Use of SNP for Whole Genome Selection in Cattle, Page 5

Dairy December 07, 2011 Print Friendly and PDF

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SNP Evaluation Systems (continued)

Limitations of SNP Selection

The main limitation of SNP selection is having good phenotypic information from a large number of animals in the population of interest (Hayes et al. 2009). For example, accuracy improved markedly when the number of progeny-tested American Holstein bulls increased from 1,151 to 3,576 (VanRaden et al. 2009). Very few populations of cattle have thousands of accurately progeny-tested bulls whose phenotypes can be matched with their SNP profiles. A related major limitation is that a separate system must be set up for each population, at least for best accuracy; for example, a system developed for Holsteins is essentially useless for evaluating Jersey cattle (see Hayes et al. 2009). Ironically, most of the same alleles are desirable in both breeds, but most of the SNPs are different, at least in how they match up to alleles, due to meiotic events over the centuries since Holsteins and Jerseys diverged (see below). Thus, while still useful, the SNP system developed for American Jerseys is much less accurate and useful than that for Holsteins, almost entirely because there are fewer accurately progeny-tested sires available. Note that information from dead animals also can be used as long as there is tissue available, such as frozen semen, for producing a SNP profile. On the other hand, the more that populations overlap, the more valid is the application of a particular SNP system. For example, the system developed for American Holsteins will likely be fairly accurate for European Holsteins since there is considerable overlap in their genetic makeup.

A theoretical limitation of SNPs is their bi-allelic nature. For many genes, there are more than two alleles in the population, so other markers such as microsatellites could be more informative than SNPs. However, it is unclear if this is of much practical consequence since multiple SNPs marking a particular gene might be fairly good at identifying multiple alleles of that gene, especially with the higher density SNP chips.

Another problem with SNP analyses is that they degrade very slightly with each new generation in the population, due to the crossing over that occurs during meiosis. If a crossing-over event occurs between a marker and the allele it is marking, one not only gets the wrong information, but it is exactly opposite, which equates to selection for the undesirable allele. Such events are rare in a statistical sense and of little consequence in applying this technology over several generations but eventually will cause problems. Fortunately, this will be a minor practical problem because SNP analysis systems likely will be re-derived on a regular basis as information becomes available on new sets of progeny-tested bulls (although less accurate, females also can contribute SNP and phenotypic information to such systems). As crossover events differ in the different populations, SNP analysis must be done within populations. The same is true for marker-assisted selection, which is most valid within families and less valid as the family (or population) becomes more extensive. It is possible to use less homogeneous populations, e.g., beef cattle instead of Angus cattle by using more SNPs, but a 250K SNP chip may be needed for equivalent accuracy to a 50K SNP chip for the Angus breed.

Intellectual Property Issues

A major limitation of SNP evaluation systems is that they are very expensive to set up, especially in developing and validating SNP chips. Once set up, the cost of making and analyzing each additional SNP chip often will be less than US $100. However, millions of dollars are required to set up the system to make the chips and then to set up the analysis system. While these costs likely will decline dramatically with time, high cost is a current reality. This also means that patents and other intellectual property issues arise, as those who spent the millions of dollars seek to recoup their investments. For example, applications of the Illumina 50K SNP chip using certain dairy cattle information in North America is proprietary and can only be used by the owners of the six bull studs that paid for development of this technology to select bulls for progeny testing through the year 2013. SNP chips developed in different countries also represent a huge duplication of an expensive procedure. For both logistical and intellectual property reasons, prospects for sharing such genomic information appear remote, at least for the next several years. This is in contrast to the widespread sharing of progeny test information. Almost all SNP technology has intellectual property constraints; in most cases, users pay indirectly by the costs of semen and other fees.

 


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This work is supported by the USDA National Institute of Food and Agriculture, New Technologies for Ag Extension project.