533aberrations with extraordinary precision. It will assist in the discovery of genes and
markers important in cancer, and the discovery of loci that may be important in
inherited predispositions to disease. Together with the information from the human
genome sequence and proteomics, ROMA will provide the ability to defi ne rear-
rangements with ultra-high resolution will improve the ability to provide accurate
prognosis both prenatally and postnatally to parents of offspring with chromosomal
aberrations.
Diagnosis of Genomic Rearrangements by Multiplex PCR
Germline and somatic genomic rearrangement play a relevant role in the pathogen-
esis of genetic disorders, and their identifi cation is a fundamental task in molecular
diagnosis. However, screening for structural genomic abnormalities is often not
included in routine mutational analyses and consequently the proportion of rear-
rangements playing a pathogenic role in several genetic disorders is likely to be
underestimated. A wide range of molecular techniques for the detection of large
genomic rearrangements has been developed: some have the power to screen the
whole genome, others are designed to analyze one or few loci that are known to be
involved in a specifi c disease; some may detect balanced rearrangements, while
others only unbalanced rearrangements; some are suitable for detection of germline
abnormalities, yet others also detect somatic abnormalities. Multiplex PCR-based
protocols are currently employed in routine detection of extended germline genomic
deletions or duplications (De Lellis et al. 2008 ).
Mutation Detection Technologies
Procedures for mutation detection can be separated into two distinct groups. The
fi rst group consists of methods to scan sequences for all mutations including known
and unknown disease causing alleles. The second consists of single nucleotide poly-
morphism (SNP) technologies (see Chap. 2 ), which effi ciently detect known and
common disease causing alleles and are described in the next section. Some of the
technologies in two groups overlap.
Traditionally SNP technologies had the advantage of being able to detect known
mutations inexpensively, and with high reproducibility. The problem with using
these technologies in general is the inability to detect rare disease causing muta-
tions, which on the whole, can account for a signifi cant number of diseased indi-
viduals. An example is the cystic fi brosis (CF) testing services based on this
technology, which fails to identify a large number of disease causing alleles, espe-
cially in rare CF occurring ethnicities.
Traditionally the mutation scanning and sequencing methods have been expen-
sive and/or not reproducible. Mutation detection technologies are shown in
Table 16.1 and some of these are described in Chap. 2.
Molecular Diagnosis of Genetic Disorders