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Article

Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients

by
Victoria C. Kelson
1,
Jennifer N. Kiser
2,
Kimberly M. Davenport
1,
Emaly M. Suarez
1,
Brenda M. Murdoch
3 and
Holly L. Neibergs
1,*
1
Department of Animal Sciences, Washington State University, Pullman, WA 99163, USA
2
Washington Animal Disease Diagnostics Laboratory, Pullman, WA 99164, USA
3
Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID 83844, USA
*
Author to whom correspondence should be addressed.
Genes 2024, 15(6), 765; https://doi.org/10.3390/genes15060765
Submission received: 14 May 2024 / Revised: 5 June 2024 / Accepted: 8 June 2024 / Published: 11 June 2024
(This article belongs to the Special Issue Livestock Genomics, Genetics and Breeding)
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Abstract

:
Heifer conception rate to the first service (HCR1) is defined as the number of heifers that become pregnant to the first breeding service compared to the heifers bred. This study aimed to identify loci associated and gene sets enriched for HCR1 for heifers that were bred by artificial insemination (AI, n = 2829) or were embryo transfer (ET, n = 2086) recipients, by completing a genome-wide association analysis and gene set enrichment analysis using SNP data (GSEA-SNP). Three unique loci, containing four positional candidate genes, were associated (p < 1 × 10−5) with HCR1 for ET recipients, while the GSEA-SNP identified four gene sets (NES ≥ 3) and sixty-two leading edge genes (LEGs) enriched for HCR1. While no loci were associated with HCR1 bred by AI, one gene set and twelve LEGs were enriched (NES ≥ 3) for HCR1 with the GSEA-SNP. This included one gene (PKD2) shared between HCR1 AI and ET services. Identifying loci associated or enriched for HCR1 provides an opportunity to use them as genomic selection tools to facilitate the selection of cattle with higher reproductive efficiency, and to better understand embryonic loss.

1. Introduction

Approximately one third of dairy cattle in the US are culled due to infertility [1]. Infertility is most common in the embryonic stage, defined as that period between conception and gestation day 42, with embryonic loss accounting for up to 57% of all pregnancy losses [2,3,4]. The fertilization rates in Bos taurus cattle fall between 90 and 100%; however, only 60% remain pregnant by day 30 of gestation [5,6,7,8]. Pregnancy loss is financially significant to producers, resulting in USD 725 to USD 1030 per cow lost from increased inseminations, maintenance, and loss of days of milk production [9,10,11,12]. Decreasing conception rates and the significant financial impact on producers has led to research focusing on breeding and genetic-based strategies to increase fertility. The addition of genetic components associated with fertility traits such as heifer conception rate to the first service (HCR1) to producer-used selection indices has increased reproductive efficiency by 5% since being implemented in 2010 [13,14,15]. The inclusion of HCR1 in current indices does not take into account artificial insemination (AI) and embryo transfer (ET) differences, making this an area of necessary research.
Dairy cattle are primarily bred by AI; however, some animals are recipients for ET with embryos from elite parents [16]. These breeding services may include various protocols of timed hormone injections to facilitate ovulation for AI or to mimic the estrous stage of the ET donor [17,18]. Animals that are ET recipients have an in vitro fertilized embryo inserted into their uterus around day 7 of embryonic development, after which anticipated estrus, or signs of estrus, are apparent in the recipient [19].
Many studies suggest that there are important biological differences between in vitro and in vivo embryonic development. For example, there is an increased rate of blastulation for in vitro-produced embryos compared to AI or naturally serviced animals [20,21]. The use of in vitro-produced embryos and, to a lesser extent, in vivo-derived embryos, which are transferred to recipients, has been shown to lead to an increase in abnormal placentation when compared to in vivo-produced embryos that are not transferred [22]. There is also a difference in the viability of in vivo- and in vitro-produced embryos, with in vitro-produced embryos having up to 40% higher rate of mortality [23]. Many aspects can affect the success of in vitro-produced embryos; however, biological differences such as the lower likelihood of elongation and slight differences in vascularization of the yolk sac to support early embryogenesis have been shown to differ between in vivo and in vitro embryos [23,24,25]. Due to the biological differences among AI and ET breeding services, heifers bred by AI and those that served as ET recipients were analyzed separately for HCR1. This study aimed to identify loci and gene sets associated with HCR1 for animals bred by AI, or that were ET recipients. Identifying regions associated or enriched for HCR1 allows for the possible validation of fertility-related loci, positional candidate genes, and leading-edge genes. Validated loci associated with HCR1 may be incorporated into genomic selection for fertility traits and provide insights into the mechanisms of embryonic loss for heifers bred by AI or that serve as ET recipients.

2. Materials and Methods

2.1. Study Animals and Phenotype

The studied heifers (n = 5750) were from a single dairy in Idaho. This study was exempt from Institutional Animal Care and Use approval as all genotyping was done prior to study initiation and genotyping was initiated by the owner of the dairy. No additional handling or records were required for this study beyond those used by the dairy for management purposes.
Dairy Comp 305 version 23.11 (Valley Agricultural Software, Tulare, CA, USA) breeding and health records were evaluated for all heifers. Animals without complete records were removed (n = 826). Cattle that showed health events (mastitis, lameness, or metabolic issues) 21 days prior to breeding through 30 days post-breeding were removed (n = 9) from the study. The exclusion of health events was performed to minimize environmental effects of infertility and enhance the genetic effects on embryonic loss.
Pregnancy was determined by ultrasound approximately 30 days post-breeding. Heifers were bred by AI or served as a recipient for ET one time per estrous cycle. Heifers served exclusively as ET recipients (n = 2086) or were exclusively bred by AI (n = 2829) (Table 1). Cattle bred by AI were inseminated following observed estrus, while cattle receiving ET were synchronized using a double ovulation synchronization protocol. HCR1 for heifers bred by AI was 56% (1575/2829) and 47% (988/2086) for heifers that served as ET recipients. Phenotypes were binomial for HCR1 for cattle bred by AI and for cattle that were ET recipients. Heifers pregnant at gestation day 30 were coded as a 1 and heifers that were not pregnant at gestation day 30 were coded as a 0 for HCR1.

2.2. DNA Extraction and Genotyping

Heifer genotypes were obtained from Zoetis CLARIFIDE® Plus (Zoetis Precision Animal Health, Parsnippany, NJ, USA) and imputed to 634,433 SNPs using Beagle [26] and the ARS-UCD 1.2 assembly “http:/bovinegenome.org (accessed on 8 January 2024)”. The reference population for imputation consisted of approximately 4800 US Holsteins from California, New Mexico, Oregon, and Washington that were genotyped with the Illumina BovineHD BeadChip (San Diego, CA, USA). Genotyped animals shared between 29,741 and 53,594 SNPs with the BovineHD BeadChip, resulting in an imputation genotype accuracy of >95%. Imputed genotypes were then used for the genome-wide association study (GWAS) after quality control filtering.

2.3. Quality Control

Quality control filtering resulted in removing imputed genotypes if the call rate < 0.9 (n = 21,760), the individual had a minor allele frequency < 0.01 (n = 100,910), or if they failed Hardy–Weinberg equilibrium testing (p < 10 × 10−100, n = 11,100). After the completion of quality control filtering, a total of 4915 heifers and 500,663 SNPs remained for the final analysis.

2.4. Genome-Wide Association Study (GWAS)

A GWAS was performed for HCR1 using the SNP and Variation Suite (SVS) software version 8.1 (Golden Helix, Bozeman, MT, USA). The GWAS utilized the efficient mixed-model association eXpedited statistical approach by using an identity-by-state matrix (EMMAX-IBS). The statistical model for EMMAX is expressed as y   = X β + Z u + ϵ ; here, y = the vector of observed phenotypic values, X = a matrix containing fixed effects, β = regression coefficients, Z = a matrix containing the observed random effects, u = vector of random effects concerning variants of allele substitutions in the population, and ϵ = residual effects [27]. Due to the unknown inheritance of HCR1 in cattle, three genotypic models (additive, dominant, and recessive) were analyzed for each group. The additive model assumed that two minor alleles (aa) resulted in twice the effect that a single minor allele (Aa) would have on fertility. The dominant model evaluated an association with (AA/Aa) compared to (aa) alleles. The recessive model compared the presence of (AA) to (Aa/aa) genotypes. The threshold for an association was measured using the recommendation of the Wellcome Trust for unadjusted p-values [28].
A principal component analysis (PCA) was performed, showing two distinct clusters within the population (Figure 1) based on birth year. The distinct separation of the population into two clusters was subsequently used as a covariate to account for population stratification for the GWAS. To identify loci when several SNPs were associated with HCR1 that were in close proximity, a linkage disequilibrium threshold of D′ > 0.7 was used. This threshold was previously used to characterize loci for humans and cattle [29,30]. The genomic inflation factor lambda (λGC) was calculated as described by Devlin and Kathryn Roeder in 1999 [31] for heifers that were bred by AI, and was 0.99, 0.99, and 1.02, for the additive, dominant, and recessive models, respectively. The λGC for HCR1 for heifers that were ET recipients was 1.01 for the additive and dominant models and 1.03 for the recessive model.
The average haplotype size for the study population was 29.9 kb using the method of Gabriel et al. (2002) [32] within SVS. This haplotype size was used to identify positional candidate genes and SNPs for the gene set enrichment analysis using SNPs as gene proxies (GSEA-SNP). Genes that were within 29.9 kb from the associated SNP in the 5′ or 3′ directions were identified as positional candidate genes or as potential gene proxies for the GSEA-SNP. Locations of genes and SNPs were based on the bovine ARS-UCD 1.2 genome assembly.

2.5. Gene Set Enrichment Analysis–Single Nucleotide Polymorphism

The GSEA-SNP was completed using the GenGen version 1 package in R version 3.6.3 [33]. Genes were represented by the single SNP that was the most significantly associated with HCR1 within the calculated average haplotype of the gene. Approximately 34,450 genes were evaluated within gene sets of five different databases: Gene Ontology (GO), Reactome (R), Bio Carta, Kyoto Encyclopedia of Gene and Genomes (KEGG), and Protein Analysis Through Evolutionary Relationships (PANTHER). These five databases focus on collecting data on biological pathways (gene sets) in humans and other animals that have been proven to interact during various biological processes. These processes include genes involved with genetic information, cellular processes, drug processing, connection to disease, and metabolism. Although there is less information on gene processes in cattle, these gene sets serve as a model for the relationship of genes present in cattle. SNPs were then ranked by their enrichment to HCR1 in heifers receiving AI or ET services within each gene set.
A running sum statistic was used to calculate enrichment scores for every gene set based on SNP ranking for HCR1 for AI-bred and ET-recipient heifers. Enrichment score statistics were calculated similarly to Kolmogorov–Smirnov’s weighted statistics [34]. Genes in a gene set that were enriched for HCR1 resulted in higher enrichment scores, while those that were not enriched (and were ranked lower) had lower enrichment scores. Leading-edge genes are genes within a gene set that contributed positively to the peak enrichment score for HCR1. Using the GenABEL R version 3.6.3 package, permutated p-values were calculated for 10,000 phenotype-based permutation tests and used for each gene set [35,36]. The enrichment scores were then normalized (NES) to account for the number of genes within the gene set. Enriched gene sets for HCR1 were defined as those with NES ≥ 3.0, which is comparable to previous fertility studies [37,38].

3. Results

3.1. Genome-Wide Association Analysis

Three loci were associated (p < 1 × 10−5) with HCR1 for heifers that were ET recipients (Figure 2). A locus on BTA13 was associated (p = 6.6 × 10−6) with HCR1 with the additive model, BTAX was associated (p = 1 × 10−6) with HCR1 with the dominant model and BTA6 was strongly associated (p = 3.8 × 10−7) with HCR1 in the recessive model (Table 2). Positional candidate gene AFAP1 was located on the BTA6 locus, while ACE2, BMX, and PIR were found within the locus on BTAX. The SNPs associated with HCR1 in the AFAP1, ACE2, and BMX genes were identified within the intron. No loci were associated with HCR1 for additive, dominant, or recessive models in heifers bred by AI.

3.2. GSEA-SNP Results

The GSEA-SNP identified one GO gene set enriched (NES = 3.0) with HCR1 for heifers bred by AI. This gene set, sodium ion transmembrane transporter activity (GO:0015081), included 13 unique leading edge genes (Table 3). The GSEA-SNP for HCR1 for ET recipients identified four GO gene sets and 66 leading edge genes (Table 3). Of the leading edge genes, glycosylphosphatidylinositol specific phospholipase D1 (GPLD1) was found in all four enriched gene sets, and polycistin-2 (PKD2) was shared between both AI and ET phenotypes. The majority of these genes are attributed to the regulation of protein secretion, transport, and localization; however, all enriched genes had ties to fertility and embryonic loss in mammals. These enriched genes included those that have been known to previously affect fertility through the production and heightened sensitivity of essential sex steroids such as growth hormone releasing hormone (GHRH) and inhibin beta B (INHBB) [39]. While GHRH leads to the regulation of growth hormone that is essential for gametogenesis in females, INHBB is connected to the number of oocytes that remain in the ovary, as well as connected to the successful conception of pregnancy [39,40]. Genes that are responsible for the protein-coding of these hormones are imperative to study concerning fertility in dairy cattle receiving different reproductive technologies. Many other genes include immune response genes such as the interleukin gene family (IL1A, IL1B, IL10, and IL18) and the toll-like receptor genes (TLR2, TLR6, and TLR10); these are important for protection against maternal immune response as well as the increase in uterine natural killer cells that help support embryonic development in early implantation, leading to lower instances of pregnancy loss [41,42,43].

4. Discussion

The results from the GWAS and the GSEA-SNP provide a foundation for identifying areas of the genome associated with HCR1 for heifers bred by AI or that were ET recipients. The identification of loci associated with HCR1 provides opportunities to predict which cattle are more likely to experience embryonic loss to an AI or ET service, and it also provides insights into potential mechanisms of the loss through the identification of positional candidate genes, enriched gene sets, and leading edge genes. DNA variants that affect these genes may result in the reduced or over-expression of genes that alter embryonic survival.
In this study, no loci were associated with HCR1 in heifers bred by AI. In comparison, there were three loci associated with HCR1 in heifers that were ET recipients. This lack of association in AI-bred heifers occurred even though there were more AI-bred heifers in this study (n = 1575) that conceived to the first service than heifers that served as ET recipients (n = 988). Although no loci were shared (because an association was not detected) with HCR1 in AI-bred heifers, there was also a lack of sharing of leading edge genes between AI bred and ET recipients. This suggests that there may be different processes or mechanisms that fail in the establishment of pregnancy in cattle that are ET recipients than in cattle bred by AI. This is supported by previous studies that have reported that the use of ET in cattle can lead to a 10% higher instance of embryonic loss due to elevated body temperature thought to be derived from an elevated immune response from the ET recipient [44]. Other studies did not find a difference between embryonic loss in AI and ET; however, they suggested that in vivo-produced embryos from superovulation are of lower quality and could potentially decrease the successful pregnancies in ET recipients [45]. Overall, many aspects of placentation, embryonic growth, and maternal immune response differ between in vitro-produced and in vivo-derived embryos and their subsequent pregnancy successes, many of which are still under investigation.

4.1. Comparison of Positional Candidate Genes with Other Studies

Loci associated with HCR1 in heifers that were ET recipients were compared to previous studies with diverse fertility phenotypes and cattle breeds (Supplemental Table S1). The locus on BTA6 associated (p = 3.8 × 10−7) with HCR1 in the recessive model was also associated with the number of times bred (by AI) to maintain a successful pregnancy in an independent Holstein population [29]. This locus contains the protein coding of the actin filament-associated protein (AFAP1) gene that has been identified as a predictive biomarker for pre-eclampsia [46,47], as well as being expressed in the uninucleate cells of the bovine placenta at days 17 and 50 of gestation [48]. AFAP1 has also been previously found to be a leading edge gene for sub-fertile and high-fertile cattle [49].
One positional candidate gene, angiotensin-converting enzyme 2 (ACE2), associated with HCR1 in heifers receiving ET on BTAX was previously associated with female fertility. ACE2 is expressed in ovarian granulosa and theca cells in cattle [50,51], and in the human placenta [52]. The expression of ACE2 impacts normal pregnancy maintenance by establishing blood pressure patterns during pregnancy that are not detrimental to the dam through angiotensin 1–7 [53,54,55]. Some animal models suggest that ACE2 deficiency leads to undesirable placenta phenotypes, such as insufficient blood supply, placental lesions, and higher levels of placental inflammation [56,57,58,59]. Studies consisting of ACE2 knock-out mice concluded that the under-expression of the gene led to lower fetal weight and decreased intrauterine growth mainly due to poor placenta vasculature and abnormal uterine artery remodeling during gestation [60,61]. ACE2 may also contribute to female fertility during the embryonic establishment of blood supply and development through the first 30 days of gestation in women [62,63].

4.2. Comparison of Leading Edge Genes with Other Studies

The GSEA-SNP results of the ET recipients had leading edge genes that have a direct connection to fertility and embryonic survivability. One important leading edge gene associated with conception rate to the first service in ET recipients was GHRH. The release of GHRH from the hypothalamus leads to the regulation of growth hormone from the pituitary gland, which targets receptors located on the ovary for multiple functions such as steroidogenesis [64]. A study suggested that the presence of growth hormone significantly (p < 0.01) increased cleavage rate and blastocyst formation by 12% in bovine oocyte in vitro maturation [60].
Another leading edge gene of note is INHBB, which codes for the glycoprotein hormone inhibin B that was previously correlated with oocyte reserves and has a negative correlation with circulation follicle-stimulating hormone, as well as a positive correlation with estradiol levels [65]. Lower levels of this hormone are connected to lower pregnancy rates due to poor response to induced ovulation while using assisted reproductive technologies [61,66].
PKD2 has been identified as a leading edge gene encompassing both artificial insemination (AI) and embryo transfer (ET) services for a higher conception rate to the first service. The observed overlap of PKD2 across breeding types in dairy cattle further hints at its relevance to fertility beyond varying reproductive technologies. Expression studies reveal high PKD2 levels during the initial five weeks of embryonic development [67]. Investigations in mice suggest that PKD2 plays a crucial role in embryonic and placental tissue development, as PKD2 knockout mice were associated with embryonic lethality, abnormal placental vascularization, issues in smooth muscle organization, and an increased amniotic fluid accumulation [68]
Leading edge genes enriched (NES ≥ 3.0) with HCR1 were compared to genes that were expressed during embryonic development in the placenta of cattle, as reported by Davenport et al. in 2023 [48]. Heifers that received AI services had six leading edge genes (ATP1A1, ATP1B3, SCNN1A, SLC1A1, SLC6A6, and SLC8A1) that were expressed in bovine placental cell clusters at days 17, 24, 30, and 50 of gestation (Table 3). For the ET recipient heifers, there were 26 leading edge genes (ANXA4, APOA1, APOA2, ARRB1, CABP1, CASP4, CYLD, DNM1L, F2R, FKBP1B, GAPVD1, HSPA5, IFNAR1, IL18, KDELR1, KDELR2, OAZ1, PDE2A, PKIA, PRDX1, RBP4, SNAP25, TGFB1, TGFB2, TLR2, and YWHAB) and a positional candidate gene (PIR) for HCR1 that were expressed in bovine placental tissues ([48]; Supplemental Figure S1).
Four HCR1 leading edge genes for heifers receiving ET (APOA1, HSPA5, IL18, and TLR2), with the overarching function of protein transport and localization, are associated with recurring embryonic loss in women [69,70,71,72]. The apolipoprotein A-1 (APOA1) gene influences the production of estrogen and progesterone, which are crucial for pregnancy [73]. The corpus luteum produces progesterone to maintain pregnancy until this role can be replaced by the production of progesterone by the placenta. The decreasing levels of progesterone allow for levels of estradiol to rise and cyclicity to continue [74]. APOA1 was expressed in endothelial and mesenchyme cell clusters at various time points in early pregnancy [48]. Expression in these cell types can be connected to the initial start of placentation as the endothelial cells within the uterus go through the mass remodeling and enhanced vascularization needed to supply sufficient blood flow [75]. In cattle, the epithelial cells also have a period of transition where they gain mesenchymal cell characteristics, which is often referred to as epithelial to mesenchymal transition [76,77]. This stage is thought to support a more ideal uterine environment for implantation and placentation, with the cells showing high proliferation and increased immune characteristics to protect the embryo [76]. When the presence of an embryo is not detected, these transitioned mesenchyme cells transition back to epithelial cells, and are commonly associated with the regeneration and proliferation of the uterus [77]. High levels of expressed APOA1 are associated with implantation failure [67,78,79,80]. High expression levels of APOA1 serve as a biomarker for poor endometrial environment and as a measure of success in vitro-produced embryos, as 76% of early miscarriage cases had high APOA1 levels when compared to controls [81]. This downregulation of APOA1 levels suggests the endometrial preparation of an environment conducive to pregnancy. This conducive environment allows for a lower likelihood of embryonic loss due to implantation failure or inflammation in the uterus, and allowing cattle to have higher chances of conception to the first service [81].
The heat shock protein family member A Hsp70 5 (HSPA5) gene (also referred to as GRP78/BiP) is a leading edge gene that has been associated with recurrent pregnancy loss in women. The HSPA5 protein resides in the endoplasmic reticulum, where it facilitates cellular protein folding [82]. The HSPA5 gene is expressed in bovine uninucleate and binucleate cell types during early pregnancy, such as days 17 through 30 of gestation [48]. This expression in both the uninucleate and binucleate cells suggests the importance of the gene in the production, migration, and success of placentation through the increased development of these cell types in early gestation [83,84]. The establishment of the placenta is crucial for cattle to remain pregnant and not suffer from an embryonic loss event. The gene expression of HSPA5 within the endometrium has been reported as negatively correlated with estrogen levels, and an increase in embryo implantation failure in mice [72,82,85]. The expression of HSPA5 was detected in the endometrial luminal epithelium cells at day 50 of gestation [48]. This representation of HSPA5 in the reproductive tissues along with studies suggesting high levels of implantation failure make this gene vital, as high levels of implantation failure are synonymous with high levels of embryonic loss.
In the embryonic stage of pregnancy, interleukin 18 (IL18) is primarily secreted within the endometrium and placenta [86,87]. The secretion of IL18 within the reproductive tract initiates the expression of tumor necrosis factor alpha and uterine natural killer cells in in vitro fertilization [87,88]. In cattle, the downregulation of IL18 around day 16 of gestation showed a –2-fold change, suggesting a level of immune system protection in cattle and possibly reducing instances of embryonic loss [41]. This suggested immune response is supported by single-cell data showing expression in macrophage cells in the cattle placenta [48]. That an interleukin would be important in pregnancy establishment and maintenance is supported by studies on interleukin 1 beta (IL1B), another associated leading edge gene, and interleukin 15 (IL15), for which over-expression leads to miscarriage and implantation failure [89]. Uterine natural killer cells also express another enriched gene toll-like receptor 2 (TLR2). This leading edge gene may be involved with embryonic survival through the interaction of uterine natural killer cells at the implantation site and in the placenta [42,43]. TLR2, similar to IL18, was found to be expressed within the macrophage cells of the placenta [48]. Macrophages and uterine natural killer cells are the most common immune cells present at the placentation site in humans, accounting for up to 90% of all leukocytes [90,91]. While the exact breakdown of the placenta in cattle has not yet been determined, many human studies have focused on the interaction of uterine natural killer cells and macrophages and their functions in successful implantation. The TLR2 gene is also expressed within the cerebellum during early embryonic development; however, its function within the nervous system is not well understood [92].
Leading edge genes enriched for HCR1 in ET heifer recipients and previously associated with fertility in mice and cattle include CASP4, PKD2, RBP4, and TGFB1. Caspase 4 (CASP4) is expressed in bovine mesenchyme cell clusters at days 17 and 24 of gestation, as well as in placental macrophage cell clusters at days 30 and 50 of gestation [48]. This expression is potentially associated with successful implantation, embryonic development, and maternal immune response [76].
The retinol-binding protein 4 (RBP4) gene is expressed in the mesenchymal cell cluster of the developing bovine placenta across gestation [48]. Uninucleate cells make up 80% of the trophoblast in the chorionic villi, with the other 20% being binucleate cells [93]. Binucleate cells are more well-known, as they fuse to the uterine epithelium during implantation, transport molecules from the fetus to the mother, as well as aiding in steroidogenesis [94,95,96]. The regeneration and presence of binucleate cells are essential to the maintenance of pregnancy in cattle and other ruminants as they establish the fetal–maternal complex [96]. RBP4 is associated with conceptus elongation, HCR1, cow conception, and daughter pregnancy rate in Holstein heifers [97,98]. The onset of conceptus elongation resulted in a 94% increase in RBP4 expression [99]. It is well known that implantation and conceptus elongation are processes prone to failure within dairy cattle pregnancy establishment [100], making this gene a good candidate for further research regarding its role in embryonic loss.
Transforming growth factor beta 1 (TGFB1) has an array of functions, especially within the reproductive tract [101]. TGFB1 is differentially upregulated by a 6.7 log2-fold change between day 17 and day 24 of gestation in uninucleate cells [48]. The regeneration of binucleate cells by uninucleate cells is essential to the establishment and maintenance of pregnancy, and therefore implies the necessity of these cell types for embryonic survivability [95,96]. The TGFB family of genes is essential to immunoregulation during pregnancy, embryo implantation, and placental development [101,102]. In particular, TGFB1 is connected to embryonic loss, with one study reporting up to 100% embryonic loss at the pre-implantation stage (day 2–3 of gestation) of pregnancy when the gene was knocked out in mice [103]. In addition, 80% of TGFB1 null mice displayed embryonic loss due to implantation failure [104]. While the actual role of TGFB1 in relation to pregnancy loss in cattle is not fully appreciated, more investigation is warranted to identify the mechanisms associated with TGFB1 in embryonic loss.

5. Conclusions

The GWAS identified no loci associated with HCR1 in AI-bred heifers, and three loci and four positional candidate genes associated with HCR1 in heifers that were ET recipients. The GSEA-SNP identified one gene set and 15 leading edge genes enriched for HCR1 in AI-bred heifers, and four gene sets and 142 leading edge genes enriched for HCR1 in Holstein heifers that were ET recipients. As with the results from the GSEA-SNP, there are clearly differences between genomic regions associated or enriched with HCR1 in heifers bred by AI and those that were ET recipients. The genomic results are consistent with reports of the physiological differences that cause embryonic loss in AI and ET pregnancies. Overall, one locus on BTA6 associated with HCR1 in ET recipients was validated in previous cattle studies to be associated with the number of times inseminated to successful pregnancy in Holsteins [29]. The 26 leading edge genes associated with HCR1 ET recipients that were supported by single-cell data also validate these genes’ presence during the embryonic stage of gestation in cattle [48]. Additional support for the genomic findings is provided by the fact that many of the positional candidate and leading edge genes have been associated with embryonic loss in mice and humans.
Many of the discovered genes associated with embryonic loss events of heifers receiving AI or ET encompassed functions of hormone production, hormone regulation, and immune response. Normal hormone secretion and function is essential for the maintenance of pregnancy [39,40,94]. Genes associated with immune response are also essential to embryonic survivability, as many allow for protection from the maternal aspect that can lead to embryonic death [41,42,43]. It is also imperative that gene expression not only protect the developing embryo, but also allow for the development of the innate immune response [105].
In Holstein dairy cattle, embryonic loss is a common reason for culling causing producers’ substantial financial loss. Identifying regions of the genome that are associated with embryonic loss in heifers bred by AI or for heifers that are ET recipients allows for the potential use of genomic selection in making selection and breeding decisions. This study suggests that one locus and 44 genes are important in embryonic loss and could be used for the selection for HCR1.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15060765/s1, Table S1: Cattle fertility studies that were compared with the current study to identify shared loci, positional candidate genes or leading edge genes [106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127]; Figure S1: Bovine placenta single-cell gene expression plots [48] of leading edge genes associated with HCR1 in ET-recipient heifers.

Author Contributions

Concept and experimental design, H.L.N.; methodology, H.L.N., B.M.M., K.M.D. and J.N.K.; software, J.N.K. and V.C.K.; validation, J.N.K., E.M.S. and V.C.K.; formal analysis, V.C.K., E.M.S. and H.L.N.; investigation, V.C.K., K.M.D. and H.L.N.; resources, K.M.D., B.M.M. and H.L.N.; data curation, J.N.K., K.M.D. and V.C.K.; writing—original draft preparation, V.C.K. and H.L.N.; writing—review and editing, J.N.K., K.M.D., E.M.S., V.C.K., B.M.M. and H.L.N.; visualization, K.M.D., V.C.K. and H.L.N.; supervision, H.L.N.; project administration, H.L.N.; funding acquisition, B.M.M. and H.L.N. All authors have read and agreed to the published version of the manuscript.

Funding

This project is supported by Agriculture and Food Research Initiative Competitive Grant no. 2023-67015-39524 from the USDA National Institute of Food and Agriculture.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to acknowledge the Idaho dairy for their assistance in collecting records for phenotyping as well as Zoetis for the delivery of the raw genotypes.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Ansari-Lari, M.; Mohebbi-Fani, M.; Rowshan-Ghasrodashti, A. Causes of culling in dairy cows and its relation to age at culling and interval from calving in Shiraz, Southern Iran. Vet. Res. Forum. 2012, 3, 233–237. [Google Scholar] [PubMed] [PubMed Central]
  2. Reece, W.O.; Erickson, H.H.; Goff, J.P.; Uemura, E.E. Dukes’ Physiology of Domestic Animals, 13th ed.; Wiley Blackwell.: Hoboken, NJ, USA, 2015. [Google Scholar]
  3. Inskeep, E.K.; Dailey, R.A. Embryonic death in cattle. Vet. Clin. N. Am. Food Anim. Pract. 2005, 21, 437–461. [Google Scholar] [CrossRef] [PubMed]
  4. Dunne, L.D.; Diskin, M.G.; Sreenan, J.M. Embryo and foetal loss in beef heifers between day 14 of gestation and full term. Anim. Reprod. Sci. 2000, 58, 39–44. Available online: https://www.sciencedirect.com/science/article/pii/S0378432099000883 (accessed on 7 August 2023). [CrossRef] [PubMed]
  5. Diskin, M.G.; Sreenan, J.M. Fertilization and embryonic mortality rates in beef heifers after artificial insemination. Reproduction 1980, 59, 463–468. [Google Scholar] [CrossRef] [PubMed]
  6. Diskin, M.G.; Parr, M.H.; Morris, D.G. Embryo death in cattle: An update. Reprod. Fertil. Dev. 2012, 24, 244. [Google Scholar] [CrossRef] [PubMed]
  7. Reese, S.T.; Franco, G.A.; Poole, R.K.; Hood, R.; Fernadez Montero, L.; Oliveira Filho, R.V.; Cooke, R.F.; Pohler, K.G. Pregnancy loss in beef cattle: A meta-analysis. Anim. Reprod. Sci. 2020, 212, 106251. [Google Scholar] [CrossRef] [PubMed]
  8. Albaaj, A.; Durocher, J.; LeBlanc, S.J.; Dufour, S. Meta-analysis of the incidence of pregnancy losses in dairy cows at different stages to 90 days of gestation. JDS Commun. 2022, 4, 144–148. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039243/#:~:text=studies%20were%20included.-,Pregnancy%20losses%20averaged%2027%25%2C%2013%25%2C%207%25%2C,pregnancy%20losses%20in%20dairy%20cows (accessed on 15 August 2023). [CrossRef] [PubMed]
  9. Enting, H.; Kooij, D.; Dijkhuizen, A.A.; Huirne, R.B.M.; Noordhuizen-Stassen, E.N. Economic losses due to clinical lameness in dairy cattle. Livest. Prod. Sci. 1998, 49, 259–267. Available online: https://www.sciencedirect.com/science/article/pii/S0301622697000511 (accessed on 3 September 2023). [CrossRef]
  10. Norman, H.D.; Wright, J.R.; Hubbard, S.M.; Miller, R.H.; Hutchison, J.L. Reproductive status of Holstein and Jersey cows in the United States. J. Dairy Sci. 2010, 92, 3517–3528. Available online: https://www.sciencedirect.com/science/article/pii/S002203020970671X (accessed on 18 September 2023). [CrossRef] [PubMed]
  11. Chebel, R.C.; Santos, J.E.P.; Reynolds, J.P.; Cerri, R.L.A.; Juchem, S.O.; Overton, M. Factors affecting conception rate after artificial insemination and pregnancy loss in lactating dairy cows. Anim. Reprod. Sci. 2004, 84, 239–255. Available online: https://www.sciencedirect.com/science/article/pii/S0378432004000193 (accessed on 15 August 2023). [CrossRef]
  12. De Vries, A. Economic value of pregnancy in dairy cattle. J. Dairy Sci. 2006, 89, 3876–3885. [Google Scholar] [CrossRef]
  13. CDCB Cattle on Dairy Cattle Breeding. The Council of Dairy Cattle Breeding. 2024. Available online: https://webconnect.uscdcb.com/#/summary-stats/genetic-trend (accessed on 1 October 2023).
  14. CDCB Cattle on Dairy Cattle Breeding. Merit Selection Indices. 28 October 2022. Available online: https://uscdcb.com/merit-selection/ (accessed on 1 October 2023).
  15. Cole, J.B.; VanRaden, P. Symposium review: Possibilities in an age of genomics: The future of selection indices. J. Dairy Sci. 2017, 101, 3686–3701. Available online: https://www.sciencedirect.com/science/article/pii/S0022030217309694 (accessed on 18 September 2023). [CrossRef]
  16. Morrell, J.M. Artificial insemination: Current and future trends. In Artificial Insemination in Farm Animals; INTECH Open Access Publisher: London, UK, 2011. [Google Scholar]
  17. Selk, G. Embryonic Transfer in Cattle. Oklahoma Cooperative Extension Service, ANSI-3158. 2002. Available online: https://shareok.org/bitstream/handle/11244/49938/oksd_ansi_3158_2002-09.pdf?sequence=1 (accessed on 30 January 2024).
  18. Rutledge, J.J. Use of embryo transfer and IVF to bypass effects of heat stress. Theriogenology 2001, 55, 105–111. [Google Scholar] [CrossRef] [PubMed]
  19. Hansen, P.J. The incompletely fulfilled promise of embryo transfer in cattle-why aren’t pregnancy rates greater and what can we do about it? J. Anim. Sci. 2020, 98, skaa288. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  20. Goossens, K.; Van Soom, A.; Van Poucke, M.; Vandaele, L.; Vandesompele, J.; Van Zeveren, A.; Peelman, L.J. Identification and expression analysis of genes associated with bovine blastocyst formation. BMC Dev. Biol. 2007, 7, 64. [Google Scholar] [CrossRef]
  21. Lee, Y.S.; Latham, K.; Sapienza, C.; Shore, S.K.; Litvin, J. Maternal Determinants of Oocyte and Embryo Quality. Ph.D. Dissertation, Temple University, Philadelphia, PA, USA, 2011. Available online: https://scholarshare.temple.edu/bitstream/handle/20.500.12613/577/Lee_temple_0225E_10588.pdf?sequence=1&isAllowed=y (accessed on 10 April 2024).
  22. Zhao, L.; Zheng, X.; Liu, J.; Zheng, R.; Yang, R.; Wang, Y.; Sun, L. PPAR signaling pathway in the first trimester placenta from in vitro fertilization and embryo transfer. Biomed. Pharmacother. 2019, 118, 109251. [Google Scholar] [CrossRef] [PubMed]
  23. Ealy, A.D.; Wooldridge, L.K.; McCoski, S.R. Board invited review: Post-transfer consequences of in vitro-produced embryos in cattle. J. Anim. Sci. 2019, 97, 2555–2568. [Google Scholar] [CrossRef]
  24. Lonergan, P.; Woods, A.; Fair, T.; Carter, F.; Rizos, D.; Ward, F.; Quinn, K.; Evans, A. Effect of embryo source and recipient progesterone environment on embryo development in cattle. Reprod. Fertil. Dev. 2007, 19, 861–868. [Google Scholar] [CrossRef]
  25. Mess, A.M.; Carreira, A.C.; Marinovic de Oliveira, C.; Fratini, P.; Favaron, P.O.; da Barreto, R.; Pfarrer, C.; Meirelles, F.V.; Miglino, M.A. Vascularization and VEGF expression altered in bovine yolk sacs from IVF and NT technologies. Theriogenology 2017, 87, 290–297. [Google Scholar] [CrossRef] [PubMed]
  26. Browning, B.L.; Browning, S.R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 2016, 98, 116–126. [Google Scholar] [CrossRef]
  27. Kang, H.M.; Sul, J.H.; Service, S.K.; Zaitlen, N.A.; Kong, S.; Freimer, N.B.; Sabatti, C.; Eskin, E. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 2010, 42, 348–354. [Google Scholar] [CrossRef] [PubMed]
  28. The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls. Nature 2007, 447, 661–678. [Google Scholar] [CrossRef] [PubMed]
  29. Galliou, J.M.; Kiser, J.N.; Oliver, K.F.; Seabury, C.M.; Moraes, J.G.N.; Burns, G.W.; Spencer, T.E.; Dalton, J.; Neibergs, H.L. Identification of Loci and Pathways Associated with Heifer Conception Rate in U.S. Holsteins. Genes 2020, 11, 767. [Google Scholar] [CrossRef] [PubMed]
  30. Weiss, K.M.; Clark, A.G. Linkage disequilibrium and the mapping of complex human traits. Trends Genet. 2002, 18, 19–24. [Google Scholar] [CrossRef] [PubMed]
  31. Devlin, B.; Roeder, K. Genomic Control for Association Studies. Biometrics 1999, 55, 997–1004. [Google Scholar] [CrossRef] [PubMed]
  32. Gabriel, S.B.; Schaffner, S.F.; Nguyen, H.; Moore, J.M.; Roy, J.; Blumenstiel, B.; Higgins, J.; DeFelice, M.; Lochner, A.; Faggart, M.; et al. The structure of haplotype blocks in the human genome. Science 2002, 296, 2225–2229. [Google Scholar] [CrossRef] [PubMed]
  33. Wang, K.; Li, M.; Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 2010, 11, 843–854. [Google Scholar] [CrossRef] [PubMed]
  34. Charmpi, K.; Ycart, B. Weighted Kolmogorov Smirnov testing: An alternative for Gene Set Enrichment Analysis. Stat. Appl. Genet. Mol. Biol. 2015, 14, 279–293. [Google Scholar] [CrossRef] [PubMed]
  35. Aulchenko, Y.S.; Ripke, S.; Isaacs, A.; van Duijn, C.M. Genabel: An R library for genome-wide association analysis. Bioinformatics 2007, 23, 1294–1296. [Google Scholar] [CrossRef]
  36. Karssen, L.C.; van Duijn, C.M.; Aulchenko, Y.S. The GenABEL Project for statistical genomics. F1000Research 2016, 5, 914. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  37. Neupane, M.; Geary, T.W.; Kiser, J.N.; Burns, G.W.; Hansen, P.J.; Spencer, T.E.; Neibergs, H.L. Loci and pathways associated with uterine capacity for pregnancy and fertility in beef cattle. PLoS ONE 2017, 12, e0188997. [Google Scholar] [CrossRef] [PubMed]
  38. Oliver, K.F.; Wahl, A.M.; Dick, M.; Toenges, J.A.; Kiser, J.N.; Galliou, J.M.; Moraes, J.G.N.; Burns, G.W.; Dalton, J.; Spencer, T.E.; et al. Genomic Analysis of Spontaneous Abortion in Holstein Heifers and Primiparous Cows. Genes 2019, 10, 954. [Google Scholar] [CrossRef]
  39. Tinkanen, H.; Bläuer, M.; Laippala, P.; Tuohimaa, P.; Kujansuu, E. Correlation between serum inhibin B and other indicators of the ovarian function. Eur. J. Obstet. Gynecol. Reprod. Biol. 2001, 94, 109–113. [Google Scholar] [CrossRef] [PubMed]
  40. Magon, N.; Agrawal, S.; Malik, S.; Babu, K. Growth hormone in the management of female infertility. Indian J. Endocrinol. Metab. 2011, 15, 246. [Google Scholar] [CrossRef] [PubMed]
  41. Mansouri-Attia, N.; Oliveira, L.J.; Forde, N.; Fahey, A.G.; Browne, J.A.; Roche, J.F.; Sandra, O.; Reinaud, P.; Lonergan, P.; Fair, T. Pivotal role for monocytes/macrophages and dendritic cells in maternal immune response to the developing embryo in cattle1. Biol. Reprod. 2012, 87, 121. [Google Scholar] [CrossRef] [PubMed]
  42. Kämmerer, U.; Eggert, A.O.; Kapp, M.; McLellan, A.D.; Geijtenbeek, T.B.H.; Dietl, J.; van Kooyk, Y.; Kämpgen, E. Unique appearance of proliferating antigen-presenting cells expressing DC-sign (CD209) in the decidua of early human pregnancy. Am. J. Pathol. 2003, 162, 887–896. [Google Scholar] [CrossRef] [PubMed]
  43. Lin, Y.; Nakashima, A.; Shima, T.; Zhou, X.; Saito, S. Toll-like receptor signaling in uterine natural killer cells—Role in embryonic loss. J. Reprod. Immunol. 2009, 83, 95–100. [Google Scholar] [CrossRef] [PubMed]
  44. Demetrio, D.G.B.; Santos, R.M.; Demetrio, C.G.B.; Vasconcelos, J.L.M. Factors affecting conception rates following artificial insemination or embryo transfer in lactating Holstein cows. J. Dairy Sci. 2007, 90, 5073–5082. [Google Scholar] [CrossRef] [PubMed]
  45. Sartori, R.; Gümen, A.; Guenther, J.N.; Souza, A.H.; Caraviello, D.Z.; Wiltbank, M.C. Comparison of artificial insemination versus embryo transfer in lactating dairy cows. Theriogenology 2006, 65, 1311–1321. [Google Scholar] [CrossRef] [PubMed]
  46. Ensembl, E.-E. Gene: AFAP1. Bos_taurus—Ensembl Genome Browser 111. 2024. Available online: http://useast.ensembl.org/Bos_taurus/Gene/Summary?db=core%3Bg (accessed on 14 March 2024).
  47. Zhang, S.; Zou, Y.; Tang, X.; Zhang, Y.; Yang, N.; Xu, K.; Xu, Y. Silencing of AFAP1-AS1 lncrna impairs cell proliferation and migration by epigenetically promoting DUSP5 expression in pre-eclampsia. J. Cell. Biochem. 2021, 122, 1506–1516. [Google Scholar] [CrossRef]
  48. Davenport, K.M.; Ortega, M.S.; Liu, H.; O’Neil, E.V.; Kelleher, A.M.; Warren, W.C.; Spencer, T.E. Single-nuclei RNA sequencing (snrna-seq) uncovers trophoblast cell types and lineages in the mature bovine placenta. Proc. Natl. Acad. Sci. USA 2023, 120, e2221526120. [Google Scholar] [CrossRef] [PubMed]
  49. Walker, C.G.; Littlejohn, M.D.; Mitchell, M.D.; Roche, J.R.; Meier, S. Endometrial gene expression during early pregnancy differs between fertile and subfertile dairy cow strains. Physiol. Genom. 2012, 44, 47–58. [Google Scholar] [CrossRef] [PubMed]
  50. Barreta, M.H.; Gasperin, B.G.; Ferreira, R.; Rovani, M.; Pereira, G.R.; Bohrer, R.C.; de Oliveira, J.F.; Goncalves, P.B. The components of the angiotensin-(1-7) system are differentially expressed during follicular wave in cattle. J. Renin Angiotensin Aldosterone Syst. 2015, 16, 275–283. [Google Scholar] [CrossRef] [PubMed]
  51. Santos, J.; Ferreira, R.; Gasperin, B.G.; Siqueira, L.C.; de Oliveira, J.F.; Santos, R.A.; Reis, A.M.; Goncalves, P.B. Molecular characterization and regulation of the angiotensin-converting enzyme type 2/angiotensin-(1-7)/MAS receptor axis during the ovulation process in cattle. J. Renin Angiotensin Aldosterone Syst. 2012, 13, 91–98. [Google Scholar] [CrossRef] [PubMed]
  52. Valdes, G.; Neves, L.A.; Anton, L.; Corthorn, J.; Chacon, C.; Germain, A.M.; Merrill, D.C.; Ferrario, C.M.; Sarao, R.; Penninger, J.; et al. Distribution of angiotensin-(1-7) and ACE2 in human placentas of normal and pathological pregnancies. Placenta 2006, 27, 200–207. [Google Scholar] [CrossRef] [PubMed]
  53. Vickers, C.; Hales, P.; Kaushik, V.; Dick, L.; Gavin, J.; Tang, J.; Godbout, K.; Parsons, T.; Baronas, E.; Hsieh, F.; et al. Hydrolysis of biological peptides by human angiotensin-converting enzyme-related carboxypeptidase. J. Biol. Chem. 2002, 277, 14838–14843. [Google Scholar] [CrossRef] [PubMed]
  54. Levy, A.; Yagil, Y.; Bursztyn, M.; Barkalifa, R.; Scharf, S.; Yagil, C. ACE2 expression and activity are enhanced during pregnancy. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2008, 295, R1953–R1961. [Google Scholar] [CrossRef] [PubMed]
  55. Neves, L.A.; Stovall, K.; Joyner, J.; Valdes, G.; Gallagher, P.E.; Ferrario, C.M.; Merrill, D.C.; Brosnihan, K.B. ACE2 and ANG-(1-7) in the rat uterus during early and late gestation. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2008, 294, R151–R161. [Google Scholar] [CrossRef] [PubMed]
  56. Bharadwaj, M.S.; Strawn, W.B.; Groban, L.; Yamaleyeva, L.M.; Chappell, M.C.; Horta, C.; Atkins, K.; Firmes, L.; Gurley, S.B.; Brosnihan, K.B. Angiotensin-converting enzyme 2 deficiency is associated with impaired gestational weight gain and fetal growth restriction. Hypertension 2011, 58, 852–858. [Google Scholar] [CrossRef] [PubMed]
  57. Azinheira Nobrega Cruz, N.; Stoll, D.; Casarini, D.E.; Bertagnolli, M. Role of ace2 in pregnancy and potential implications for COVID-19 susceptibility. Clin. Sci. 2021, 135, 1805–1824. [Google Scholar] [CrossRef]
  58. Ghadhanfar, E.; Alsalem, A.; Al-Kandari, S.; Naser, J.; Babiker, F.; Al-Bader, M. The role of ACE2, angiotensin-(1–7) and MAS1 receptor axis in glucocorticoid-induced intrauterine growth restriction. Reprod. Biol. Endocrinol. 2017, 15, 97. [Google Scholar] [CrossRef] [PubMed]
  59. Yamaleyeva, L.M.; Pulgar, V.M.; Lindsey, S.H.; Yamane, L.; Varagic, J.; McGee, C.; daSilva, M.; Lopes Bonfa, P.; Gurley, S.B.; Brosnihan, K.B. Uterine artery dysfunction in pregnant Ace2 knockout mice is associated with placental hypoxia and reduced umbilical blood flow velocity. Am. J. Physiol.-Endocrinol. Metab. 2015, 309, E84–E94. [Google Scholar] [CrossRef] [PubMed]
  60. Izadyar, F.; Colenbrander, B.; Bevers, M.M. In vitro maturation of bovine oocytes in the presence of growth hormone accelerates nuclear maturation and promotes subsequent embryonic development. Mol. Reprod. Dev. 1996, 45, 372–377. [Google Scholar] [CrossRef]
  61. Seifer, D.B.; Lambert-Messerlian, G.; Hogan, J.W.; Gárdiner, A.C.; Blazar, A.S.; Berk, C.A. Day 3 serum inhibin-B is predictive of assisted reproductive technologies outcome. Fertil. Steril. 1997, 67, 110–114. [Google Scholar] [CrossRef] [PubMed]
  62. Ata, B.; Vermeulen, N.; Mocanu, E.; Gianaroli, L.; Lundin, K.; Rautakallio-Hokkanen, S.; Tapanainen, J.S.; Veiga, A. SARS-CoV-2, fertility and assisted reproduction. Hum. Reprod. Update 2022, 29, 177–196. [Google Scholar] [CrossRef] [PubMed]
  63. Weatherbee, B.A.; Glover, D.M.; Zernicka-Goetz, M. Expression of SARS-CoV-2 receptor ACE2 and the protease TMPRSS2 suggests susceptibility of the human embryo in the first trimester. Open Biol. 2020, 10, 200162. [Google Scholar] [CrossRef]
  64. Hull, K.; Harvey, S. Growth hormone: Roles in female reproduction. J. Endocrinol. 2001, 168, 1–23. [Google Scholar] [CrossRef]
  65. Poretsky, L.; Cataldo, N.A.; Rosenwaks, Z.; Giudice, L.C. The insulin-related ovarian regulatory system in health and disease. Endocr. Rev. 1999, 20, 535–582. [Google Scholar] [CrossRef]
  66. Hall, J.E.; Weltand, C.K.; Cramer, D.W. Inhibin A and inhibin B reflect ovarian function in assisted reproduction but are less useful at predicting outcome. Hum. Reprod. 1999, 14, 409–415. [Google Scholar] [CrossRef]
  67. Chauvet, V.; Qian, F.; Boute, N.; Cai, Y.; Phakdeekitacharoen, B.; Onuchic, L.F.; Attié-Bitach, T.; Guicharnaud, L.; Devuyst, O.; Germino, G.G.; et al. Expression of PKD1 and PKD2 transcripts and proteins in human embryo and during normal kidney development. Am. J. Pathol. 2002, 160, 973–983. [Google Scholar] [CrossRef]
  68. Garcia-Gonzalez, M.A.; Outeda, P.; Zhou, Q.; Zhou, F.; Menezes, L.F.; Qian, F.; Huso, D.L.; Germino, G.G.; Piontek, K.B.; Watnick, T. PKD1 and Pkd2 are required for normal placental development. PLoS ONE 2010, 5, e0012821. [Google Scholar] [CrossRef] [PubMed]
  69. Amjadi, F.; Aflatoonian, R.; Javanmard, S.H.; Saifi, B.; Ashrafi, M.; Mehdizadeh, M. Apolipoprotein A1 as a novel anti-implantation biomarker in polycystic ovary syndrome: A case-control study. J. Res. Med. Sci. 2015, 20, 1039–1045. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  70. Lin, Y.; Wang, H.; Wang, W.; Zeng, S.; Zhong, Y.; Li, D.-J. Prevention of embryo loss in non-obese diabetic mice using adoptive ITGA2+ISG20+ natural killer-cell transfer. Reproduction 2009, 137, 943–955. [Google Scholar] [CrossRef] [PubMed]
  71. Löb, S.; Amann, N.; Kuhn, C.; Schmoeckel, E.; Wöckel, A.; Zatizehni, A.; Kaltofen, T.; Keckstein, S.; Mumm, J.-N.; Meister, S.; et al. Interleukin-1 β is significantly upregulated in the decidua of spontaneous and recurrent miscarriage placentas. J. Reprod. Immunol. 2021, 144, 103283. [Google Scholar] [CrossRef] [PubMed]
  72. Reese, J.; Das, S.K.; Paria, B.C.; Lim, H.; Song, H.; Matsumoto, H.; Knudtson, K.L.; DuBois, R.N.; Dey, S.K. Global gene expression analysis to identify molecular markers of uterine receptivity and embryo implantation. J. Biol. Chem. 2001, 276, 44137–44145. [Google Scholar] [CrossRef] [PubMed]
  73. Miller, W.L.; Bose, H.S. Early steps in steroidogenesis: Intracellular Cholesterol trafficking. J. Lipid Res. 2011, 52, 2111–2135. [Google Scholar] [CrossRef] [PubMed]
  74. Skarzynski, D.; Ferreira-Dias, G.; Okuda, K. Regulation of luteal function and corpus luteum regression in cows: Hormonal control, immune mechanisms and intercellular communication. Reprod. Domest. Anim. 2008, 43 (Suppl. S2), 57–65. [Google Scholar] [CrossRef] [PubMed]
  75. Shao, X.; Yu, W.; Yang, Y.; Wang, F.; Yu, X.; Wu, H.; Ma, Y.; Cao, B.; Wang, Y.-L. The mystery of the life tree: The Placentas. Biol. Reprod. 2022, 107, 301–316. [Google Scholar] [CrossRef] [PubMed]
  76. Calle, A.; López-Martín, S.; Monguió-Tortajada, M.; Borràs, F.E.; Yáñez-Mó, M.; Ramírez, M.Á. Bovine endometrial MSC: Mesenchymal to epithelial transition during luteolysis and tropism to implantation niche for immunomodulation. Stem Cell Res. Ther. 2019, 10, 23. [Google Scholar] [CrossRef]
  77. Hugo, H.; Ackland, M.L.; Blick, T.; Lawrence, M.G.; Clements, J.A.; Williams, E.D.; Thompson, E.W. Epithelial—Mesenchymal and mesenchymal—Epithelial transitions in carcinoma progression. J. Cell. Physiol. 2007, 213, 374–383. [Google Scholar] [CrossRef]
  78. Brosens, J.J.; Hodgetts, A.; Feroze-Zaidi, F.; Sherwin, J.R.; Fusi, L.; Salker, M.S.; Higham, J.; Rose, G.L.; Kajihara, T.; Young, S.L.; et al. Proteomic analysis of endometrium from fertile and infertile patients suggests a role for apolipoprotein A-I in embryo implantation failure and endometriosis. Mol. Hum. Reprod. 2009, 16, 273–285. [Google Scholar] [CrossRef] [PubMed]
  79. Chen, X.; Scholl, T.O.; Stein, T.P.; Steer, R.A.; Williams, K.P. Maternal Circulating Lipid Profile during Early Pregnancy: Racial/Ethnic Differences and Association with Spontaneous Preterm Delivery. Nutrients 2017, 9, 19. [Google Scholar] [CrossRef]
  80. Rødgaard, T.; Heegaard, P.M.H.; Callesen, H. Non-invasive assessment of in-vitro embryo quality to improve transfer success. Reprod. BioMed. Online 2015, 31, 585–592. [Google Scholar] [CrossRef]
  81. Verma, P.; Nair, R.R.; Singh, S.; Rajender, S.; Khanna, A.; Jha, R.K.; Singh, K. High Level of APOA1 in Blood and Maternal Fetal Interface Is Associated with Early Miscarriage. Reprod. Sci. 2019, 26, 649–656. [Google Scholar] [CrossRef]
  82. Guzel, E.; Basar, M.; Ocak, N.; Arici, A.; Kayisli, U.A. Bidirectional interaction between unfolded-protein-response key protein HSPA5 and estrogen signaling in human endometrium1. Biol. Reprod. 2011, 85, 121–127. [Google Scholar] [CrossRef] [PubMed]
  83. Koshi, K.; Suzuki, Y.; Nakaya, Y.; Imai, K.; Hosoe, M.; Takahashi, T.; Kizaki, K.; Miyazawa, T.; Hashizume, K. Bovine trophoblastic cell differentiation and binucleation involves enhanced endogenous retrovirus element expression. Reprod. Biol. Endocrinol. 2012, 10, 41. [Google Scholar] [CrossRef] [PubMed]
  84. Wooding, F.B.P. The synepitheliochorial placenta of ruminants: Binucleate cell fusions and hormone production. Placenta 1992, 13, 101–113. [Google Scholar] [CrossRef]
  85. Jee, B.; Dhar, R.; Singh, S.; Karmakar, S. Heat shock proteins and their role in pregnancy: Redefining the function of “Old rum in a new bottle”. Front. Cell Dev. Biol. 2021, 9, 648463. [Google Scholar] [CrossRef]
  86. Löb, S.; Ochmann, B.; Ma, Z.; Vilsmaier, T.; Kuhn, C.; Schmoeckel, E.; Herbert, S.-L.; Kolben, T.; Wöckel, A.; Mahner, S.; et al. The role of interleukin-18 in recurrent early pregnancy loss. J. Reprod. Immunol. 2021, 148, 103432. [Google Scholar] [CrossRef]
  87. Yoshino, O.; Osuga, Y.; Yano, T.; Koga, K.; Tsutsumi, O.; Taketani, Y. Evidence for the presence of interleukin (IL)-18, IL-18 receptor and IL-18 binding protein expression in the human endometrium. Fertil. Steril. 2001, 76, S63. [Google Scholar] [CrossRef]
  88. Lédée-Bataille, N.; Dubanchet, S.; Coulomb-L’hermine, A.; Durand-Gasselin, I.; Frydman, R.; Chaouat, G. A new role for Natural Killer Cells, interleukin (IL)-12, and il-18 in repeated implantation failure after in vitro fertilization. Fertil. Steril. 2004, 81, 59–65. [Google Scholar] [CrossRef] [PubMed]
  89. Toth, B.; Haufe, T.; Scholz, C.; Kuhn, C.; Friese, K.; Karamouti, M.; Makrigiannakis, A.; Jeschke, U. Placental interleukin-15 expression in recurrent miscarriage. Am. J. Reprod. Immunol. 2010, 64, 402–410. [Google Scholar] [CrossRef] [PubMed]
  90. Faas, M.M.; de Vos, P. Uterine NK cells and macrophages in pregnancy. Placenta 2017, 56, 44–52. [Google Scholar] [CrossRef] [PubMed]
  91. Williams, P.J.; Searle, R.F.; Robson, S.C.; Innes, B.A.; Bulmer, J.N. Decidual leucocyte populations in early to late gestation normal human pregnancy. J. Reprod. Immunol. 2009, 82, 24–31. [Google Scholar] [CrossRef] [PubMed]
  92. Okun, E.; Griffioen, K.J.; Gen Son, T.; Lee, J.; Roberts, N.J.; Mughal, M.R.; Hutchison, E.; Cheng, A.; Arumugam, T.V.; Lathia, J.D.; et al. TLR2 activation inhibits embryonic neural progenitor cell proliferation. J. Neurochem. 2010, 114, 462–474. [Google Scholar] [CrossRef] [PubMed]
  93. Polei, M.; Günther, J.; Koczan, D.; Fürbass, R. Gene expression profiles of bovine uninucleate trophoblast cells and trophoblast giant cells: A data note. BMC Res. Notes 2020, 13, 115. [Google Scholar] [CrossRef] [PubMed]
  94. Senger, P.L. Pathways to Pregnancy & Parturition; Current Conceptions: Redmond, OR, USA, 2012. [Google Scholar]
  95. Su, Y.; Li, Q.; Zhang, Q.; Li, Z.; Yao, X.; Guo, Y.; Xiao, L.; Wang, X.; Ni, H. Exosomes derived from placental trophoblast cells regulate endometrial epithelial receptivity in dairy cows during pregnancy. J. Reprod. Dev. 2022, 68, 21–29. [Google Scholar] [CrossRef]
  96. Wooding, F.B. The ruminant placental trophoblast binucleate cell: An evolutionary breakthrough. Biol. Reprod. 2022, 107, 705–716. [Google Scholar] [CrossRef]
  97. Abdollahi-Arpanahi, R.; Carvalho, M.R.; Ribeiro, E.S.; Peñagaricano, F. Association of lipid-related genes implicated in conceptus elongation with female fertility traits in dairy cattle. J. Dairy Sci. 2019, 102, 10020–10029. [Google Scholar] [CrossRef]
  98. Mullen, M.P.; Forde, N.; Parr, M.H.; Diskin, M.G.; Morris, D.G.; Nally, J.E.; Evans, A.C.; Crowe, M.A. Alterations in systemic concentrations of progesterone during the early luteal phase affect RBP4 expression in the bovine uterus. Reprod. Fertil. Dev. 2012, 24, 715. [Google Scholar] [CrossRef]
  99. Ribeiro, E.S.; Greco, L.F.; Bisinotto, R.S.; Lima, F.S.; Thatcher, W.W.; Santos, J.E. Biology of preimplantation conceptus at the onset of elongation in Dairy Cows1. Biol. Reprod. 2016, 94, 97. [Google Scholar] [CrossRef] [PubMed]
  100. Ribeiro, E.S. Symposium review: Lipids as regulators of conceptus development: Implications for metabolic regulation of reproduction in dairy cattle. J. Dairy Sci. 2018, 101, 3630–3641. [Google Scholar] [CrossRef]
  101. Ingman, W.V.; Robertson, S.A. Defining the actions of transforming growth factor β in reproduction. BioEssays 2002, 24, 904–914. [Google Scholar] [CrossRef]
  102. Monsivais, D.; Matzuk, M.M.; Pangas, S.A. The TGF-β family in the reproductive tract. Cold Spring Harb. Perspect. Biol. 2017, 9, a022251. [Google Scholar] [CrossRef]
  103. Kallapur, S.; Ormsby, I.; Doetschman, T. Strain dependency of TGF?1 function during embryogenesis. Mol. Reprod. Dev. 1999, 52, 341–349. [Google Scholar] [CrossRef]
  104. Ingman, W.V.; Robertson, S.A. The essential roles of TGFB1 in reproduction. Cytokine Growth Factor Rev. 2009, 20, 233–239. [Google Scholar] [CrossRef]
  105. Ihim, S.A.; Abubakar, S.D.; Zian, Z.; Sasaki, T.; Saffarioun, M.; Maleknia, S.; Azizi, G. Interleukin-18 cytokine in immunity, inflammation, and autoimmunity: Biological role in induction, regulation, and treatment. Front. Immunol. 2022, 13, 919973. [Google Scholar] [CrossRef] [PubMed]
  106. Akanno, E.C.; Plastow, G.; Fitzsimmons, C.; Miller, S.P.; Baron, V.; Ominski, K.; Basarab, J.A. Genome-wide association for heifer reproduction and calf performance traits in beef cattle. Genome 2015, 58, 549–557. [Google Scholar] [CrossRef]
  107. Blaschek, M.; Kaya, A.; Zwald, N.; Memili, E.; Kirkpatrick, B.W. A whole-genome assocation analysis of noncompensatory fertility in Holstein bulls. J. Dairy Sci. 2011, 94, 4695–4699. [Google Scholar] [CrossRef] [PubMed]
  108. Cochran, S.D.; Cole, J.B.; Null, D.J.; Hansen, P.J. Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genet. 2013, 14, 49. [Google Scholar] [CrossRef]
  109. Cole, J.B.; Wiggans, G.R.; Ma, L.; Sonstegard, T.S.; Lawlor, T.J., Jr.; Crooker, B.A.; Van Tassell, C.P.; Yang, J.; Wang, S.; Matukumalli, L.; et al. Genome-wide association analysis of thirty one production, health, and reproduction and body conformation traits in contemporary US Holstein cows. BMC Genom. 2011, 12, 408. [Google Scholar] [CrossRef] [PubMed]
  110. Cole, J.B.; VanRaden, P.M.; Null, D.J.; Hutchison, J.L.; Hubbard, S.M. Haplotype Tests for Economically Important Traits of Dairy Cattle. AIP RESEARCH REPORT GENOMIC5 (12-20). 2020. Available online: https://www.ars.usda.gov/ARSUserFiles/80420530/Publications/ARR/Haplotype%20tests_ARR-Genomic5.pdf (accessed on 1 February 2024).
  111. Guillaume, F.; Gautier, M.; Ben Jemaa, S.; Fritz, S.; Eggen, A.; Boichard, D.; Druet, T. Refinement of two female fertility qtl using alternative phenotypes in French Holstein Dairy Cattle. Anim. Genet. 2007, 38, 72–74. [Google Scholar] [CrossRef] [PubMed]
  112. Häfliger, I.M.; Spengeler, M.; Seefried, F.R.; Drögemüller, C. Four novel candidate causal variants for deficient homozygous haplotypes in Holstein cattle. Sci. Rep. 2022, 12, 5435. [Google Scholar] [CrossRef] [PubMed]
  113. Höglund, J.K.; Guldbrandsten, B.; Su, G.; Thomsen, B.; Lund, M.S. Genome scan detects quantitative trait loci affecting female fertility traits in Danish and Swedish Holstein cattle. J. Dairy Sci. 2009, 92, 2136–2143. [Google Scholar] [CrossRef] [PubMed]
  114. Höglund, J.K.; Sahana, G.; Guldbrandtsen, B.; Lund, M.S. Validation of associations for female fertility traits in Nordic Holstein, Nordic Red, and Jersey dairy cattle. BMC Genet. 2014, 15, 8. [Google Scholar] [CrossRef] [PubMed]
  115. Höglund, J.K.; Buitenhuis, B.; Guldbrandtsen, B.; Lund, M.S.; Sahana, G. Genome-wide association study for female fertility in Nordic Red cattle. BMC Genet. 2015, 16, 110. [Google Scholar] [CrossRef] [PubMed]
  116. Huang, W.; Kirkpatrick, B.W.; Rosa, G.J.M.; Khatib, H. A genome-wide assocation study using selective DNA pooling identifies candidate markers for feritliy in Holstein cattle. Anim. Genet. 2010, 41, 570–578. [Google Scholar] [CrossRef]
  117. Iso-Touru, T.; Sahana, G.; Guldbrandtsen, B.; Lund, M.S.; Vikki, J. Genome-wide association analysis of milk yield traits in Nordic Red cattle using imputed whole genome sequence variants. BMC Genet. 2016, 17, 55. [Google Scholar] [CrossRef] [PubMed]
  118. Liu, A.; Wang, Y.; Sahana, G.; Zhang, Q.; Liu, L.; Lund, M.S.; Su, G. Genome-wide association studies for female fertility traits in Chinese and Nordic Holsteins. Sci. Rep. 2017, 7, 8487. [Google Scholar] [CrossRef]
  119. Minozzi, G.; Nicolazzi, E.L.; Stella, A.; Biffani, S.; Negrini, R.; Lazzari, B.; Ajmone-Marsan, P.; Williams, J.L. Genome wide analysis of fertility and production traits in Italian Holstein cattle. PLoS ONE 2013, 8, e80219. [Google Scholar] [CrossRef]
  120. Minten, M.A.; Bilby, T.R.; Bruno, R.G.S.; Allen, C.C.; Madsen, C.A.; Wang, Z.; Sawyer, J.E.; Tibary, A.; Neibergs, H.L.; Geary, T.W.; et al. Effects of fertility on gene expression and function of the bovine endometrium. PLoS ONE 2013, 8, e69444. [Google Scholar] [CrossRef] [PubMed]
  121. Moraes, J.G.; Behura, S.K.; Geary, T.W.; Hansen, P.J.; Neibergs, H.L.; Spencer, T.E. Uterine influences on conceptus development in fertility-classified animals. Proc. Natl. Acad. Sci. USA 2018, 115, E1749–E1758. [Google Scholar] [CrossRef] [PubMed]
  122. Müller, M.P.; Rothammer, S.; Seichter, D.; Russ, I.; Hinrichs, D.; Tetens, J.; Thaller, G.; Medugorac, I. Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18. J. Dairy Sci. 2017, 100, 1987–2006. [Google Scholar] [CrossRef] [PubMed]
  123. Nayeri, S.; Sargolzaei, M.; Abo-Ismail, M.K.; May, N.; Miller, S.P.; Schenkel, F.; Moore, S.S.; Stothard, P. Genome-wide assocation for milk production and female fertility traits in Canadian dairy Holstein cattle. BMC Genet. 2016, 17, 75. [Google Scholar] [CrossRef] [PubMed]
  124. Ortega, M.S.; Denicol, A.C.; Cole, J.B.; Null, D.J.; Hansen, P.J. Use of single nucleotide polymorphisms in candidate genes associated with daughter pregnancy rate for prediction of genetic merit for reprodiction in Holstein cows. Anim. Genet. 2016, 47, 288–297. [Google Scholar] [CrossRef] [PubMed]
  125. Ortega, M.S.; Denicol, A.C.; Cole, J.B.; Null, D.J.; Taylor, J.F.; Schnabel, R.D.; Hansen, P.J. Association of single nucleotide polymorphisms in candidate genes previously related to genetic variation in fertility with phenotypic measurements of reproductive function in Holstein cows. J. Dairy Sci. 2017, 100, 3725–3734. [Google Scholar] [CrossRef] [PubMed]
  126. Sahana, G.; Guldbrandtsen, B.; Bendixen, C.; Lund, M.S. Genome-wide association mapping for female fertility traits in Danish and Swedish Holstein cattle. Anim. Genet. 2010, 41, 579–588. [Google Scholar] [CrossRef] [PubMed]
  127. Wu, X.; Mesbah-Uddin, M.; Guldbrandtsen, B.; Lund, M.S.; Sahana, G. Haplotypes responsible for early embryonic lethality detected in Nordic Holsteins. J. Dairy Sci. 2019, 102, 11116–11123. [Google Scholar] [CrossRef]
Genes 15 00765 g001
Figure 1. A principal component analysis (PCA) colored by birth year showing the two distinct clusters within the population.
Figure 1. A principal component analysis (PCA) colored by birth year showing the two distinct clusters within the population.
Genes 15 00765 g001
Genes 15 00765 g002
Figure 2. Loci associated with conception to the first service in Holstein heifers that were ET recipients for additive (A), dominant (B) and recessive (C) models. The axes for all plots have the Bos taurus chromosome on the x axis, and the −log10p-value on the y axis. The black line signifies the threshold for a moderate association with an uncorrected p < 1 × 10−5, and a red line indicates the threshold for a strong association (p < 5 × 10−7) with HCR1.
Figure 2. Loci associated with conception to the first service in Holstein heifers that were ET recipients for additive (A), dominant (B) and recessive (C) models. The axes for all plots have the Bos taurus chromosome on the x axis, and the −log10p-value on the y axis. The black line signifies the threshold for a moderate association with an uncorrected p < 1 × 10−5, and a red line indicates the threshold for a strong association (p < 5 × 10−7) with HCR1.
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Table 1. Summary of conception rate to the first service for Holstein heifers bred by artificial insemination and heifers that were embryo transfer recipients and remained pregnant.
Table 1. Summary of conception rate to the first service for Holstein heifers bred by artificial insemination and heifers that were embryo transfer recipients and remained pregnant.
Breeding TypeNo. Pregnant to the First ServiceNo. Not Pregnant to the First ServiceTotal
Artificial insemination157512542829
Embryo Transfer Recipients98810982086
Table 2. Single-nucleotide polymorphisms associated with HCR1 for ET recipients.
Table 2. Single-nucleotide polymorphisms associated with HCR1 for ET recipients.
BTA 1Position (bp) 2p-Value 3SNP 4Minor AlleleFreq. Cases 5Freq. Controls
6114,186,0623.81 × 10−7rs136769131A0.3790.433
1383,298,9646.63 × 10−6rs43097654T0.0500.027
X127,944,5652.47 × 10−6rs133237751T0.3920.373
X127,962,6701.78 × 10−6rs110097934A0.3910.373
X127,976,1371.78 × 10−6rs110705866T0.4750.409
1 Bos taurus chromosome location of the locus. 2 Location in chromosome base pairs of the significant SNP. 3 Significance (p-value) of the most significant SNP associated with HCR1 and ET. 4 The significant SNP associated with HCR1 to ET recipients listed by the rs number assigned by the National Center for Biotechnology Information SNP database “https://www.ncbi.nlm.nih.gov/snp/ (accessed on 30 April 2024)”. 5 When case frequency > control frequency it is the unfavorable allele.
Table 3. Enriched gene sets and leading edge genes with conception rate to the first service in Holstein heifers.
Table 3. Enriched gene sets and leading edge genes with conception rate to the first service in Holstein heifers.
Gene Set
(Database ID) 1		# Genes 2
(# LEG) 3		NES 4Leading Edge Genes 5
Heifer Conception Rate to the First AI Service
Sodium Ion Transmembrane Transporter Activity (GO:0015081)25
(13)		3.0SCN4B, ATP1B3, SLC1A1, SCNN1A, SLC6A3, PKD2, ATP1A1, SLC24A1, SLC6A17, SLC6A15, SLC8A1, SLC6A13, SLC6A6
Heifer Conception Rate to the First ET Service
Regulation of Protein Transport (GO:0051223)121
(49)		3.4F2RL1, F2R, DRD2, KDELR1, KDELR2, IL5, HSPA5, HMGN3, PDE2A, PIK3R1, GCG, TGFB2, TRIP6, FIS1, GAPVD1, ZPR1, TLR2, OAZ1, DNM1L, YWHAB, CASP4, TLR10, TLR6, CDK5, CABP1, PKD2, CYLD, CLEC6A, SLC51B, PBLD, TGFBR1, ASPH, ARRB1, IL18, NOD2, GPLD1, SIRT4, ANG, PKIA, ANKRD1, PRDX1, IL1B, DMTN, SERGEF, APOA1, IFNG, TGFB1, RBM22, APOA2
Regulation of Establishment of Protein Localization (GO:0070201)125
(53)		3.23F2RL1, F2R, DRD2, KDELR1, KDELR2, IL5, HSPA5, HMGN3, PDE2A, PIK3R1, GCG, TGFB2, TRIP6, FIS1, GAPVD1, ZPR1, TLR2, OAZ1, DNM1L, YWHAB, CASP4, TLR10, TLR6, CDK5, CABP1, PKD2, CYLD, CLEC6A, SLC51B, PBLD, TGFBR1, ASPH, ARRB1, IL18, NOD2, GPLD1, SIRT4, ANG, PKIA, ANKRD1, PRDX1, IL1B, DMTN, SERGEF, APOA1, IFNG, TGFB1, RBM22, APOA2, ANXA4, APOD, SNAP25, RBP4
Liposaccharide Metabolic Process and Glycolipid Metabolic Process (GO:1903509)18
(5)		3.05DPM3, ST6GALNAC6, BAX, PIGY, GPLD1
Regulation of Protein Secretion (GO:0050708)69
(35)		3.03F2RL1, F2R, DRD2, IL5, HMGN3, GCG, TGFB2, TLR2, DNM1L, CASP4, TLR10, TLR6, CLEC6A, ARRB1, NOD2, GPLD1, SIRT4, ANG, ANKRD1, IL1B, SERGEF, APOA1, IFNG, TGFB1, APOA2, ANXA4, RBP4, FKBP1B, IL10, IL1A, NR1H3, IFNAR1, GHRH, GHRL, INHBB, CHGA
1 Database sources. GO = Gene Ontology. 2 Total number of genes in the gene set. 3 Number of leading edge genes (LEGs) associated with HCR1 in the gene set. 4 NES is the normalized enrichment score. 5 Gene names of leading edge genes. Leading edge genes in bold are shared in more than one gene set.
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Kelson, V.C.; Kiser, J.N.; Davenport, K.M.; Suarez, E.M.; Murdoch, B.M.; Neibergs, H.L. Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients. Genes 2024, 15, 765. https://doi.org/10.3390/genes15060765

AMA Style

Kelson VC, Kiser JN, Davenport KM, Suarez EM, Murdoch BM, Neibergs HL. Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients. Genes. 2024; 15(6):765. https://doi.org/10.3390/genes15060765

Chicago/Turabian Style

Kelson, Victoria C., Jennifer N. Kiser, Kimberly M. Davenport, Emaly M. Suarez, Brenda M. Murdoch, and Holly L. Neibergs. 2024. "Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients" Genes 15, no. 6: 765. https://doi.org/10.3390/genes15060765

APA Style

Kelson, V. C., Kiser, J. N., Davenport, K. M., Suarez, E. M., Murdoch, B. M., & Neibergs, H. L. (2024). Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients. Genes, 15(6), 765. https://doi.org/10.3390/genes15060765

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