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Given the enormous progress in the knowledge of the human genome, genetic markers are now available throughout the genome. Haplotype analysis, allowing the simultaneous use of information from several markers, has thus become increasingly popular. However, we often face the problem of missing data and of haplotype identification. We have proposed a haplotype based method for the genetic study of multifactorial diseases in founder populations, the MILC method (Bourgain et al. 2000). MILC is based on the contrast of identity length between haplotypes transmitted to affected offspring and haplotypes non-transmitted. In this study, the impact of different strategies, regarding missing data, on the MILC method are evaluated. A real situation is considered where data are derived from a genome screen for asthma susceptibility alleles in the Hutterites. Results are illustrated on this asthma data set.
Coeliac disease (CD) is a malabsorptive disorder of the small intestine resulting from ingestion of
gluten. The HLA risk factors involved in CD are well known but do not explain the whole genetic
susceptibility. Several regions of potential linkage on chromosomes 3q, 5q, 10q, 11q, 15q and 19q
have already been reported in the literature. These six regions were analyzed with the Maximum Lod
Score method on a dense set of markers. A new sample of 89 Italian sibpairs was available for study.
There was no evidence for linkage for any of the regions tested, except for chromosome 5q. For this
region, our data, as well as a sample of 93 sibpairs from our first genome screen (Greco et al. 1998),
are compatible with the presence of a risk factor for CD with a moderate effect.
The proportions of affected sibs sharing 2, 1 or 0 identical by descent parental marker alleles have
been shown to conform to the ‘triangle constraints’ (Suarez, 1978; Holmans, 1993). It has also been
shown (Dudoit & Speed, 1999) that the constraints are verified provided certain assumptions hold.
In this study we explore a realistic situation in which the constraints fail due to the presence of a
factor in which the sibs differ, a factor on which penetrance depends. This factor may be a
characteristic of the trait (severe vs. mild form), or the presence/absence of an associated trait or an
environmental factor. We show that under such situations, using the triangle constraints may lead
to important loss of power to detect linkage by the MLS test. We propose here an alternative
approach in order to detect both linkage and heterogeneity.
The current challenge in biomedical research is to detect genetic risk factors involved in common
complex diseases. The power to detect their role is generally poor in populations that have been large
for a long time. It has been suggested that the power may be increased by taking advantage of the
specificity of founder populations; linkage disequilibrium spanning larger regions and kinship
coefficients being stronger than in large populations. A new method is proposed here, the Maximum
Identity Length Contrast (MILC) which, in contrast with other existing methods, does not make the
assumption of unique ancestry for the genetic risk factors. It is thus appropriate for a search for
common genetic risk factors for complex diseases. Statistical properties of the method are discussed
in realistic contexts.
Mapping of genes involved in rare recessive diseases is usually
difficult because of the lack of
families with more than one affected progeny. The problem may be avoided
by using inbred affected
individuals and the strategy of homozygosity mapping.
In practice, the use of homozygosity mapping in a genome-wide scan requires
that a set of markers
regularly spaced and spanning the whole genome are tested. Investigators
are then faced to the
problem of choosing the spacing of markers.
To help solve this problem, we give some useful clues by computing (1)
the expected length of the
region of identity by descent around the disease locus, (2) the distribution,
given the spacing of
markers, of the number of affected individuals expected not to be homozygous
at the marker closest
to the disease locus and, (3) the expected type-one error. We show that
even if the markers are very
closely spaced, it is not unlikely that some affected individuals in the
sample will not be homozygous
at the marker closest to the disease locus. Excluding a region by the criterion
that all affected
individuals in the sample are not homozygous may then dramatically increase
the rate of false
negatives. We thus propose to relax the criterion to declare a region candidate,
based on the sample
size and the spacing of markers.
When dominant mutations of different genes may lead to the
same disease, it is often difficult to detect in a particular patient
which gene is involved. A strategy is to make genealogical extensions
to find affected relatives that should have inherited the same
mutation. In particular, for diseases with late age of onset or short
survival time, only poor information may be obtained from close
relatives of probands and it can be particularly efficient to make
genealogical extensions to detect pairs of distantly related affected
individuals. Such a pair of affecteds may provide information
concerning the region of the genome where the mutated gene should map.
Two situations may be encountered depending on whether or not prior
information on the location of mutated genes involved in the disease
are available. If we already know, from previous linkage studies, that
a gene located in a given region R of the genome may be involved in
the disease, the problem is then to confirm that it is indeed a
mutation of this gene that is involved in the affected pair. Once the
implication of a gene in region R has been confirmed the affected pair
of relatives may give information to restrict the length of this
region R. In this paper we discuss these two points by deriving
analytically first the lod score expected and second the expected
reduction of the length of the region where the mutation is suspected
to map as a function of the number of meioses between the two affected
individuals and of the polymorphism of the markers available in the
Because of an association between sexual aneuploidies and schizophrenia, and because schizophrenic siblings have been found to be more often of the same than of the opposite sex, the susceptibility locus for schizophrenia is thought to lie within the pseudoautosomal region of the sex chromosomes. We analysed 33 sibships comprising 18 pairs, 13 trios, and 2 quartets of affected siblings, and found support for non-random segregation of alleles at the DXYS14 locus in affected siblings. These findings are consistent with the pseudoautosomal hypothesis for schizophrenia and favour a genetic linkage between DXYS 14 and the disease.
Evidence of linkage in families of bipolar patients has so far been identified with genetic markers on chromosome X and 11. However, replications of these data have not consistently been reported in either case, which favours the hypothesis of genetic heterogeneity. Therefore, we have tried to outline a sampling strategy for linkage replication in affective disorders. We estimated the average number of nuclear families required to replicate X or 11 linkage as a function of the degree of heterogeneity as well as the number to prove heterogeneity given that linkage exists. The results are presented and discussed.
Jusqu’à présent, différentes méthodes ont été utilisées pour mettre en évidence l’influence de facteurs de risque d’origine génétique: comparaison du taux de concordance chez des jumeaux monozygotes et dizygotes, études d’adoption, analyse de ségrégation et analyse de linkage. Le développement explosif de la biologie moléculaire, fournissant un nombre grandissant de marqueurs génétiques, rend particulièrement prometteuse l’application des techniques de linkage. Néanmoins, il faut être conscient que l’application de cette stratégie aux maladies psychiatriques se heurte à des difficultés spécifiques: critères diagnostiques, âge de début variable, hétérogénéité probable, corrélations intrafamiliales environnementales.
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