Genomic Regulation of Minerals in Milk: analysis of bovine chromosome 5
Catillo G., Abeni F., Petrera F., Steri R., Moioli B., Scatà M.C., Marchitelli C. and Napolitano F.
Consiglio per la Ricerca e la sperimentazione in Agricoltura (CRA) - ITALY
The influence of the genetic background on milk features, especially those related to cheese-making
features, was previously studied starting from the context of protein families, mainly caseins and
their variants. The mineral components of milk, calcium (Ca), phosphorous (P), potassium (K), and
magnesium (Mg), are reported to influence milk composition and milk coagulation properties
(MCP). In addition, it is important to assess how each of these minerals is present in milk, either in
the micelle-bound form or in the soluble form, because this partition may affect the interaction with
caseins, thus determining milk clotting ability. Beyond the concern for cheese-making properties,
there is growing interest in the availability of Ca2+ in dairy products, since colloidal calcium is
easily absorbed in the small intestine and Ca2+ phosphate from casein phosphopeptides can improve
intestinal Ca absorption. Very recently, Bijl et al. 2013 (J. Dairy Sci. 96: 5455-5464) described the
relationship among the mineral fractions on the basis of their link with the micellar component
rather than their presence in the solution, confirming the growing interest in milk mineral
equilibrium. The availability of genomic tools allowing the genotyping of most livestock at over
50,000 markers, which are distributed along the genome, could allow the identification of genomic
regions carrying QTLs. Appropriate statistical tools, such as Multivariate Factors Analysis, allow
the investigation of the effects of a high number of genetic markers of complex traits, such as milk
mineral components.
Within the GENZOOT project 72 cows of two dairy breeds, Holstein Friesian and Bianca Val
Padana, were genotyped using the Illumina Bovine SNP50K BeadChip. From 42 milk samples of
the genotyped cows, 52 milk quality parameters, including Ca, P, K, and Mg in soluble and
colloidal form, micellar composition, and MCP, were determined. The Multivariate Factors
Analysis allowed extraction of 50 genomic factors and 13 phenotypic factors that explained 94% of
total phenotypic variance.
Genotypes and phenotypes of each cow sample were connected by correlating the phenotypic and
genomic factor scores. The factors with a correlation coefficients > 0.55 were investigated through
the analysis of those SNPs, within each genomic factor, having loading greater than |0.50|. The
allele substitution effect for each SNP analysed was computed by regressing phenotype scores on
marker SNP and we proceeded to the estimation of the statistical test for multiple comparisons
using the False Discovery Rate (FDR), to exclude the false positives.
Seven SNPs of chromosome 5, selected for the high loading value (|0.66| ÷ |0.85|), displayed a
significant effect (FDR= 0.02 ÷ 0.005) on phenotypic factor labelled as ‘Milk mineral excretion’,
which brought together 9 phenotypes: the total milk secretion of Ca, P, and Mg; the daily secreted
amount of soluble and colloidal Ca and Mg; the daily amount of colloidal and casein P; and
indicates that the amount of all forms of mineral components is highly correlated.
The analysis of the genomic regions encoding the SNPs that mostly affected milk mineral
components allowed the detection of genes directly involved in mineral bone metabolism and in
membrane transport of cations:
LRP6 – Low-density lipoprotein receptor-related protein 6. (Williams and Insogna, 2009 - J. Bone
Miner. Res. 24: 171–178).
IRAK4 – Interleukin-1 receptor-associated kinase 4 (Li et al., 2005 - J. Exp. Med., 201: 1169–1177).
SLC38A2 or SNAT2 – Solute Carrier Family 38 member 2 (Young et al., 2010 - Am. J. Physiol.
Cell Physiol. 298: C1401–C1413).