SeriesGenetic linkage studies
Section snippets
Parametric linkage analysis
Parametric or model-based linkage analysis is the analysis of the cosegregation of genetic loci in pedigrees. Loci that are close enough together on the same chromosome segregate together more often than do loci on different chromosomes. Loci on different chromosomes segregate together purely by chance. Each genotype for one genetic marker or locus is made up of two alleles, one inherited from each parent. Specific alleles are in gametic phase when they are coinherited from the same parent—ie,
Ehlers-Danlos disease
As an example, figure 1 shows a pedigree segregating a form of the Ehlers-Danlos disease (EDS-VIII [MIM 130080]). We will use the reported linkage analysis of this pedigree3 to illustrate parametric linkage analysis. EDS-VIII is a very rare autosomal dominant disorder. 72 individuals from five generations were clinically examined in this family, and DNA samples were available for genetic analysis from 19 of them. Figure 1 shows only those parts of the pedigree segregating the disease (ie, many
LOD scores
Linkage is usually reported as a logarithm of the odds (LOD) score (panel 1). This score was first proposed by Morton in 1955.5 It is a function of the recombination fraction (θ) or chromosomal position measured in cM. This means that the LOD score is different depending upon which value of θ is being considered. Large positive scores are evidence for linkage (or cosegregation), and negative scores are evidence against. To calculate a LOD score a model for disease expression must be specified.
Specifying the genetic model
For any parametric linkage analysis, the genetic model for the disease of interest must be specified. For a simple mendelian disease, this model amounts to mode of inheritance and frequency of disease allele. For some diseases, carrying the risk genotype does not always result in the individual being affected (incomplete penetrance). In more complex models, only a proportion of disease cases are due to a specific major gene, resulting in some risk of disease for individuals with any disease
Genetic heterogeneity
The fact that the pattern of disease in families is consistent with a strong major gene component does not necessarily imply that only one gene is involved. There are many examples of diseases caused by inherited mutations in distinct genes. Some mutations give rise to the same disease but with a different mode of inheritance—for example, Charcot-Marie-Tooth disease has autosomal recessive, dominant, and X-linked forms, and mutations in up to ten genes are responsible for the different forms.8
Heterogeneity LOD scores
Locus heterogeneity such as that with Charcot-Marie-Tooth disease can seriously affect the power of parametric linkage analysis. The most common solution is to assume that mutations in the disease genes will be so rare that each family will be linked to only one such gene. The genome scan is then done maximising a heterogeneity LOD score (panel 1). At each genomic position, the heterogeneity LOD score is maximised with respect to another parameter, α: the proportion of families linked to this
Model-free (non-parametric) linkage analysis
For multifactorial diseases, where several genes (and environmental factors) might contribute to disease risk, there is no clear mode of inheritance. Methods to investigate linkage have therefore been developed that do not require specification of a disease model. Such methods are referred to as non-parametric, or model-free. The rationale is that, between affected relatives excess sharing of haplotypes that are identical by descent (IBD) in the region of a disease-causing gene would be
Sibling pairs
The simplest approach is to study sibling pairs, both of whom are affected. At any locus, according to the null hypothesis of no linkage, the number of IBD alleles shared by a pair of siblings is none with probability 0·25, one with probability 0·5, or two with probability 0·25 (panel 2). If IBD sharing in the families is known, the observed proportions of pairs sharing no, one, and two alleles at a candidate locus can be compared with these expectations. Linkage would be suggested if the pairs
Other groups of relatives
Pairwise comparisons between relatives can easily be modified for types of relative pair other than siblings. However, in studies that set out to examine affected sibling pairs, additional affected siblings are often recruited. Various methods have been proposed to extend the pairwise approach to sibships larger than two. Selecting one pair at random or using only independent pairs means discarding information, so using all possible pairs is preferred. Should larger sibships be down-weighted to
Issues of power and interpretation
A fundamental issue in understanding the results of a linkage analysis is the interpretation of statistical significance. Whenever statistical tests are done, a balance must be struck between making claims many of which fail to be substantiated and adopting criteria so stringent that true findings are missed. For the parametric analysis of single gene disorders, it was suggested early that a threshold of 3 for the LOD score indicated a significant result at the genome-wide level. This approach
Choice of phenotype
Some traits or diseases have a clear phenotype definition. For simple mendelian traits, it is straightforward to identify affected and unaffected individuals and even in a disease such as cancer, once symptoms are experienced the diagnosis is based on pathological findings. However, other illnesses such as psychiatric disorders are more problematic because the diagnosis often depends upon several distinct symptoms, and there is often disagreement as to what constitutes a definitive diagnosis.43
Linkage analysis: what next?
A linkage analysis of the whole genome can identify regions that show evidence of containing a disease gene. In the study of mendelian traits, crossover events often narrow down the region sufficiently to define a small interval of interest. Linkage analysis of complex diseases can only identify large regions (typically tens of cM). Location estimates indicated by the linkage peak are highly variable, and increasing the density of the marker map only somewhat improves the resolution.48 Although
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