variance explained by the X chromosome.

In order to define the thresholds for genome-wide significant/suggestive associations, we calculated the effectively independent tests based on the estimated number of independent markers and linkage disequilibrium blocks for markers [26]. A linkage disequilibrium block was defined as a set of adjacent SNPs with pairwise r² values greater than 0.40 [27].

A total of 15,820 effectively independent tests was recommended and the threshold P-value for genome-wide significance association was 3.16E–6 (0.05/15,820) and for suggestive association was 6.32E–5 (1/15,820) suggested by Lander & Kruglyak [26, 28]. Inflation or deflation in p-values due to stratification or family structure was assessed by genomic inflation factor (λ) and also visually inspected by quantile-quantile (Q-Q) plot. λ is calculated as the median of the χ2 test statistics (1 degree of freedom) divided by its theoretical median under the null distribution[29]. The λ was calculated using GENABEL packages of R.

Haplotype block analysis

To further detect candidate regions associated with the immunoglobulins contents in colostrum and serum, we performed linkage disequilibrium analysis for the chromosomal regions with adjacent multiple significant SNPs based on Haploview v4.2[30]. The block was defined according to the solid spin algorithm by the criteria of Gabriel [31].

Identification of candidate genes and functional annotation

^{To identify the positional candidate genes that are potentially associated with the levels of immunoglobulins in colostrum and serum, the genomic regions that were close to the significant SNPs with a distance less than 1 Mb were selected. These regions were then referenced against the bovine genome assembly (UMD 3.1, Ensemble Genes Release 92) using the BioMart tool (http://asia.ensembl.org/biomart/martview) to found genes that are located in the vicinity of the significant SNPs. Simultaneously, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for these extracted genes by KOBAS 3.0 [32]. KOBAS annotates a set of genes with putative pathways and immunity relationships by mapping to genes with a known annotation. Afterwards, we compared the physical positions of the identified candidate genes with the reported quantitative traits loci (QTLs) that have been shown to be associated with immune capacity traits for cattle in the Animal QTL database (http://www.animalgenome.org/cgi-bin/QTLdb/index).}

Statistics of phenotype and SNP data

In this study, we analyzed 10 traits, including concentration of IgG, IgG1, IgG2, IgA and IgM in colostrum and serum. Mean and the corresponding standard deviations for the original and corrected phenotypic values with or without square root or log transformation were shown in Table 1.

Genome-wide association study

For the genotype imputation with the software Beagle 3.0.4, SNPs with R² > 0.3 were retained [33] and the average allelic R² of imputed genotypes was 74.8% , which made sure the accuracy of imputation and full use of chip data. With GCTA 1.90.2, no population substructure was observed form QQ plots (Figures. 1). In addition, the inflation factor (λ) estimated was from 0.98-1.02 for all traits, which indicated that our results can be accepted for further analysis.

For the contents of immunoglobulins in colostrum, as shown in Figure. 2 and Table 2, at genome-wide significant level, 3 SNPs (P<3.16E-6) located on BTA-20 (1 SNP) and 21 (2 SNPs) were found for concentration of IgG2 in colostrum. At suggestive significant level, a total of 11, 7 and 17 suggestive significant SNPs (P<6.32E-5) were detected for concentration of IgG, IgG1 and IgG2 in colostrum. Of these, the majority of them were located on BTA-21 (15 SNPs) while a few SNPs were located on BTA-1 (3 SNPs), 23 (2 SNPs), 24(7 SNPs), 3 (2 SNPs), 5 (1 SNP), 6 (1 SNP), 20 (2 SNPs), and 27 (2 SNPs). In addition, a total of 3 and 6 suggestive significant SNPs (P<6.32E-5) were detected for the concentration of IgA and IgM in colostrum and distributed in chromosomes 1 (1 SNP), 4 (1 SNP), 5 (1 SNP), 8 (1 SNP), 10 (2 SNPs), 15 (1 SNP) and 17 (2 SNPs). Associations between 4 SNPs and concentration of IgM in colostrum reached genome-wide significant level in chromosome 10 (2 SNPs) and 17 (2 SNPs) (P<3.16E-6).

For the immunoglobulins contents in serum, genome-wide significant associations between 17 SNPs and concentration of IgG2 were found in BTA-21 (16 SNPs) and 20 (1 SNP) (P<3.16E-6). Additionally, 22 suggestive significant SNPs (P<6.32E-5) were detected for IgG1 and IgG2 in serum. Of these, 15 SNPs were located on BTA-21 and the rest of them were on 28 (1 SNP), 19 (1 SNP), 7 (1 SNP), 19 (1 SNP), 12 (2 SNPs), and 20 (1 SNP) (Figure. 2 and Table 3). At both level, concentration of IgG in serum did not show any significant associations. For IgA and IgM in serum, 8 and 4 suggestive significant SNPs (P<6.32E-5) were identified in chromosomes 2 (1 SNP), 15 (3 SNPs), 18 (1 SNP), 25 (2 SNP), 29 (1 SNP), 4 (1 SNP), 6 (1 SNP) and 7 (2 SNPs) (Figure. 2 and Table 3). And 1 SNP associate with IgA in BTA-17 achieved genome-wide significant level (P<3.16E-6). The Manhattan plots for concentrations of IgG, IgG1, IgG2 IgA and IgM in colostrum and serum were shown in Figures. 2.

Haplotype Block Analysis

As shown in Figure. 2, with regards to the concentration of IgG in colostrum, 7 significant SNPs on BAT 24 (45.89–46.24Mb) generated a haplotype block (Figure. 3A) and 22 genes were found within this region after gene searching. As for the concentration of IgG2 in colostrum, 18 significant SNPs were found located on BTA-21 (66.58–71.39Mb) that contained 168 genes. In this region, we detected 3 haplotype blocks: block 1(14kb, 2 SNPs), block 2 (17kb, 3 SNPs) and block 3 (14kb, 2 SNPs) (Figure. 3B). For the concentration of IgG2 in serum, we detected 32 significant SNPs on BAT 21 (64.93–72.48Mb) that accommodate 204 genes and totally formed 3 haplotype blocks, including block 1 (18kb, 2 SNPs), block 2 (62kb, 4 SNPs) and block 3 (14kb, 2 SNPs) (Figure. 3C). Additionally, the haplotype block of IgG2 concentration in colostrum was covered in the block of IgG2 concentration in serum. However, few significant SNPs were observed within haplotype blocks for the other traits.

Candidate Genes and Function Analysis

For IgG in serum and colostrum, a total of 707 functional genes that contained or were near (within 1Mb) the identified significant SNPs were obtained based on the bovine genome assembly UMD3.1. In addition, 252 and 408 functional genes were identified that contained or were near the identified significant SNPs for IgA and IgM traits with distance of less than 1 Mb, respectively. After removing the duplicates of results, a total of 1,083 candidate genes were obtained for contents of immunoglobulins traits, 706 protein-coding genes, 60 miRNA genes, 45 spliceosomal RNAs, 76 small nucleolar RNAs and 196 novel genes (Additional file 3: Table S1). Out of these, with GO and KEGG analysis, 151 genes were observed participated in immune related pathways such as immune response, Fc gamma R-mediated phagocytosis, negative regulation of immunoglobulin secretion, humoral immune response, Fc-epsilon receptor and NF-kappaB signaling pathways, etc (Additional file 4:Table S2).

Based on the Cattle QTL database that has released 232 loci for immune capacity until now (April 26, 2020, http://www.animalgenome.org/cgi-bin/QTLdb/), we compared the physical positions of the 151 candidate genes with the peak of the known QTLs for immune capacity in dairy cattle, including IgG level, FMDV peptide-induced cell proliferation and ConA-induced cell proliferation. Consequently, 21 genes were found located within the QTL regions with distance to the peak positions of less than 1.0 cM so that they were considered as promising candidates for immunoglobulins level in serum and colostrum (Table 4). They were BR activator of RhoGEF and GTPase (ABR), translocase of inner mitochondrial membrane 22 (TIMM22), CRK proto-oncogene, adaptor protein (CRK), myosin IC(MYO1C), Rab interacting lysosomal protein (RILP), serpin family F member 2 (SERPINF2), AKT serine/threonine kinase 1 (AKT1), BAF chromatin remodeling complex subunit BCL11B (BCL11B), HHIP like 1 (HHIPL1),dynein cytoplasmic 1 heavy chain 1(DYNC1H1), heat shock protein 90 alpha family class A member 1(HSP90AA1), TNF receptor associated factor 3(TRAF3), kinesin light chain 1(KLC1), interleukin 6 (IL6), PYD and CARD domain containing (PYCARD), integrin subunit alpha M (ITGAM), transforming growth factor beta 1 induced transcript 1 (TGFB1I1), glucuronidase beta (GUSB), CGRP receptor component (CRCP), RAB guanine nucleotide exchange factor 1 (RABGEF1) and SBDS ribosome maturation factor (SBDS).

In this study, we identified candidate region influencing immunoglobulins in colostrum and serum by performing GWASs with high density SNP genotypes. We found 3 significant regions on 21, 10 and 17 chromosome and 78 suggestive significant SNPs on other 20 chromosomes associated with IgG, IgA and IgM. To our knowledge, this is the first investigation focusing on unrevealing the genetic mechanism of these immune traits in bovine colostrum based on a high-density SNP chip panel.

Population stratiﬁcation is a major factor for false positives in GWAS. In general, a genomic inﬂation factor λ of <1.05 indicates no population stratiﬁcation [34]. In this study, λ values were 0.98~1.02 for concentration and ratio of IgG, IgM and IgA in colostrum and serum. QQ plots indicated that population stratiﬁcation has been controlled as well. In addition, we calculated the effectively independent tests based on the estimated number of independent markers and linkage disequilibrium blocks for autosome markers, and a total of 15,820 effectively independent tests was suggested. This setting of thresholds may lead slight false positive but numerous useful information.

In this GWAS, we found a region on BTA-21 from 63.9 to 71.5Mb that was significantly associated with for IgG2. Previous GWASs on bovine nature antibodies in Canadian and Dutch Holstein populations identified genomic regions on BTA-21 from 55.5 to 70.6 Mb and 66.0 to 71.6 Mb that were significantly associated with KLH-IgG and PGN-IgG1 [12, 20], which overlapped with region in this study. Although our samples were taken from colostrum and serum, whereas theirs were from serum and mature milk, and we analyzed IgG, IgG1 and IgG2 while they analyzed IgG and IgG1, it is likely due to the high genetic correlation between the same IgG isotype in serum and milk (0.81±0.18) [13]. This implies that the all IgG isotype subclasses from serum or milk shared the same genetic mechanism.

For IgM, although its heritability was relatively high, we detected 10 and 5 SNPs associate with concentration of IgM in colostrum and serum, respectively, we did not identified any candidate genes for this trait. Also, the identified significant SNPs for IgM in colostrum and serum were totally different. Similar results were reported in a previous GWAS by de Klerk et al. [12]. As for the other genomic regions we identified for immunoglobulins have not been reported in previous studies in bovine.

Role of Candidate Genes

For IgG1 in colostrum and serum, 6 candidate genes were identified and from 22.3~23.4 Mbp on BTA-19. ABR, encodes protein that contains a GTPase-activating protein domain. Previous studies identified ABR and BCR as the only GTPase-activating proteins that specifically negatively regulate Rac function in vivo in primary macrophages and normally curb very specific functions of mature tissue innate immune cells [35, 36]. The protein encoded by TIMM22 is a subunit of TIM22 which is transporters and represents the voltage-activated and signal-gated channel. TIMM22 is involved in protein transmembrane transporter activity, which may relate to the transport of IgG though the receptor. CRK encodes a member of an adapter protein family that binds to several tyrosine-phosphorylated proteins. Differential migration of CRK/CRKL-deficient T cells resulted in efficient graft-versus-leukemia responses in mice [37]. MYO1C encodes a member of the unconventional myosin protein family which play a role in bacterial infectious disease and Pathogenic Escherichia coli infection. RILP encodes a lysosomal protein that is related to the phagosomes in macrophages [38]. SERPINF2 encodes a member of the serpin family of serine protease inhibitors. The protein is a major inhibitor of plasmin, which degrades fibrin and various other proteins. SERPINF2 is involved in the recruitment of lymphocytes in the peripheral tissues and the progression of fibrosis. Besides, SERPINF2 contributes to the maintenance of immunological functions that are related to IgE in human [39].

For IgG2, totally 7 candidate genes were found on BTA-21 from 65.8~70.9Mbp. AKT1, is part of the PI3K-AKT pathway and involved in B cell receptor, T cell receptor, Fc gamma R-mediated phagocytosis pathway. Some of the known functions include T cell development and FOXO1 signaling, which involves maturation and survival of peripheral B cells and class switching [40-42]. It is worth mentioning that AKT1 was also identified as candidate gene for IgG1 in a previous study [20]. BCL11B encodes a C2H2-type zinc finger protein that regulates the initial stages of human T-cell differentiation and mediated epigenetic repression for histone deacetylase inhibitors in cutaneous T-Cell lymphoma [43-45]. HHIPL1 belongs to the glucose/sorbosone dehydrogenase family and take part in receptor-mediated endocytosis so that it can effect receptor activity. DYNC1H1 encodes a member of the cytoplasmic dynein heavy chain family that function as molecular motors and play a role in Salmonella infection. The protein encoded by HSP90AA1 is an inducible molecular chaperone that functions as a homodimer. Hsp90 was related to beta cell autoimmunity and protected cells from complement-dependent cytotoxicity by inhibiting, together with mortalin, C5b-9 assembly and/or stability at the plasma membrane [46, 47]. The protein encoded by TRAF3 is a member of the TNF receptor associated factor (TRAF) protein family that participates in the signal transduction of CD40, a TNFR family member important for the activation of the immune response and was found to be a critical component of the lymphotoxin-beta receptor (LTbetaR) signaling complex, which induces NF-kappaB activation and cell death initiated by LTbeta ligation [48, 49]. The findings suggest that Parkin plays a novel role in innate immune signaling by targeting TRAF3 for degradation and maintaining the balance of innate antiviral immunity [50]. A TRAF3-NIK axis differentially regulates viral DNA vs RNA pathways in innate immune signaling [51]. KLC1 encodes a member of the kinesin light chain family. KLC1 is associated with infectious disease caused by bacterial. Additionally, 2 and 4 significant SNPs for the ratio of IgG1 and IgG2 in colostrum and serum but no candidate gene were selected, which might attribute to the less transmission but most self-synthetization.

For IgA, we selected 7 candidate genes. PYCARD, encodes an adaptor protein that is composed of two protein-protein interaction domains: a N-terminal PYRIN-PAAD-DAPIN domain (PYD) and a C-terminal caspase-recruitment domain (CARD). The PYD and CARD domains are members of the six-helix bundle death domain-fold superfamily that mediates assembly of large signaling complexes in the inflammatory and apoptotic signaling pathways via the activation of caspase. In normal cells, this protein is localized to the cytoplasm. Han Y et.al found that PYCARD as a negative regulator of the MAVS-mediated innate immunity, may play an important role in host protection upon virus infection [52]. ITGAM gene encodes the integrin alpha M chain. The alpha M beta 2 integrin is important in the adherence of neutrophils and monocytes to stimulated endothelium, and also in the phagocytosis of complement coated particles and ITGAM as a ligand for the integrin Mac-1 and suggest that many immune-modulating effects previously ascribed to PF4 are mediated through its interaction with Mac-1 [53]. TGFB1I1 encodes a coactivator of the androgen receptor, a transcription factor which is activated by androgen and has a key role in male sexual differentiation. Hic-5 regulates GR binding site selection by a novel mechanism, exploiting gene-specific requirements for chromatin remodeling enzymes to selectively influence DNA occupancy and gene regulation by a transcription factor [54]. GUSB encodes a hydrolase that degrades glycosaminoglycans, including heparan sulfate, dermatan sulfate, and chondroitin-4,6-sulfate and play role in HCV-infected human liver [55]. CRCP encodes a membrane protein that functions as part of a receptor complex for a small neuropeptide that increases intracellular cAMP levels. The influence of the CGRP family peptides in reproduction and pregnancy is explored and discussed[56]. RABGEF1 forms a complex with rabaptin-5 (RABPT5; MIM 603616) that is required for endocytic membrane fusion. Millrine D et, al. identified Rabex-5 as an immunomodulatory drugs (IMiDs) target molecule that functions to restrain TLR activated auto-immune promoting pathways. We propose that release of Rabex-5 from complex with Cereblon enables the suppression of immune responses, contributing to the anti-inflammatory properties of IMiDs [57]. Additionally, RABGEF1 mediates recycling endosome fusion with GAS-containing autophagosome-like vacuoles through the STX6-VAMP3-VTI1B complex; SNAREs are involved in autophagosome formation in response to bacterial infection [58]. SBDS encodes a highly conserved protein that related to primary immunodeficiency and phagocyte defects. In addition, SBDS play a role in immune system process and hematopoietic or lymphoid organ development. Another candidate gene in chromosome 4, IL6, encodes a cytokine that functions in inflammation and the maturation of B cells. Besides, IL6 has been shown to have important functions in immune response, hematopoiesis, inflammation and the acute phase response. The protein is primarily produced at sites of acute and chronic inflammation, where it is secreted into the serum and induces a transcriptional inflammatory response through interleukin 6 receptor, alpha [59, 60]. In the breeding perspective of dairy cattle, the relatively high heritability of immunoglobulin and available genetic information provides an opportunity to include immunoglobulin as a new trait to improve dairy cattle health. Although plentiful studies for the benefits of IgM and to a lesser extent IgG in mice [61-63], the beneficial correlations reported between immunoglobulin and resistance against infectious diseases are minimal in livestock [15, 64, 65]. Hence, the character of immunoglobulin in immunity of dairy cattle needs further verification. It cannot be ignored that a number of studies are proposing self-antigens as the stimulator of B1 cells and reporting both negative and positive roles of self-reactive immunoglobulin in autoimmune diseases [62]. In mice, lupus-like autoimmune diseases developed when immunoglobulin production was impaired, but mice that could produce only IgM immunoglobulin, but not IgG immunoglobulin, did not develop the autoimmune disease [66, 67]. Therefore, to develop breeding strategies to increase immunoglobulin in animals with relatively long lifespan such as cattle the antigen should be cautiously explored and animals monitored for any signs of autoimmunity.

Natural antibodies immunoglobulins in colostrum and serum have an important genetic component, and we identified specific variants in the genome that influence immunoglobulins by GWAS. A total of 25 genome wide and 78 suggestive significant SNPs were identified for IgG, IgG1, IgG2, IgA and IgM in both colostrum and serum. In the case of IgG, chromosomes-19, 21, carried the main quantitative trait loci. Candidate regions for IgA were main located in chromosomes-4 and 25. Integrated analysis of the identified significant SNPs, functional enrichment and the known QTL data, we suggested 21 candidate genes for content of immunoglobulins in colostrum and serum. Of these, ABR, TIMM22, CRK, MYO1C, RILP, SERPINF2, AKT1, BCL11B, HHIPL1, DYNC1H1, HSP90AA1, TRAF3 and KLC1 were for IgG traits, as well as IL6, PYCARD, ITGAM, TGFB1I1, GUSB, CRCP, RABGEF1, SBDS were identified candidates for IgA content in colostrum and serum, respectively. Our results provide new insights into the regulation of immunoglobulins that will help give a better understanding of the complex relationship between milk nature antibodies and disease resistance in dairy cattle and provide critical genetic information for management or genome selection.

Acknowledgements

We appreciate Beijing Dairy Cattle Center for providing the animal samples.

Funding

This work was financially supported by the National Natural Science Foundation of China (31872330, 31872330, 31802041), Beijing Science and Technology Program (20200105, D171100002417001), and the Program for Changjiang Scholar and Innovation Research Team in University (IRT_15R62).

Availability of data and materials

All supporting data can be found within the additional files.

Authors’ contributions

SL performed bioinformatics and statistical analysis, and also was a major contributor to manuscript preparation. CK, LL, YG and XL performed experiments and sample collection. CK and BH participated in result interpretation, wrote, revised and approved the manuscript. YZ and SZ commented the manuscript. DS conceived and designed the experiments and was the major contribution to manuscript revision. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All protocols for collection of the blood and hair samples of China Holstein cows were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University. Blood and hair samples were collected specifically for this study following standard procedures with the full agreement of the Beijing Dairy Cattle Center who owned the animals

Consent for Publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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**Table 1**: Means and standard deviations for the original and corrected concentrations of immunoglobulins; n = 620
Traits	Mean	Std dev	Min	Max	Traits	Mean	Std dev	Min	Max
col_IgG	47.44	29.02	0.01	146.60	col_IgG_sqrt	6.51	2.23	0.10	12.11
col_IgG1	14.46	7.75	0.01	37.73	col_IgG1_sqrt	3.63	1.14	0.05	6.14
col_IgG2	3.20	1.94	0.07	11.04	col_IgG2_sqrt	1.70	0.54	0.26	3.32
ser_IgG	8.34	3.37	0.68	22.90	ser_IgG_sqrt	2.83	0.58	0.82	4.79
ser_IgG1	1.04	0.74	0.01	7.65	ser_IgG1_sqrt	1.04	0.31	0.09	2.77
ser_IgG2	13.79	6.51	0.69	42.36	ser_IgG2_sqrt	3.61	0.86	0.83	6.51
col_IgA	3.11	3.59	0.01	51.18	col_IgA_log	0.28	0.49	-1.91	1.71
col_IgM	5.47	3.70	0.09	36.41	col_IgM_log	0.63	0.35	-1.06	1.56
ser_IgA	0.23	0.17	0.01	1.21	ser_IgA_log	-0.73	0.39	-5.40	0.08
ser_IgM	2.25	1.68	0.01	14.93	ser_IgM_log	0.24	0.34	-2.34	1.17

Mean, arithmetic mean; SD, standard deviation; Max, maximum; Min, minimum; col_IgG, col_IgG1, col_IgG2, col_IgA and col_IgM represent the corresponding concentration value of IgG, IgG1 IgG2, IgA and IgM in colostrum, ser_IgG, ser_IgG1, ser_IgG2, ser_IgA and ser_IgM represent the corresponding concentration value of IgG, IgG1 IgG2, IgA and IgM in serum, respectively; _sqrt, phenotype by square root transformed; _log, phenotype by log transformed.

**Table 2**: The significant SNPs for concentrations of IgG, IgG1, IgG2, IgA and IgM in colostrum.
Triats^a	Chr^b	SNP	Position (bp)	Major/ minor allele	MAF^c	SNP effect^d	SE^e	P-value
col_IgG	1	BovineHD0100036412	128805177	A/G	0.281	11.960	2.964	4.20E-06
col_IgG	1	BTB-02067404	129170050	A/G	0.253	12.451	3.067	6.02E-05
col_IgG	23	BovineHD2300015105	52008189	A/G	0.431	11.301	2.818	4.38E-06
col_IgG	23	BovineHD2300015111	52023784	A/G	0.430	11.358	2.813	2.15E-05
col_IgG	24	BovineHD2400012624	45892763	C/A	0.467	11.042	2.752	2.70E-05
col_IgG	24	ARS-BFGL-NGS-18561	45933554	G/A	0.452	11.211	2.728	5.45E-05
col_IgG	24	BovineHD2400012657	45972409	A/G	0.366	13.095	2.851	3.97E-05
col_IgG	24	ARS-BFGL-NGS-20011	45973112	G/A	0.364	13.160	2.860	5.40E-05
col_IgG	24	ARS-BFGL-NGS-106793	45998467	A/C	0.378	11.689	2.785	4.92E-05
col_IgG	24	ARS-BFGL-NGS-44158	46027109	G/A	0.377	11.869	2.794	6.06E-05
col_IgG	24	BovineHD2400012729	46241172	A/G	0.351	11.496	2.845	5.32E-05
col_IgG1	1	BovineHD0100020052	70005456	G/A	0.363	2.027	0.457	9.23E-06
col_IgG1	3	ARS-BFGL-NGS-105483	52329712	A/G	0.457	-1.770	0.437	5.07E-05
col_IgG1	5	BovineHD0500008574	29136394	A/G	0.195	-2.652	0.571	3.43E-06
col_IgG1	6	BovineHD0600010748	38816105	G/A	0.312	1.900	0.472	5.72E-05
col_IgG1	20	BovineHD2000008079	27230044	A/G	0.386	-1.946	0.483	5.59E-05
col_IgG1	22	BovineHD2200013311	46250930	G/A	0.407	-2.011	0.493	4.50E-05
col_IgG1	27	ARS-BFGL-NGS-24322	19137853	G/C	0.398	-1.936	0.462	2.75E-05
col_IgG2	3	BTB-00163972	118145216	A/G	0.324	0.147	0.035	2.88E-05
col_IgG2	20	BovineHD2000021006	71896856	G/A	0.273	0.223	0.037	1.13E-09
col_IgG2	21	BovineHD2100019547	66578213	A/G	0.403	-0.139	0.032	1.75E-05
col_IgG2	21	BovineHD2100020165	69062573	G/A	0.399	-0.135	0.032	2.72E-05
col_IgG2	21	BovineHD2100020225	69289258	A/G	0.331	0.145	0.033	1.08E-05
col_IgG2	21	BovineHD2100020241	69357379	A/C	0.427	0.139	0.033	2.87E-05
col_IgG2	21	ARS-BFGL-NGS-115062	69395154	G/A	0.450	0.133	0.033	5.98E-05
col_IgG2	21	BovineHD2100020341	69673486	A/G	0.361	0.148	0.032	5.34E-06
col_IgG2	21	BovineHD2100020413	69920970	G/A	0.387	0.132	0.033	5.57E-05
col_IgG2	21	ARS-BFGL-NGS-2644	70608408	A/G	0.350	0.155	0.034	5.00E-06
col_IgG2	21	BovineHD2100020676	70621565	G/A	0.350	0.159	0.034	3.22E-06
col_IgG2	21	BovineHD2100020685	70655075	A/G	0.327	0.174	0.035	5.78E-07
col_IgG2	21	BovineHD2100020687	70665452	G/A	0.281	-0.150	0.037	4.31E-05
col_IgG2	21	BovineHD2100020689	70672433	A/G	0.379	0.142	0.034	3.35E-05
col_IgG2	21	BovineHD2100020696	70687439	G/A	0.368	0.138	0.033	2.44E-05
col_IgG2	21	ARS-BFGL-NGS-73522	70702245	G/A	0.347	0.152	0.034	7.68E-06
col_IgG2	21	BovineHD4100015389	70726088	C/A	0.367	-0.142	0.034	2.84E-05
col_IgG2	21	BovineHD2100020833	71318798	A/G	0.286	0.169	0.035	1.11E-06
col_IgG2	21	BovineHD2100020837	71338809	G/A	0.148	0.176	0.044	6.25E-05
col_IgG2	21	BovineHD2100020853	71389313	G/A	0.248	-0.167	0.039	1.55E-05
col_IgA	1	BovineHD0100012276	43138880	A/C	0.425	0.129	0.031	3.61E-05
col_IgA	4	BTB-00172973	31573521	G/A	0.267	-0.140	0.033	2.87E-05
col_IgA	5	BovineHD0500021305	74998613	G/A	0.334	-0.138	0.031	9.66E-06
col_IgM	8	BovineHD0800026113	88021995	G/A	0.484	0.189	0.047	6.22E-05
col_IgM	10	BovineHD1000019825	69080826	A/C	0.162	-0.305	0.060	4.14E-07
col_IgM	10	BovineHD1000019983	69718494	C/A	0.323	-0.242	0.049	9.53E-07
col_IgM	10	BovineHD1000020063	70071886	A/G	0.429	-0.212	0.048	1.01E-05
col_IgM	10	Hapmap29285-BTA-162580	69721690	T/A	0.356	-0.201	0.049	3.68E-05
col_IgM	15	ARS-BFGL-NGS-109495	1492400	A/T	0.379	-0.187	0.047	6.16E-05
col_IgM	17	BTB-01973064	73349750	A/G	0.391	0.234	0.048	1.33E-06
col_IgM	17	BovineHD1700021434	73349278	A/G	0.457	0.227	0.047	1.35E-06
col_IgM	17	BovineHD1700021122	72466328	G/A	0.439	-0.215	0.047	4.18E-06
col_IgM	17	ARS-BFGL-NGS-5369	71841734	A/C	0.298	-0.216	0.051	2.51E-05

^acol_IgG, col_IgG1, col_IgG2, col_IgA and col_IgM represent concentration of IgG, IgG1 IgG2, IgA and IgM in colostrum, respectively.

^bCow chromosome number.

^cMinor allele frequency.

^dSNP effect.

^estandard error

**Table 3**: The significant SNPs for concentrations of IgG, IgG1, IgG2, IgA and IgM in serum.
Triats^a	Chr^b	SNP	Position (bp)	Major/ minor allele	MAF^c	SNP effect^d	SE^e	P-value
ser_IgG1	28	BovineHD2800011781	41894086	A/G	0.362	0.083	0.018	6.88E-06
ser_IgG1	19	Hapmap57648-rs29022376	22709090	G/A	0.347	-0.084	0.019	1.28E-05
ser_IgG1	7	BovineHD0700032048	109730181	A/G	0.445	-0.075	0.018	2.89E-05
ser_IgG1	19	ARS-BFGL-NGS-20165	23561900	C/A	0.410	-0.075	0.018	4.19E-05
ser_IgG2	12	BovineHD1200008568	28980452	C/A	0.323	0.251	0.055	5.84E-06
ser_IgG2	12	BovineHD1200008569	28987016	A/G	0.478	-0.204	0.050	5.16E-05
ser_IgG2	20	BovineHD2000021006	71896856	G/A	0.274	0.413	0.057	5.04E-13
ser_IgG2	21	BovineHD2100018787	63926754	A/G	0.464	-0.229	0.051	7.19E-06
ser_IgG2	21	BTA-24891-no-rs	63955841	G/A	0.357	0.214	0.052	4.18E-05
ser_IgG2	21	BovineHD2100019656	66910728	G/A	0.416	0.215	0.051	2.36E-05
ser_IgG2	21	ARS-BFGL-NGS-20339	67088847	G/A	0.333	0.240	0.052	4.71E-06
ser_IgG2	21	BovineHD2100019814	67542721	A/C	0.363	0.240	0.053	5.03E-06
ser_IgG2	21	BovineHD2100019834	67604077	A/C	0.147	0.289	0.071	5.19E-05
ser_IgG2	21	BovineHD4100015366	67786313	A/G	0.421	0.267	0.052	2.94E-07
ser_IgG2	21	BovineHD2100019888	67885290	A/G	0.217	0.246	0.060	4.21E-05
ser_IgG2	21	BovineHD2100019906	67946189	A/G	0.319	0.241	0.054	6.86E-06
ser_IgG2	21	ARS-BFGL-NGS-86477	68399787	A/C	0.482	-0.213	0.052	4.06E-05
ser_IgG2	21	BovineHD2100020157	69009950	A/C	0.442	0.229	0.052	1.07E-05
ser_IgG2	21	BovineHD2100021033	69033145	G/A	0.201	0.331	0.062	7.14E-08
ser_IgG2	21	BovineHD2100020225	69289258	A/G	0.331	0.272	0.052	1.37E-07
ser_IgG2	21	BovineHD2100020232	69327116	G/A	0.349	0.271	0.051	1.32E-07
ser_IgG2	21	BovineHD2100020241	69357379	A/C	0.424	0.224	0.053	2.14E-05
ser_IgG2	21	ARS-BFGL-NGS-115062	69395154	G/A	0.450	0.231	0.053	1.17E-05
ser_IgG2	21	BovineHD2100020317	69613677	G/A	0.243	0.254	0.060	2.29E-05
ser_IgG2	21	BovineHD2100020341	69673486	A/G	0.366	0.299	0.051	4.21E-09
ser_IgG2	21	BovineHD2100020413	69920970	G/A	0.386	0.263	0.052	3.92E-07
ser_IgG2	21	ARS-BFGL-NGS-1345	69939350	C/A	0.420	0.232	0.052	7.32E-06
ser_IgG2	21	BovineHD2100020439	70000656	A/G	0.446	0.239	0.053	6.19E-06
ser_IgG2	21	ARS-USDA-AGIL-chr21-70182028-000470	70182028	C/G	0.376	0.212	0.052	5.34E-05
ser_IgG2	21	BovineHD2100020653	70537404	A/G	0.182	0.380	0.063	2.17E-09
ser_IgG2	21	BovineHD2100020670	70592463	A/C	0.415	0.251	0.052	1.52E-06
ser_IgG2	21	ARS-BFGL-NGS-2644	70608408	A/G	0.353	0.330	0.053	6.66E-10
ser_IgG2	21	BovineHD2100020676	70621565	G/A	0.353	0.343	0.054	1.74E-10
ser_IgG2	21	BovineHD2100020685	70655075	A/G	0.329	0.342	0.055	4.05E-10
ser_IgG2	21	BovineHD2100020689	70672433	A/G	0.381	0.256	0.054	1.73E-06
ser_IgG2	21	BovineHD2100020696	70687439	G/A	0.370	0.296	0.051	8.63E-09
ser_IgG2	21	ARS-BFGL-NGS-73522	70702245	G/A	0.349	0.287	0.053	6.29E-08
ser_IgG2	21	BovineHD2100020833	71318798	A/G	0.286	0.322	0.055	6.15E-09
ser_IgG2	21	BovineHD2100020883	71479429	A/G	0.165	0.400	0.068	3.18E-09
ser_IgA	2	ARS-BFGL-NGS-100214	132821698	A/G	0.139	-0.142	0.033	1.76E-05
ser_IgA	15	BTA-91367-no-rs	60316301	G/A	0.464	-0.099	0.024	2.52E-05
ser_IgA	15	BovineHD1500016066	55611146	G/A	0.477	-0.101	0.025	4.69E-05
ser_IgA	15	BovineHD1500016061	55590128	G/A	0.476	-0.100	0.025	5.43E-05
ser_IgA	18	BovineHD1800015730	53613268	G/A	0.133	-0.150	0.037	5.23E-05
ser_IgA	25	BovineHD2500007939	28524826	A/G	0.387	-0.106	0.025	1.88E-05
ser_IgA	25	BovineHD2500007948	28580391	A/G	0.388	-0.107	0.025	1.96E-05
ser_IgA	29	BovineHD2900006144	21381114	A/G	0.201	-0.115	0.028	5.55E-05
ser_IgM	4	BovineHD0400027460	98194248	A/G	0.115	0.125	0.031	6.29E-05
ser_IgM	6	BovineHD0600009523	34015077	G/A	0.270	-0.101	0.022	3.73E-06
ser_IgM	7	ARS-BFGL-NGS-12159	19220954	A/C	0.433	-0.084	0.020	1.56E-05
ser_IgM	7	BovineHD0700005341	19183296	A/G	0.432	-0.081	0.019	3.45E-05
ser_IgM	17	BovineHD1700021706	74223510	G/A	0.170	-0.123	0.026	2.74E-06

^aser_IgG1, ser_IgG2, ser_IgA and ser_IgM represent concentration of IgG1 IgG2, IgA and IgM in serum, respectively.

^bCow Chromosome number.

^cMinor allele frequency.

^dSNP effect.

^estandard error

**Table 4**: The list of candidate genes nearby the SNPs associated with IgG, IgG1, IgG2, IgA and IgM in the colostrum and serum.
Gene ID	Gene Name	Chr^a	Gene Start	Gene End	Traits^b
ENSBTAG00000008424	ABR	19	22287405	22431563	ser_IgG1
ENSBTAG00000008423	TIMM22	19	22432982	22439070	ser_IgG1
ENSBTAG00000005665	CRK	19	23139102	23159120	ser_IgG1
ENSBTAG00000001332	MYO1C	19	23168435	23191280	ser_IgG1
ENSBTAG00000038889	RILP	19	23320981	23323996	ser_IgG1
ENSBTAG00000020859	SERPINF2	19	23403657	23411269	ser_IgG1
ENSBTAG00000017636	AKT1	21	70878138	70895537	col_IgG2, ser_IgG2
ENSBTAG00000018019	BCL11B	21	65841997	65938304	col_IgG2, ser_IgG2
ENSBTAG00000026913	HHIPL1	21	66284693	66314293	col_IgG2, ser_IgG2
ENSBTAG00000016598	DYNC1H1	21	68507134	68568024	col_IgG2, ser_IgG2
ENSBTAG00000006270	HSP90AA1	21	68595873	68601204	col_IgG2, ser_IgG2
ENSBTAG00000013475	TRAF3	21	69228047	69254153	col_IgG2, ser_IgG2
ENSBTAG00000017299	KLC1	21	69896140	69957173	col_IgG2, ser_IgG2
ENSBTAG00000014921	IL6	4	31578311	31582667	col_IgA
ENSBTAG00000020535	PYCARD	25	27541409	27542740	ser_IgA
ENSBTAG00000047238	ITGAM	25	27602222	27639821	ser_IgA
ENSBTAG00000002391	TGFB1I1	25	27760811	27766954	ser_IgA
ENSBTAG00000000704	GUSB	25	28162569	28174955	ser_IgA
ENSBTAG00000037489	CRCP	25	28255764	28294982	ser_IgA
ENSBTAG00000003842	RABGEF1	25	28509094	28534880	ser_IgA
ENSBTAG00000004051	SBDS	25	28630825	28636300	ser_IgA

^aCow chromosome number.

^bGene contain SNP significantly associated with concentration of IgG, IgG1 IgG2, IgA and IgM in colostrum and serum, respectively. col_IgG, col_IgG1, col_IgG2, col_IgA and col_IgM represent concentration of IgG, IgG1 IgG2, IgA and IgM in colostrum, ser_IgG, ser_IgG1, ser_IgG2, ser_IgA and ser_IgM represent concentration of IgG, IgG1 IgG2, IgA and IgM in serum, respectively.

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posted 10 Jul, 2020

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Genome-Wide Association Studies for Immunoglobulins in Colostrum and Serum in Chinese Holstein

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