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Identifying the genes and mutations responsible for inherited diseases can provide information on the disease mechanism and possible preventative and therapeutic measures. Three

strategies for the selection of candidate genes for an inherited trait are the traditional

candidate gene strategy, the comparative genomics strategy, and the positional candidate gene strategy (Zhu and Zhao, 2007).

1.9.2 Traditional Candidate Gene Strategy

The traditional approach considers all known genes of the organism of interest and selects those which are known to have functions relevant to the trait. Each candidate gene is

analysed to find variations associated with the trait, and then the mechanism of the formation of the trait from the mutation is investigated.

The first and most famous application of this strategy was in the study of sickle-cell anaemia (Pauling et al, 1949). This condition involves changes to the shape of red blood cells when oxygen is removed from them. Since haemoglobin is the protein involved in binding oxygen in the red blood cells, it was selected as the candidate protein for investigation. The

investigation pre-dated the discovery of the genetic code so it was carried out on proteins instead of DNA. A difference was found in the primary structure of haemoglobin from sickle cell patients. This was the first time a genetic disease had been linked to the mutation of a specific protein.

The candidate gene approach worked in this case because the link between the pathology of sickle-cell anaemia and haemoglobin was clear. There are many other traits with the same clear connection to known genes that have been successfully investigated using the same approach. However, this approach relies on biochemical and functional knowledge about the traits which is not always available, making the selection of candidate genes from the large number of known genes difficult (Zhu and Zhao, 2007).

1.9.3 Comparative Genomics Strategy

The comparative genomics strategy takes advantage of homologous genes in other organisms which are known to be responsible for similar traits. Information on the genetics of humans, mice, rats, and other species which have been the subjects of extensive study has frequently been used to select candidate genes for traits in livestock (Zhu and Zhao, 2007).

An example is the double-muscled phenotype in cattle. The myostatin gene was selected as a candidate for this trait because a mouse model which lacked the gene showed a similar

phenotype to the cattle (Grobet et al, 1997). A homologue to the mouse myostatin gene was subsequently discovered in cattle. Sequencing of the cattle myostatin gene revealed an 11- base deletion leading to a premature stop codon which prevents the synthesis of functional myostatin protein and causes the double-muscled phenotype (Grobet et al, 1997).

A similar approach was used in this study to select chromosomes for genome scanning, as described in Chapter 3.

1.9.4 Positional Candidate Gene Strategy

The positional candidate gene strategy involves using the genetic linkage techniques

described in section 1.6 to determine the chromosome region linked to the disease. Candidate genes are then chosen from the identified chromosomal region (set of genes in the region of interest), not from the entire genome (Zhu and Zhao, 2007). This approach can be combined with the comparative genomic approach to select genes in the region of interest which are associated with similar traits in other organisms.

An example is the identification of a gene for milk yield in cattle, DGAT1 (Grisart et al, 2002). A quantitative trait locus (QTL) for milk yield was mapped to a 3cM interval on the telomeric end of cattle chromosome 14. DGAT1 was known to be involved in triglyceride synthesis and a mouse model lacking it did not lactate. The bovine homologue of the gene was shown to lie in the region that contained the QTL, making it a positional candidate gene. When the gene was sequenced in several animals, a mutation was discovered which changed a conserved amino acid. This mutation was associated with a decrease in milk yield along with an increase in fat yield, as predicted from the QTL, so it was concluded to be the gene

microphthalmia in homozygotes. The gene responsible was mapped to an 8.6 megabase region on rat chromosome 2 which contained the gene GJA8. This gene codes for the gap junction protein connexin 50, which was known to be responsible for cataracts in humans (Shiels 1998). A mutation was discovered in the gene which changed an amino acid and segregated perfectly with the mutant phenotypes (Liška et al, 2008). It was therefore concluded that GJA8 was responsible for the SHR-Dca cataract. As described in Section 3.2.3, chromosome 1 is the most likely location for GJA8 in sheep, and markers on this chromosome show no significant linkage to OHC. Therefore GJA8 has been ruled out as the gene associated with OHC.

1.9.5 Single Nucleotide Polymorphism Chips

Single nucleotide polymorphisms (SNPs) are natural variations in single nucleotides that occur throughout the genome. They can be used as markers in a similar way to microsatellites, although they are more difficult to detect. Recently SNP chips consisting of arrays of

molecular probes have become available which allow hundreds of thousands of SNPs to be genotyped simultaneously, providing linkage data for inherited traits across the entire genome (Schymick et al, 2007). This method of analysis is faster and more informative than genome scans using microsatellites due to the closer spacing of SNPs. SNP chips were first created for human SNPs, but chips containing probes for sheep SNPs have recently been released (Smith et al, 2010). Sheep SNP chips were not available at the time the genome scan in this

investigation was carried out, but they would be the most efficient method if the investigation was repeated now.

1.9.6 Next Generation Sequencing

Next generation sequencing refers to systems that produce hundreds of thousands of sequencing reactions in parallel, as opposed to capillary sequencing which generates one sequence at a time (reviewed in Turner et al, 2009). Sequence data can be combined to give continuous sequences of up to 2 kilobases, much longer than from capillary sequencing (Turner et al, 2009). Several different systems are used, mostly involving microscale solid phase components such as beads, cells, or wells. Next generation sequencing makes sequencing the entire genome of an organism much faster and cheaper. Already positional candidate approaches in the genetics of organisms such as yeast are declining in popularity

because it is easier to sequence the entire genome and search for variations. It is expected that the same approach will eventually be possible in organisms with larger genomes such as vertebrates (Turner et al, 2009). Even with current technology it is possible to sequence entire linked regions from linkage studies instead of individually testing candidate genes. No next generation sequencing equipment was available at the time this investigation was carried out.