Genomic architecture of bovine κ-casein and β-lactoglobulin

Rocio, Gambra; Peñagaricano, Francisco; Schmidt, Jenna Kropp; Khateeb, Karam; Weigel, K.A.; Lucey, J.A.; Khatib, Hasan

doi:10.3168/jds.2012-6324

Cited by 32 publications

(39 citation statements)

References 44 publications

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“…Because this SNP is located within the region identified on BTA 13, the effect of the relative concentration of κ-CN on rennet-induced gelation could be strengthened. In addition, Gambra et al (2013) identified 25 SNP associated with the absolute concentration of κ-CN, and especially, one of the SNP could be of interest in relation to findings in the present study. The interesting SNP is located in the MTUS1 gene within the genome-wide significant region on BTA 27.…”

Section: Confirmation Of Associated Genomic Regionssupporting

confidence: 61%

“…The fixed effect of the significant haplotype has a large negative effect on G 30 , and it was found to explain 7.7% of the phenotypic variation of the trait. The overlap between the study by Gambra et al (2013) and the present study indicates that a gene affecting the concentration of κ-CN might be responsible for the QTL within this location.…”

Section: Confirmation Of Associated Genomic Regionscontrasting

confidence: 46%

“…Furthermore, Frederiksen et al (2011a) and Gustavsson et al (2014b) found that milk with smaller CN micelles has shorter gelation times and forms firmer gels than milk with larger CN micelles. As discussed earlier, both the significant QTL on BTA 13 and the QTL on BTA 27 harbor SNP that has been associated with relative (Schopen et al, 2011) and absolute concentration (Gambra et al, 2013) of κ-CN. This shows that κ-CN is a good marker for rennet-induced gelation and shows that the effects cannot be explained by the CSN3 gene itself.…”

Section: Major Qtl In Relation To the Casein Clustermentioning

confidence: 77%

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Bovine chromosomal regions affecting rheological traits in rennet-induced skim milk gels

Gregersen

Gustavsson

Glantz

et al. 2015

Journal of Dairy Science

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Optimizing cheese yield and quality is of central importance to cheese manufacturing. The yield is associated with the time it takes before the gel has an optimal consistency for further processing, and it is well known that gel formation differs between individual milk samples. By identifying genomic regions affecting traits related to rennet-induced gelation, the aim of this study was to identify potential candidate genes affecting these traits. Hence, rennet-induced gelation, including rennet coagulation time, gel strength, and yield stress, was measured in skim milk samples collected from 379 animals of the Swedish Red breed using low-amplitude oscillation measurements. All animals had genotypes for almost 621,000 segregating single nucleotide polymorphisms (SNP), identified using the Bovine HD SNPChip (Illumina Inc., San Diego, CA). The genome was scanned for associations, haplotypes based on SNP sets comprising highly associated SNP were inferred, and the effects of the 2 most common haplotypes within each region were analyzed using mixed models. Even though the number of animals was relatively small, a total of 21 regions were identified, with 4 regions showing association with more than one trait. A major quantitative trait locus for all traits was identified around the casein cluster explaining between 9.3 to 15.2% of the phenotypic variation of the different traits. In addition, 3 other possible candidate genes were identified; that is, UDP-N-acetyl-α-d-galactosamine:polypeptide N-acetylgalactosaminyl-transferase 1 (GALNT1), playing a role in O-glycosylation of κ-casein, and 2 cathepsins, CTSZ and CTSC, possibly involved in proteolysis of milk proteins. We have shown that other genes than the casein genes themselves may be involved in the regulation of gelation traits. However, additional analysis is needed to confirm these results. To our knowledge, this is the first study identifying quantitative trait loci affecting rennet-induced gelation of skim milk through a high-density genome-wide association study.

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Section: Confirmation Of Associated Genomic Regionssupporting

confidence: 61%

Section: Confirmation Of Associated Genomic Regionscontrasting

confidence: 46%

Section: Major Qtl In Relation To the Casein Clustermentioning

confidence: 77%

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Bovine chromosomal regions affecting rheological traits in rennet-induced skim milk gels

Gregersen

Gustavsson

Glantz

et al. 2015

Journal of Dairy Science

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“…During the past years, several whole-genome scans and genome-wide association studies (GWAS) have been conducted for milk composition traits (e.g., Schopen et al, 2009;Gambra et al, 2013), but such studies are scarce for technological properties of milk. Thus far, only chromosomal regions affecting rennet-induced coagulation important for cheese production have been studied (Gregersen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Bovine chromosomal regions affecting rheological traits in acid-induced skim milk gels

Glantz

Gustavsson

Bertelsen

et al. 2015

Journal of Dairy Science

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The production of fermented milk products has increased worldwide during the last decade and is expected to continue to increase during the coming decade. The quality of these products may be optimized through breeding practices; however, the relations between cow genetics and technological properties of acid milk gels are not fully known. Therefore, the aim of this study was to identify chromosomal regions affecting acid-induced coagulation properties and possible candidate genes. Skim milk samples from 377 Swedish Red cows were rheologically analyzed for acid-induced coagulation properties using low-amplitude oscillation measurements. The resulting traits, including gel strength, coagulation time, and yield stress, were used to conduct a genome-wide association study. Single nucleotide polymorphisms (SNP) were identified using the BovineHD SNPChip (Illumina Inc., San Diego, CA), resulting in almost 621,000 segregating markers. The genome was scanned for putative quantitative trait loci (QTL) regions, haplotypes based on highly associated SNP were inferred, and the additive genetic effects of haplotypes within each QTL region were analyzed using mixed models. A total of 8 genomic regions were identified, with large effects of the significant haplotype explaining between 4.8 and 9.8% of the phenotypic variance of the studied traits. One major QTL was identified to overlap between gel strength and yield stress, the QTL identified with the most significant SNP closest to the gene coding for κ-casein (CSN3). In addition, a chromosome-wide significant region affecting yield stress on BTA 11 was identified to be colocated with PAEP, coding for β-lactoglobulin. Furthermore, the coagulation properties of the genetic variants within the 2 genes were compared with the coagulation properties identified by the patterns of the haplotypes within the regions, and it was discovered that the haplotypes were more diverse and in one case slightly better at explaining the phenotypic variance. Besides these significant QTL comprising the 2 milk proteins, 3 additional genes are proposed as possible candidates, namely RAB22A, CDH13, and STAT1, and all have previously been found to be expressed in the mammary gland. To our knowledge, this is the first attempt to map QTL regions for acid-induced coagulation properties.

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“…Supplemental material is available online at www.g3journal.org/lookup/suppl/ doi:10.1534/g3.116.031096/-/DC1 1 into three domains: biological processes, molecular functions, and cellular components. This ontology has been used successfully for dissecting relevant traits in livestock species (e.g., Peñagaricano et al 2013;Gambra et al 2013). Similarly, each MeSH term is clustered into 19 different categories; some MeSH categories, such as Diseases, are not included in GO, whereas other functional categories, such as Phenomena and Processes, or Chemicals and Drugs, share similar concepts with those of GO.…”

mentioning

confidence: 99%

MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken

Morota

Beissinger

Peñagaricano

2015

Preprint

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Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO) and more recently by Medical Subject Headings (MeSH). Here we report a suite of MeSH packages for chicken in Bioconductor and illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published datasets that were employed represent (i) differentially expressed genes and (ii) candidate genes under selective sweep or epistatic selection. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis but also that MeSH is able to further enrich the representation of biological knowledge and often provide more interpretable results. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies as well as semantic similarities among selected genes. These yielded the similarity levels between significant functional terms, and the annotation of each gene yielded the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits.

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Genomic architecture of bovine κ-casein and β-lactoglobulin

Cited by 32 publications

References 44 publications

Bovine chromosomal regions affecting rheological traits in rennet-induced skim milk gels

Bovine chromosomal regions affecting rheological traits in rennet-induced skim milk gels

Bovine chromosomal regions affecting rheological traits in acid-induced skim milk gels

MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken

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