Biomedical subjects

Jeanette C Papp

Publications and source records attributed to Jeanette C Papp.

4 recordsLinked to original sources

PubMed2005

Chromosome 19p13 loci in Finnish migraine with aura families.

Chromosomal area 19p13 contains two migraine associated genes: a Ca(v)2.1 (P/Q-type) calcium channel alpha(1) subunit gene, CACNA1A, and an insulin receptor gene, INSR. Missense mutations in CACNA1A cause a rare Mendelian form of migraine, familial hemiplegic migraine type 1 (FHM1). Contribution of CACNA1A locus has also been studied in the common forms of migraine, migraine with (MA) and without aura (MO), but the results have been contradictory. The role of INSR is less well established: A region on 19p13 separate from CACNA1A was recently reported to be a major locus for migraine and subsequently, the INSR gene was associated with MA and MO. Our aim was to clarify the role of these loci in MA families by analyzing 72 multigenerational Finnish MA families, the largest family sample so far. We hypothesized that the potential major contribution of the 19p13 loci should be detected in a family sample of this size, and this was confirmed by simulations. We genotyped eight polymorphic microsatellite markers surrounding the INSR and CACNA1A genes on 757 individuals. Using parametric and non-parametric linkage analysis, none of the studied markers showed any evidence of linkage to MA either under locus homogeneity or heterogeneity. However, marginally positive lod scores were observed in three families, and thus for these families the results remain inconclusive. The overall conclusion is that our study did not provide evidence of a major MA susceptibility region on 19p13 and thus we were not able to replicate the INSR locus finding.

Antigens, CD↗

PubMed2002

A susceptibility locus for migraine with aura, on chromosome 4q24.

Migraine is a complex neurovascular disorder with substantial evidence supporting a genetic contribution. Prior attempts to localize susceptibility loci for common forms of migraine have not produced conclusive evidence of linkage or association. To date, no genomewide screen for migraine has been published. We report results from a genomewide screen of 50 multigenerational, clinically well-defined Finnish families showing intergenerational transmission of migraine with aura (MA). The families were screened using 350 polymorphic microsatellite markers, with an average intermarker distance of 11 cM. Significant evidence of linkage was found between the MA phenotype and marker D4S1647 on 4q24. Using parametric two-point linkage analysis and assuming a dominant mode of inheritance, we found for this marker a maximum LOD score of 4.20 under locus homogeneity (P=.000006) or locus heterogeneity (P=.000011). Multipoint parametric (HLOD = 4.45; P=.0000058) and nonparametric (NPL(all) = 3.43; P=.0007) analyses support linkage in this region. Statistically significant linkage was not observed in any other chromosomal region.

Chromosome Mapping↗

PubMed2002

Detection and integration of genotyping errors in statistical genetics.

Detection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.

Algorithms↗

PubMedDate not supplied

Merging microsatellite data.

Genotype calling procedures vary from laboratory to laboratory for many microsatellite markers. Even within the same laboratory, application of different experimental protocols often leads to ambiguities. The impact of these ambiguities ranges from irksome to devastating. Resolving the ambiguities can increase effective sample size and preserve evidence in favor of disease-marker associations. Because different data sets may contain different numbers of alleles, merging is unfortunately not a simple process of matching alleles one to one. Merging data sets manually is difficult, time-consuming, and error-prone due to differences in genotyping hardware, binning methods, molecular weight standards, and curve fitting algorithms. Merging is particularly difficult if few or no samples occur in common, or if samples are drawn from ethnic groups with widely varying allele frequencies. It is dangerous to align alleles simply by adding a constant number of base pairs to the alleles of one of the data sets. To address these issues, we have developed a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for sampling the posterior distribution under the model. Our computer program, MicroMerge, implements the algorithm and almost always accurately and efficiently finds the most likely correct alignment. Common allele frequencies across laboratories in the same ethnic group are the single most important cue in the model. MicroMerge computes the allelic alignments with the greatest posterior probabilities under several merging options. It also reports when data sets cannot be confidently merged. These features are emphasized in our analysis of simulated and real data.

Algorithms↗