Dairy Genome Project (DGP) Introduction

Introduction

The genomic revolution has changed the way in which we approach biological questions. Publicly available genetic maps, whole genome physical maps, knowledge from knockouts and transgenics combined with a wealth of information on transcription and protein profiling, provide researchers with the tools to make discoveries at a staggering pace. What at one time might take a career to accomplish can now be done in a relatively short period of time. This is reflected by a report which states that almost half of the causal mutations identified for QTL to date have occurred in 2001, and the rate of discovery is expected to grow in the near future (Korstanje and Paigen, 2002). Indeed, we believe the time has come to begin identifying the causal mutations for QTL in livestock that have been known for many years now.

One of the conjectures that has been realized from the flood of genomic data is that genetic questions only become more complex as additional data becomes available. Many believed that the completion of the human genome sequence would provide a fast track for identifying QTL in livestock. Although some genetic tests have been developed in the beef, dairy and swine sectors using the human sequence, the number of validated DNA tests in livestock pales in comparison to the number of QTL that have been identified. We believe there are two main reasons for the lack of DNA based tests in livestock.

Firstly, we simply don't know what we're looking for. Aside from indels producing frameshift mutations, premature stop codons or charged amino acid substitutions, we have limited scientific understanding of the genetic mechanisms responsible for phenotypic variation at the DNA level. However, this is beginning to change. We believe that as RNA and protein expression data become available the paradigm will shift from that of mutations in coding sequence causing QTL to one in which mutations in regulatory regions affecting RNA and protein expression levels will be responsible for the majority of the genetic mechanism behind QTL. Indeed, the few livestock quantitative trait nucleotides (QTN) which have been identified reflect our current understanding of these genetic mechanisms to date. Myostatin (Smith, 1997) is a deletion in the coding sequence, DGAT1 (Grisart et al. 2002) is a charged amino acid substitution and GHR (Blott et al, 2003) is a polar amino acid substitution within the transmembrane domain of the protein. Recently, Freking et al. (2002) discovered the mutation affecting the callipyge phenotype in sheep to be a single base mutation in noncoding DNA which affects methylation status. The callipyge mutation serves to illustrates the need to consider additional genetic mechanisms beyond that which we currently understand. Furthermore, currently available commercial tests in the bovine thyroglobulin gene (TG5) (Barendse et al, 2001) and mu calpain gene (CAPN1) (Smith et al., 2000) have yet to be fully validated. It is possible that the mutations being tested for in these genes are in fact not the causal QTN and may have limited predictive power in populations outside those which they were discovered (Moore et al., 2004; White et al. 2004; Taylor unpublished data). Thus there is a need to validate putative QTN in order for them to be of economic value to the industry.

Secondly, aside from the dairy industry, the populations used to identify QTL are not very well suited for discovering the causal QTN. Traditional livestock resource populations have been extremely effective at identifying chromosomal regions harboring genes of major effect (MacNeil and Grosz, 2002; Milan et al., 2001; Stone et al., 1999; Zhang et al., 1998) but the confidence intervals for the putative QTL are in the range of 20-40cM. This is an order of magnitude larger than what is required for positional cloning. One of the major impediments to narrowing these confidence intervals is the number of available meioses with which to map genes. One way to increase the number of meioses in a mapping population is to simply obtain samples from commercial populations. The dairy industry is perfectly suited to fine-mapping genes due to the large number of pedigreed animals that have phenotypes currently available. Even so, only two QTN have been discovered in dairy cattle despite the numerous QTL which have been published. Although the resources are available (pedigree, DNA, phenotypes) in dairy populations we believe that an efficient strategy must be developed to utilize these resources in order to identify QTN.

Dairy Genome Project (DGP) Introduction

Introduction

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