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4.1 Introduction

Having observed elevated rates of recombination within PGMl through comparative genetic and physical mapping and confirming earlier proposals (Wetterling, 1990; Yip et al, 1999b) of elevated rates of recombination within the human PGMl gene, it was necessary to characterize the region more precisely. Patterns of linkage disequilibrium (LD) were mapped across the previously uncharacterized Site B, a region of 38.5 kb bounded by the N1 and 2/1 markers (Figure 1.14). Earlier studies suggested the presence of a hot spot within this region, derived from the collapse in LD between the N1 and 2/1 sites, discussed in Chapter 1.

Additional SNPs in Site B were therefore generated to fine map some previously identified Site B crossovers in CEPH families and identify additional recombinant chromosomes in the CEPH and the multi-generation families (discussed in Chapter 5). Furthermore, these additional SNPs were used in an attempt to define the recombinogenic activity across the region by way of association analyses (Chapter

6).

4.1.1 Single nucleotide polymorphisms (SNPs)

Single base mutations alter genomic DNA sequence at a given point as a nucleotide substitution, insertion or deletion. Mutations occurring at a frequency greater than one percent in the population is referred to as single nucleotide polymorphisms (SNPs) and account for the majority of human sequence variation that occurred once in the history of mankind (Wang et a l, 1998; Cargill et al, 1999).

From an evolutionary standpoint, polymorphisms represent the transient state between the origin of genetic variation by mutation and the loss of variation by fixation of either the ancestral or derived state (Kirk et al, 2002). Whilst SNPs in their most severe form cause missense mutations that alter amino acid sequences or impede RNA splicing leading to disease or altered phenotypes, others have become a major tool in molecular genetic studies.

The earliest genetic markers were, what we now call SNPs; coding SNPs (cSNPs) that altered the protein product allowing variation to be measured by electrophoretic methods. The most recent generation of SNPs include those in non­ coding regions and ones that do not always abolish or create a restriction site, but are

extremely widespread throughout the human genome, becoming the most common source o f human genetic variation.

Most recently it has been estimated that 5.3 million common SNPs, each with a frequency of 10-50%, account for the bulk of DNA differences; such SNPs present themselves once every 600 bp within the human genome (Kruglyak and Nickerson, 2001). The maximum heterozygosity of these bi-allelic SNPs is 0.5, yet the low informativity o f these DNA markers is more than offset by the fact that they are present in such high numbers across the genome. It is valuable to note that earlier estimates of the average heterozygosity per locus in humans was based on the incidence of enzyme polymorphisms and was calculated to be 0.067 (Harris and Hopkinson, 1972).

The potential use o f SNPs for genetic mapping of complex traits, pharmocogenetics and medical diagnostics is a very important issue, and is discussed further in chapter 6.

4.1,2 Techniques fo r SN P detection

Many modes o f SNP detection exist amid the scientific arena today and are all instrumental, requiring robust, reliable and economical methods for high throughput analyses. Methods for mutation detection can be categorized as scanning methods that detect previously unknown nucleotide differences and as discrimination methods designed to detect specific known mutations or polymorphisms. Several DNA scanning methods (the search for unknown mutations in a DNA sequence) exist and many are being modified to allow for high throughput as the number o f disease genes and candidate genes for disease increases. Amongst these are “physical methods” which rely on physical alterations caused by mutational base changes when compared to reference DNA; these include denaturing gradient gel electrophoresis (DGGE), single strand conformation analysis/polymorphism (SSCA/P) (Orita et a l, 1989) and heteroduplex analysis (HA, discussed further in section 4.2.2). “Cleavage” methods take advantage o f hybridization of mutant and reference DNA differences at a single base pair, giving rise to heteroduplexes comprising mismatches thereby enabling cleavage method detection.

Other techniques include the classic standard sequencing (vital in defining mutations) and the revolutionary DNA chip method where inconsistencies in patterns o f hybridization between reference and test sample indicate a mutation; this is

achieved by hybridization of labeled test single strand DNA to an array o f known oligonucleotides on a very small physical substrate (Castellino, 1997).

Many different methodologies have been proposed for high throughput SNP discrimination genotyping. Multistep sample processing techniques include restriction fragment length polymorphism (RFLP) analysis (discussed further in section 4.2.1), hybridization with allele-specific probes (Conner et a l, 1983; Saiki et a l, 1985; Saiki et al, 1989), allele-specific PGR (Newton et a l, 1989), oligonucleotide ligation (Landegren et a l, 1988), allele specific ligase chain reaction (Barany, 1991) and Flap endonuclease digestion (Lyamichev et a l, 1993; Mein et a l,

2000).

More recently other techniques reducing sample processing to a single step have been developed, these include nick translation PGR (Lee et a l, 1993), fluorogenic allele-specific PGR, The Wave® analysis system (Kuklin et a l, 1997); section 4.2.2) and matrix-assisted laser desorption ionisation mass spectrometry with time-of-flight (MALDI-TOF) which determines sequence based entirely upon the inherent mass differences of the four naturally occurring bases (Haff and Smirnov, 1997; Ross et a l, 1998). However some methods must be monitored in real time requiring expensive instrumentation and/or design. Whitcombe and colleagues (1998) described a single tube SNP genotyping method using universal Taqman™ probes (Gelmini et al, 1997), discussed further in section 4.2.3.