Linking DNA to production: the mapping of quantitative trait loci in livestock (Genomics)

1. Introduction

The world’s livestock populations provide a considerable resource with which functions can be assigned to genes. Unique populations have resulted from centuries of genetic selection focused on specific traits such as milk or meat production, fertility, and conformation, to name a few. Livestock production is of significant economic importance worldwide, and animal breeders and geneticists alike have historically been interested in linking the genetic makeup of an animal with its production. While the accuracy of traditional methods of selection is high, selection for certain traits can be limited by the fact that they can only be measured in one sex or are difficult to measure in a production setting. For these traits, molecular technologies can help improve the accuracy of their selection.

The majority of the traits of interest for the various livestock sectors are quantitative in nature in that they are controlled by many genes. Quantitative trait loci (QTL) are areas of the genome that affect a quantitative trait. A QTL may contain one or a number of genes affecting a specific trait. The size of the effect will vary depending on the gene and the trait involved. Our ability to identify QTL is a function of the size of the gene effect, the family or population structure under study, and the density of informative DNA marker information available. DNA marker information, compiled in the form of genetic linkage maps, is currently available for all of the major livestock species (Barendse et al., 1997; Kappes et al., 1997; Rohrer et al., 1996; De Gotari et al., 1998; Groenen et al., 2000).

DNA markers generally fall into two categories. The first category includes highly informative, multiallelic markers, of which Simple Sequence Repeats (SSRs or microsatellites) are the most widely used for genetic and QTL mapping in livestock. SSRs are hypermutable, which has resulted in multiple alleles segregating in any population. Studies using these markers have generally been restricted to within families, rather than populations, where the genetic phase, the specific allelic association with the trait, can be determined. This, in turn, has meant that in species such as cattle, QTL segregating in only a limited number of individuals, usually the sires or grandsires of families, have been studied.

The second type of marker is the Single Nucleotide Polymorphism (SNP). These markers are usually biallelic and much less mutable than SSRs. Studies using SNPs may be carried out in animal populations rather than families, allowing the analysis of many more individual genomes than is otherwise possible. SNPs promise to revolutionize the way in which QTL mapping is carried out in livestock species, promising both more rapid and sensitive approaches.

2. QTL mapping in livestock species

The majority of research in QTL identification has been carried out in cattle (dairy and beef) and swine. In both species, work has centered on traits that are of importance for production or product quality. For dairy cattle, the work has focused on milk production and quality-associated traits (e.g., Freyer et al., 2002; Olsen et al., 2002), while for beef cattle (see Table 1) and swine (e.g., Andersson-Eklund et al., 1998; Varona et al., 2002), the focus has been on growth and carcass-related traits. Work has also been done in both species to identify, among other things, QTL associated with reproductive, behavior and health-/disease-related traits (e.g., Desautes et al., 2003; Kuhn et al., 2003).

Table 1 Examples of QTL reported in the literature for several growth- and meat-quality traits in beef cattle

Trait Chromosome Region (cM) Effecta (S.D.)
Birth weight 5 70-110 0.39-0.82
20-30 0.79
60-70 n/a
6 20-70 0.39-0.82
25-60 3.8 (kg)
14 30-60 0.39-0.82
18 100-130 0.39-0.82
21 6-30 0.39-0.82
Average daily gain on feed 5 0-20 55-75 0.62 0.65
Preweaning average daily gain 5 0-20 70-80 0.68 0.50
Backfat 5 65-70 0.67
6 64-68 0.43
81-83 0.42
19 5-15 0.67
39-46 1.33
65-100 0.43
Marbling 2 25-45 n/a
17 10-60 32.77b
27 20-65 29.82b

a Size of phenotypic variation attributable to the QTL. b Actual effect. n/a: Not available.

In chickens, the research focus has been primarily divided between the discovery of QTL underlying egg production-associated traits and growth/carcass/meat production-related traits (e.g., de Koning et al., 2003). Some research has also been carried out in identifying genes associated with disease, for example, Marek’s disease (Yonash et al., 1999).

Experiments to discover QTL in sheep are more limited. For example, Beh et al. (2002) have performed a genome scan to identify QTL for resistance to the intestinal parasite Trichostrongylus colubriformis in sheep, and Rozen et al. (2003) have identified QTL associated with milk production traits.

3. Genes underlying QTL

While a significant amount of research has been done on QTL detection in livestock species, very few genes underlying the various QTL have actually been identified. Several examples are listed in Table 2. The limitation in identifying the genes has been due largely to the resolution of QTL mapping attainable using the current interval mapping approaches. Most regions identified as housing QTL have been in the order of 20-50 cM in length. This can approximate to as many as 50 million bases. The fact that such an interval can house thousands of genes, plus the fact that comparative maps with more well-studied genomes have until recently been rudimentary, has made it difficult to move from QTL regions to the genes themselves. More recently, Identity by Decent (IBD) methodology has been used to successfully narrow down QTL regions, and, in some cases, identify genes underlying QTL (Farnir et al., 2000; Moore et al., 2003; Li et al., 2002, 2004; see also Article 12, Haplotype mapping, Volume 3). This approach uses the historical recombination that has occurred over many generations within a given population, rather than the recombination observed within two or three generations in a family. The limitation has now become the reliance on SSR markers and the accompanying uncertainty regarding phase in wider populations. The use of the more stable SNP markers will greatly improve our ability to fine map genes.

Table 2 Examples of genes underlying or associated with QTL in livestock

Gene Trait
AcylCoA: diacylglycerol acyltransferase 1 (DGAT1) Milk composition (cattle)
Myostatin Double-muscling (cattle)
Thyroglobulin Ryanodine receptor Marbling score (cattle) Stress susceptibility (pigs)

Estrogen receptor

Fatness (cattle) Litter size (pigs)

4. Future directions

The vast majority of the QTL work that has been done to date has relied on microsatellite markers coupled with interval mapping approaches. In order to fine-map many of the QTL that have been identified, SNP markers coupled with populationwide IBD methodologies will prove useful in the future. Linkage disequilibrium mapping (Jorde, 2000) also holds great promise in this regard. The completion of the bovine sequence and the analysis of the data generated will make application of these various techniques more feasible in the future.

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