Schizophrenia is a severe mental disorder affecting approximately 1% of the world's population. Here, we report the results from a three-stage genomewide screen performed in a study sample from an internal isolate of Finland. An effort was made to identify genes predisposing for schizophrenia that are potentially enriched in this isolate, which has an exceptionally high lifetime risk for this trait. Ancestors of the local families with schizophrenia were traced back to the foundation of the population in the 17th century. This genealogical information was used as the basis for the study strategy, which involved screening for alleles shared among affected individuals originating from common ancestors. We found four chromosomal regions with markers revealing pairwise LOD scores>1.0: 1q32.2-q41 (Z(max)=3.82, dominant affecteds-only model), 4q31 (Z(max)=2. 74, dominant 90%-penetrance model), 9q21 (Z(max)=1.95, dominant 90%-penetrance model), and Xp11.4-p11.3 (Z(max)=2.01, recessive 90%-penetrance model). This finding suggests that there are several putative loci predisposing to schizophrenia, even in this isolate.

Developmental dyslexia is a neurofunctional disorder characterised by an unexpected diYculty in learning to read and write despite adequate intelligence, motivation, and education. Previous studies have suggested mostly quantitative susceptibility loci for dyslexia on chromosomes 1, 2, 6, and 15, but no genes have been identified yet. We studied a large pedigree, ascertained from 140 families considered, segregating pronounced dyslexia in an autosomal dominant fashion. AVected status and the subtype of dyslexia were determined by neuropsychological tests. A genome scan with 320 markers showed a novel dominant locus linked to dyslexia in the pericentromeric region of chromosome 3 with a multipoint lod score of 3.84. Nineteen out of 21 aVected pedigree members shared this region identical by descent (corrected p<0.001). Previously implicated genomic regions showed no evidence for linkage. Sequencing of two positional candidate genes, 5HT1F and DRD3, did not support their role in dyslexia. The new locus on chromosome 3 is associated with deficits in all three essential components involved in the reading process, namely phonological awareness, rapid naming, and verbal short term memory. (J Med Genet 2001;38:658-664)

Preeclampsia is a common, pregnancy-specific disorder characterized by reduced placental perfusion, endothelial dysfunction, elevated blood pressure, and proteinuria. The pathogenesis of this heterogeneous disorder is incompletely understood, but it has a familial component, which suggests that one or more common alleles may act as susceptibility genes. We hypothesized that, in a founder population, the genetic background of preeclampsia might also show reduced heterogeneity, and we have performed a genomewide scan in 15 multiplex families recruited predominantly in the Kainuu province in central eastern Finland. We found two loci that exceeded the threshold for significant linkage: chromosome 2p25, near marker D2S168 (nonparametric linkage [NPL] score 3.77; P=.000761) at 21.70 cM, and 9p13, near marker D9S169 (NPL score 3.74; P=.000821) at 38.90 cM. In addition, there was a locus showing suggestive linkage at chromosome 4q32 between D4S413 and D4S3046 (NPL score 3.13; P=.003238) at 163.00 cM. In the present study the susceptibility locus on chromosome 2p25 is clearly different (21.70 cM) from the locus at 2p12 found in an Icelandic study (94.05 cM) and the locus at 2q23 (144.7 cM) found in an Australian/New Zealand study. The locus at 9p13 has been shown to be a candidate region for type 2 diabetes in two recently published genomewide scans from Finland and China. The regions on chromosomes 2p25 and 9p13 may harbor susceptibility genes for preeclampsia.

We introduce a new method for linkage disequilibrium mapping: haplotype pattern mining (HPM). The method, inspired by data mining methods, is based on discovery of recurrent patterns. We define a class of useful haplotype patterns in genetic case-control data and use the algorithm for finding disease-associated haplotypes. The haplotypes are ordered by their strength of association with the phenotype, and all haplotypes exceeding a given threshold level are used for prediction of disease susceptibility-gene location. The method is model-free, in the sense that it does not require (and is unable to utilize) any assumptions about the inheritance model of the disease. The statistical model is nonparametric. The haplotypes are allowed to contain gaps, which improves the method's robustness to mutations and to missing and erroneous data. Experimental studies with simulated microsatellite and SNP data show that the method has good localization power in data sets with large degrees of phenocopies and with lots of missing and erroneous data. The power of HPM is roughly identical for marker maps at a density of 3 single-nucleotide polymorphisms/cM or 1 microsatellite/cM. The capacity to handle high proportions of phenocopies makes the method promising for complex disease mapping. An example of correct disease susceptibility-gene localization with HPM is given with real marker data from families from the United Kingdom affected by type 1 diabetes. The method is extendable to include environmental covariates or phenotype measurements or to find several genes simultaneously.