Sequence-based GWAS and post-GWAS analyses reveal a key role of SLC37A1, ANKH, and regulatory regions on bovine milk mineral content

Genome-wide association studies for citric and lactic acids in dairy sheep milk in a New Zealand flock

The objectives of this study were to estimate genetic parameters for citric acid content (CA) and lactic acid content (LA) in sheep milk and to identify the associated candidate genes in a New Zealand dairy sheep flock. Records from 165 ewes were used. Heritability estimates based on pedigree records for CA and LA were 0.65 and 0.33, respectively. The genetic and phenotypic correlations between CA and LA were strong-moderate and negative. Estimates of genomic heritability for CA and LA were also high (0.85, 0.51) and the genomic correlation between CA and LA was strongly negative (-0.96 ± 0.11). No significant associations were found at the Bonferroni level. However, one intragenic SNP in C1QTNF1 (chromosome 11) was associated with CA, at the chromosomal significance threshold. Another SNP associated with CA was intergenic (chromosome 15). For LA, the most notable SNP was intragenic in CYTH1 (chromosome 11), the other two SNPs were intragenic in MGAT5B and TIMP2 (chromosome 11), and four SNPs were intergenic (chromosomes 1 and 24). The functions of candidate genes indicate that CA and LA could potentially be used as biomarkers for energy balance and clinical mastitis. Further research is recommended to validate the present results.

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Genetic analysis of milk minerals in dairy cattle: a review

Mineral composition in milk can affect its nutritional as well as physio-chemical properties of milk and is considered an important trait in the evaluation of milk quality. The composition and concentration of milk minerals could be altered with natural source of variation including nutrition and genetics. The effect of diet on milk minerals is well studied. However, genetic effects on the milk minerals have recently gained the attention. This review provides an overview of the genetic variation of milk minerals, and the genomic regions associated with mineral concentration in the milk are also discussed. The difference of milk minerals between breeds and the genetic parameters including heritability estimates and correlation among minerals indicates that milk minerals are under strong genetic control. Recently, the genome-wide association study (GWAS) has explored several regions associated with milk minerals and thus provides a new genetic source for improving the milk quality through genomics-assisted breeding. Hence, a combination of the qualitative and molecular approaches can be exploited to improving the nutritional quality of cattle milk in terms of its mineral composition.

Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

Human pangenome analysis of sequences missing from the reference genome reveals their widespread evolutionary, phenotypic, and functional roles

Nonreference sequences (NRSs) are DNA sequences present in global populations but absent in the current human reference genome. However, the extent and functional significance of NRSs in the human genomes and populations remains unclear. Here, we de novo assembled 539 genomes from five genetically divergent human populations using long-read sequencing technology, resulting in the identification of 5.1 million NRSs. These were merged into 45284 unique NRSs, with 29.7% being novel discoveries. Among these NRSs, 38.7% were common across the five populations, and 35.6% were population specific. The use of a graph-based pangenome approach allowed for the detection of 565 transcript expression quantitative trait loci on NRSs, with 426 of these being novel findings. Moreover, 26 NRS candidates displayed evidence of adaptive selection within human populations. Genes situated in close proximity to or intersecting with these candidates may be associated with metabolism and type 2 diabetes. Genome-wide association studies revealed 14 NRSs to be significantly associated with eight phenotypes. Additionally, 154 NRSs were found to be in strong linkage disequilibrium with 258 phenotype-associated SNPs in the GWAS catalogue. Our work expands the understanding of human NRSs and provides novel insights into their functions, facilitating evolutionary and biomedical researches.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Genome-wide elucidation of CNV regions and their association with production and reproduction traits in composite Vrindavani cattle

The present study was aimed to analyze the genome-wide copy number variations (CNVs) in Vrindavani composite cattle and concatenate them into CNV regions (CNVRs), and finally test the association of CNVRs with different production and reproduction traits. Genotypic data, generated on BovineSNP50 Beadchip (v3) array for 96 Vrindavani animals, was used to elucidate the CNVs at the genome level. Intensity data covering over 53,218 SNP genotypes on bovine genome was used. Algorithm based on Hidden Markov Model was employed in PennCNV program to detect, normalize and filter CNVs across the genome. 252 putative CNVs, detected via PennCNV program, in different individuals were concatenated into 71 CNV regions (CNVRs) using CNVRuler program. Association of CNVRs with important (re)production traits in Vrindavani animals was assessed using linear regression. Five CNVRs were found to be significantly associated with ten important (re)production traits. The genes harbored in these regions provided useful insights into the association of CNVRs with genes and ultimately the variation at phenotype level. Important genes that overlapped with CNVRs included WASHC4, HS6ST3, MBNL2, TOLLIP, PIDD1 and TSPAN4. Furthermore, the CNVRs were found to overlap with important QTLs available in AnimalQTL database which affect milk yield and composition along with reproduction and immune function traits. The copy number states of three enes were validated using digital droplet PCR technique. The results from the present study significantly enhance the understanding about CNVs in Vrindavani cattle and should help establish its CNV map. The study will also enable further investigation on association of these variants with important traits of economic interest including disease incidence.

Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle

In this study, genome-wide association study (GWAS) was conducted for identifying significantly associated genomic regions/SNPs with milk protein and minerals in the 96 taurine-indicine crossbred (Vrindavani) cows using 50K SNP Chip. After quality control, a total of 41,427 SNPs were retained and were further analyzed using a single-SNP additive linear model. Lactation stage, parity, test day milk yield and proportion of exotic inheritance were included as fixed effects in GWAS model. Across all traits, 13 genome-wide significant (p < 1.20 x 10⁻⁰⁶) and 49 suggestive significant (p < 2.41 x 10⁻⁰⁵) SNPs were identified which were located on 18 different autosomes. The strongest association for protein percentage, calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), and iron (Fe) were found on BTA 18, 7, 2, 3, 14, and 2, respectively. No significant SNP was detected for manganese (Mn). Several significant SNPs identified were within or close proximity to CDH13, BHLHE40, EDIL3, HAPLN1, INHBB, USP24, ZFAT, and IKZF2 gene, respectively. Enrichment analysis of the identified candidate genes elucidated biological processes, cellular components, and molecular functions involved in metal ion binding, ion transportation, transmembrane protein, and signaling pathways. This study provided a groundwork to characterize the molecular mechanism for the phenotypic variation in milk protein percentage and minerals in crossbred cattle. Further work is required on a larger sample size with fine mapping of identified QTL to validate potential candidate regions.

Transcriptome sequencing analysis for the identification of stable lncRNAs associated with bovine Staphylococcus aureus mastitis

Background

Staphylococcus aureus (S. aureus) mastitis is one of the most difficult diseases to treat in lactating dairy cows worldwide. S. aureus with different lineages leads to different host immune responses. Long non-coding RNAs (lncRNAs) are reported to be widely involved in the progress of inflammation. However, no research has identified stable lncRNAs among different S. aureus strain infections. In addition, folic acid (FA) can effectively reduce inflammation, and whether the inflammatory response caused by S. aureus can be reduced by FA remains to be explored.

Methods

lncRNA transcripts were identified from Holstein mammary gland tissues infected with different concentrations of S. aureus (in vivo) and mammary alveolar cells (Mac-T cells, in vitro) challenged with different S. aureus strains. Differentially expressed (DE) lncRNAs were evaluated, and stable DE lncRNAs were identified in vivo and in vitro. On the basis of the gene sequence conservation and function conservation across species, key lncRNAs with the function of potentially immune regulation were retained for further analysis. The function of FA on inflammation induced by S. aureus challenge was also investigated. Then, the association analysis between these keys lncRNA transcripts and hematological parameters (HPs) was carried out. Lastly, the knockdown and overexpression of the important lncRNA were performed to validate the gene function on the regulation of cell immune response.

Results

Linear regression analysis showed a significant correlation between the expression levels of lncRNA shared by mammary tissue and Mac-T cells (P < 0.001, R² = 0.3517). lncRNAs PRANCR and TNK2–AS1 could be regarded as stable markers associated with bovine S. aureus mastitis. Several HPs could be influenced by SNPs around lncRNAs PRANCR and TNK2–AS1. The results of gene function validation showed PRANCR regulates the mRNA expression of SELPLG and ITGB2 within the S. aureus infection pathway and the Mac-T cells apoptosis. In addition, FA regulated the expression change of DE lncRNA involved in toxin metabolism and inflammation to fight against S. aureus infection.

Conclusions

The remarkable association between SNPs around these two lncRNAs and partial HP indicates the potentially important role of PRANCR and TNK2–AS1 in immune regulation. Stable DE lncRNAs PRANCR and TNK2–AS1 can be regarded as potential targets for the prevention of bovine S. aureus mastitis. FA supplementation can reduce the negative effect of S. aureus challenge by regulating the expression of lncRNAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-021-00639-2.