Abstract
Goat genomics has evolved at a low pace because of a lack of molecular tools and sufficient investment. Whilst thousands and hundreds of quantitative trait loci (QTL) have been identified in cattle and sheep, respectively, about nine genome scans have been performed in goats dealing with traits as conformation, growth, fiber quality, resistance to nematodes, and milk yield and composition. In contrast, a great effort has been devoted to the characterization of candidate genes and their association with milk, meat, and reproduction phenotypes. In this regard, causal mutations have been identified in theαS1-casein gene that has a strong effect on milk composition and thePISlocus that is linked to intersexuality and polledness. In recent times, the development of massive parallel sequencing technologies has allowed to build a reference genome for goats as well as to monitor the expression of mRNAs and microRNAs in a broad array of tissues and experimental conditions. Besides, the recent design of a 52K SNP chip is expected to have a broad impact in the analysis of the genetic architecture of traits of economic interest as well as in the study of the population structure of goats at a worldwide scale.
Highlights
The main purpose of this review is to provide a general perspective of the advances made in the field of goat genomics in the last three decades
Schopen et al [44] reported trans-quantitative trait loci (QTL) influencing the contents of CSN1S1, CSN1S2, CSN2, and total caseins
This relationship is less obvious; for example, the polymorphism of the diacylglycerol acyltransferase 2 (DGAT2) gene that regulates triacylglycerol synthesis has been associated with withers height in Chinese breeds without providing any mechanistic explanation [70]
Summary
The main purpose of this review is to provide a general perspective of the advances made in the field of goat genomics in the last three decades. Schopen et al [44] reported trans-QTL influencing the contents of CSN1S1 (bovine chromosome 9), CSN1S2 (chromosomes 1, 10, and 17), CSN2 (chromosome 3), and total caseins (chromosome 11) This means that genome-wide approaches will be needed to identify the genetic factors that regulate milk casein and protein contents. In the case of GH and MSTN, genetic variability may affect growth rate because both molecules are known to play a key role in this physiological process ( causal mutations have not been identified yet) For other loci, this relationship is less obvious; for example, the polymorphism of the diacylglycerol acyltransferase 2 (DGAT2) gene that regulates triacylglycerol synthesis has been associated with withers height in Chinese breeds without providing any mechanistic explanation [70]. A downregulation of lipid metabolism genes was observed, a feature that is consistent with the inhibition of this biochemical pathway as a consequence of intramammary infection [128]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
