Minimum description length block finder, a method to identify haplotype blocks and to compare the strength of block boundaries.

We describe a new probabilistic method for finding haplotype blocks that is based on the use of the minimum description length (MDL) principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly and colleagues. The results expose some problems that exist in the current methods for the evaluation of the significance of predicted block boundaries. Our method, MDL block finder, can be used to compare block borders in different sample sets, and we demonstrate this by applying the MDL-based method to define the block structure in chromosomes from population isolates.

An MDL method for finding haplotype blocks and for estimating the strength of haplotype block boundaries.

We describe a new method for finding haplotype blocks based on the use of the minimum description length principle. We give a rigorous definition of the quality of a segmentation of a genomic region into blocks, and describe a dynamic programming algorithm for finding the optimal segmentation with respect to this measure. We also describe a method for finding the probability of a block boundary for each pair of adjacent markers: this gives a tool for evaluating the significance of each block boundary. We have applied the method to the published data of Daly et al. The results are in relatively good agreement with the published results, but also show clear differences in the predicted block boundaries and their strengths. We also give results on the block structure in population isolates.

Association analysis for quantitative traits by data mining: QHPM.

Previously, we have presented a data mining-based algorithmic approach to genetic association analysis, Haplotype Pattern Mining. We have now extended the approach with the possibility of analysing quantitative traits and utilising covariates. This is accomplished by using a linear model for measuring association. We present results with the extended version, QHPM, with simulated quantitative trait data. One data set was simulated with the population simulator package Populus, and another was obtained from GAW12. In the former, there were 2-3 underlying susceptibility genes for a trait, each with several ancestral disease mutations, and 1 or 2 environmental components. We show that QHPM is capable of finding the susceptibility loci, even when there is strong allelic heterogeneity and environmental effects in the disease models. The power of finding quantitative trait loci is dependent on the ascertainment scheme of the data: collecting the study subjects from both ends of the quantitative trait distribution is more effective than using unselected individuals or individuals ascertained based on disease status, but QHPM has good power to localize the genes even with unselected individuals. Comparison with quantitative trait TDT (QTDT) showed that QHPM has better localization accuracy when the gene effect is weak.

Expression of myeloid-specific genes in childhood acute lymphoblastic leukemia - a cDNA array study.

Several specific cytogenetic changes are known to be associated with childhood acute lymphoblastic leukemia (ALL), and many of them are important prognostic factors for the disease. Little is known, however, about the changes in gene expression in ALL. Recently, the development of cDNA array technology has enabled the study of expression of hundreds to thousands of genes in a single experiment. We used the cDNA array method to study the gene expression profiles of 17 children with precursor-B ALL. Normal B cells from adenoids were used as reference material. We discuss the 25 genes that were most over-expressed compared to the reference. These included four genes that are normally expressed only in the myeloid lineages of the hematopoietic cells: RNASE2, GCSFR, PRTN3 and CLC. We also detected over-expression of S100A12, expressed in nerve cells but also in myeloid cells. In addition to the myeloid-specific genes, other over-expressed genes included AML1, LCP2 and FGF6. In conclusion, our study revealed novel information about gene expression in childhood ALL. The data obtained may contribute to further studies of the pathogenesis and prognosis of childhood ALL.

Offspring risk and sibling risk for multilocus traits.

The recurrence risk of a trait in a relative of type R is the probability that an individual who is in relationship of type R to an affected proband has the trait. It is intuitively clear that closer relationships lead to higher recurrence risks. However, no exact analysis of this phenomenon has been presented for multilocus traits. We prove a theorem that shows how recurrence risks are influenced by the degree of closeness of the relationship R. For example, our theorem implies that sibling risk is always higher than offspring risk. The loci influencing the trait are assumed to be autosomal and unlinked, but arbitrary epistasis between the loci is allowed. We give a detailed proof of the theorem by using stochastic matrices. A shorter proof based on the additive and dominance genetic variances is also sketched. Additionally, we also give some empirical results and discuss generalizations of the theorem.

Mining associations between genetic markers, phenotypes, and covariates.

We used Haplotype Pattern Mining, HPM [Toivonen et al., Am J Hum Genet 67:133-45, 2000], for gene localization in Genetic Analysis Workshop (GAW) 12 isolate data. In HPM, association is analyzed by searching all trait-associated haplotype patterns. Data mining algorithms are utilized to make the search efficient. The strength of the haplotype-trait associations is measured by a linear model, into which a pre-seelected set of covariates is incorporated. Marker-wise patterns of association are used for predicting the disease gene location. Genome-wide scans of susceptibility genes for affection status as well as for the quantitative traits (Q1-Q5) were performed. First analyses were made with small sample sizes, 63-94 trios per trait, which is compared with a pilot study of a larger complex disease-mapping project. Subsequently, the analysis was repeated with approximately 600 cases and 600 controls per trait to give higher power to the analyses. With small sample sizes, only the susceptibility genes having the strongest effects on the traits could be localized. The larger sample size gave very good results: all susceptibility genes, except one, could be correctly localized. First experiments on candidate genes suggested that HPM is applicable even to fine mapping of mutations in DNA sequence.

The IL9R region contribution in asthma is supported by genetic association in an isolated population.

Interleukin 9 (IL9) is involved in mast cell maturation and the enhancement of IgE production by B cells. Furthermore, linkage data in human and mice have suggested that IL9 may contribute to asthma. Since our genetic analysis of the 5q cytokine cluster did not support a genetic role for the IL9 gene, we became interested in the IL9 receptor gene (IL9R) in the pseudoautosomal region. We genotyped markers sDF2 and sDF1 close to the IL9R gene among 289 affected and 368 family-based controls. The results were studied by using linkage, transmission disequilibrium, association and homozygosity analyses. Linkage analyses remained negative, presumably because of our low power for linkage study. However, all the other analyses yielded evidence that the IL9R gene region may have a role in the development of asthma. The sDF2*10 allele was more frequently transmitted than untransmitted to asthmatic offspring (34 vs 16, pchi2 < or = 0.01), and it was found homozygotic among asthma patients more often than expected (Psimul2 = 0.009). Also, a specific X chromosomal haplotype, sDF2*10-sDF1*6 associated with asthma (40 vs 7, Pchi2 < 0.005, Psimul1 = 0.04).

Data mining applied to linkage disequilibrium mapping.

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.

Association study of the chromosomal region containing the FCER2 gene suggests it has a regulatory role in atopic disorders.

On the basis of studies with animal models, the gene for the low-affinity receptor for immunoglobulin E (IgE) (FCER2, CD23) has been implicated as a candidate for IgE-mediated allergic diseases and bronchial hyperreactivity, or related traits. Given evidence for genetic complexity in atopic disorders, we sought to study two European subpopulations, Finnish and Catalonian. We studied three phenotypic markers: (1) total serum IgE level; (2) asthma; and (3) specific IgE level for a mixture of the most common aeroallergens in Finland. Altogether, eight polymorphic markers spanning a region of 10 cM around the FCER2 gene on chromosome 19p13 were analyzed in 124 families. The physical order of the markers and the location of the FCER2 gene were confirmed by using radiation hybrids. The allele and haplotype association study showed a suggestive haplotype association (significance of p

A genomewide screen for schizophrenia genes in an isolated Finnish subpopulation, suggesting multiple susceptibility loci.

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.