Limits...
Go with the flow-biology and genetics of the lactation cycle.

Strucken EM, Laurenson YC, Brockmann GA - Front Genet (2015)

Bottom Line: Only very few studies have estimated exact gene and marker effects at different time-points during lactation.The most prominent gene affecting milk yield and milk fat, DGAT1, exhibits its main effects after peak production, whilst the casein genes have larger effects in early lactation.Understanding the physiological dynamics and elucidating the time-dependent genetic effects behind dynamically expressed traits will contribute to selection decisions to further improve productive and healthy breeding populations.

View Article: PubMed Central - PubMed

Affiliation: Animal Science, School of Environmental and Rural Science, University of New England Armidale, NSW, Australia.

ABSTRACT
Lactation is a dynamic process, which evolved to meet dietary demands of growing offspring. At the same time, the mother's metabolism changes to meet the high requirements of nutrient supply to the offspring. Through strong artificial selection, the strain of milk production on dairy cows is often associated with impaired health and fertility. This led to the incorporation of functional traits into breeding aims to counteract this negative association. Potentially, distributing the total quantity of milk per lactation cycle more equally over time could reduce the peak of physiological strain and improve health and fertility. During lactation many factors affect the production of milk: food intake; digestion, absorption, and transportation of nutrients; blood glucose levels; activity of cells in the mammary gland, liver, and adipose tissue; synthesis of proteins and fat in the secretory cells; and the metabolic and regulatory pathways that provide fatty acids, amino acids, and carbohydrates. Whilst the endocrine regulation and physiology of the dynamic process of milk production seems to be understood, the genetics that underlie these dynamics are still to be uncovered. Modeling of longitudinal traits and estimating the change in additive genetic variation over time has shown that the genetic contribution to the expression of a trait depends on the considered time-point. Such time-dependent studies could contribute to the discovery of missing heritability. Only very few studies have estimated exact gene and marker effects at different time-points during lactation. The most prominent gene affecting milk yield and milk fat, DGAT1, exhibits its main effects after peak production, whilst the casein genes have larger effects in early lactation. Understanding the physiological dynamics and elucidating the time-dependent genetic effects behind dynamically expressed traits will contribute to selection decisions to further improve productive and healthy breeding populations.

No MeSH data available.


Related in: MedlinePlus

Simplified pathways for major genes involved in milk production. Green boxes are genes, orange circles are the pathways the genes are involved in, blue boxes are the milk production traits that are affected (information is assembled from KEGG Pathway Database, 17.11.2014; http://www.genome.jp/kegg/pathway.html and literature review; for gene names, see Table 1).
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Figure 4: Simplified pathways for major genes involved in milk production. Green boxes are genes, orange circles are the pathways the genes are involved in, blue boxes are the milk production traits that are affected (information is assembled from KEGG Pathway Database, 17.11.2014; http://www.genome.jp/kegg/pathway.html and literature review; for gene names, see Table 1).

Mentions: Only around a dozen candidate genes have been consistently identified between studies and described more extensively with regards to their association with the main milk production traits (Table 1). The pathways through which these genes affect milk production traits depict the variety of processes that have to be considered (Figure 4). Genes like the BDNF, FTO, or IGF1 impact upon food intake and thus nutrient and energy availability (Mullen et al., 2011; Zielke et al., 2011, 2013; Waters et al., 2012). Other genes such as GHR, PRLR, or SPP1 affect growth, proliferation, and apoptosis of cells (Viitala et al., 2006; Khatib et al., 2007; Banos et al., 2008; Lu et al., 2011a; Rahmatalla et al., 2011), whilst DGAT1 and AGPAT6 are involved directly in triglyceride synthesis (Winter et al., 2002; Bionaz and Loor, 2008a; Strucken et al., 2010a; He et al., 2011). Of further note are the casein genes which encode the major fraction of milk proteins (Velmala et al., 1995). Figure 4 provides an overview of those candidate genes and the pathways through which they affect milk production traits. To our knowledge, no genes affecting mammogenesis have been directly linked to milk production. Recently, Raven et al. (2014) included traits of the mammary system in a GWAS study which identified five regions on four different chromosomes with significant effects; however, a clear description of the phenotype (the mammary system) was lacking.


Go with the flow-biology and genetics of the lactation cycle.

Strucken EM, Laurenson YC, Brockmann GA - Front Genet (2015)

Simplified pathways for major genes involved in milk production. Green boxes are genes, orange circles are the pathways the genes are involved in, blue boxes are the milk production traits that are affected (information is assembled from KEGG Pathway Database, 17.11.2014; http://www.genome.jp/kegg/pathway.html and literature review; for gene names, see Table 1).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374477&req=5

Figure 4: Simplified pathways for major genes involved in milk production. Green boxes are genes, orange circles are the pathways the genes are involved in, blue boxes are the milk production traits that are affected (information is assembled from KEGG Pathway Database, 17.11.2014; http://www.genome.jp/kegg/pathway.html and literature review; for gene names, see Table 1).
Mentions: Only around a dozen candidate genes have been consistently identified between studies and described more extensively with regards to their association with the main milk production traits (Table 1). The pathways through which these genes affect milk production traits depict the variety of processes that have to be considered (Figure 4). Genes like the BDNF, FTO, or IGF1 impact upon food intake and thus nutrient and energy availability (Mullen et al., 2011; Zielke et al., 2011, 2013; Waters et al., 2012). Other genes such as GHR, PRLR, or SPP1 affect growth, proliferation, and apoptosis of cells (Viitala et al., 2006; Khatib et al., 2007; Banos et al., 2008; Lu et al., 2011a; Rahmatalla et al., 2011), whilst DGAT1 and AGPAT6 are involved directly in triglyceride synthesis (Winter et al., 2002; Bionaz and Loor, 2008a; Strucken et al., 2010a; He et al., 2011). Of further note are the casein genes which encode the major fraction of milk proteins (Velmala et al., 1995). Figure 4 provides an overview of those candidate genes and the pathways through which they affect milk production traits. To our knowledge, no genes affecting mammogenesis have been directly linked to milk production. Recently, Raven et al. (2014) included traits of the mammary system in a GWAS study which identified five regions on four different chromosomes with significant effects; however, a clear description of the phenotype (the mammary system) was lacking.

Bottom Line: Only very few studies have estimated exact gene and marker effects at different time-points during lactation.The most prominent gene affecting milk yield and milk fat, DGAT1, exhibits its main effects after peak production, whilst the casein genes have larger effects in early lactation.Understanding the physiological dynamics and elucidating the time-dependent genetic effects behind dynamically expressed traits will contribute to selection decisions to further improve productive and healthy breeding populations.

View Article: PubMed Central - PubMed

Affiliation: Animal Science, School of Environmental and Rural Science, University of New England Armidale, NSW, Australia.

ABSTRACT
Lactation is a dynamic process, which evolved to meet dietary demands of growing offspring. At the same time, the mother's metabolism changes to meet the high requirements of nutrient supply to the offspring. Through strong artificial selection, the strain of milk production on dairy cows is often associated with impaired health and fertility. This led to the incorporation of functional traits into breeding aims to counteract this negative association. Potentially, distributing the total quantity of milk per lactation cycle more equally over time could reduce the peak of physiological strain and improve health and fertility. During lactation many factors affect the production of milk: food intake; digestion, absorption, and transportation of nutrients; blood glucose levels; activity of cells in the mammary gland, liver, and adipose tissue; synthesis of proteins and fat in the secretory cells; and the metabolic and regulatory pathways that provide fatty acids, amino acids, and carbohydrates. Whilst the endocrine regulation and physiology of the dynamic process of milk production seems to be understood, the genetics that underlie these dynamics are still to be uncovered. Modeling of longitudinal traits and estimating the change in additive genetic variation over time has shown that the genetic contribution to the expression of a trait depends on the considered time-point. Such time-dependent studies could contribute to the discovery of missing heritability. Only very few studies have estimated exact gene and marker effects at different time-points during lactation. The most prominent gene affecting milk yield and milk fat, DGAT1, exhibits its main effects after peak production, whilst the casein genes have larger effects in early lactation. Understanding the physiological dynamics and elucidating the time-dependent genetic effects behind dynamically expressed traits will contribute to selection decisions to further improve productive and healthy breeding populations.

No MeSH data available.


Related in: MedlinePlus