Early environment and neurobehavioral development predict adult temperament clusters.

Background: Investigation of the environmental influences on human behavioral phenotypes is important for our understanding of the causation of psychiatric disorders. However, there are complexities associated with the assessment of environmental influences on behavior.

Methods/principal Findings: We conducted a series of analyses using a prospective, longitudinal study of a nationally representative birth cohort from Finland (the Northern Finland 1966 Birth Cohort).

Temperament clusters in a normal population: implications for health and disease.

Background: The object of this study was to identify temperament patterns in the Finnish population, and to determine the relationship between these profiles and life habits, socioeconomic status, and health.

Methods/principal Findings: A cluster analysis of the Temperament and Character Inventory subscales was performed on 3,761 individuals from the Northern Finland Birth Cohort 1966 and replicated on 2,097 individuals from the Cardiovascular Risk in Young Finns study. Clusters were formed using the k-means method and their relationship with 115 variables from the areas of life habits, socioeconomic status and health was examined.

Randomization techniques for assessing the significance of gene periodicity results.

Background: Modern high-throughput measurement technologies such as DNA microarrays and next generation sequencers produce extensive datasets. With large datasets the emphasis has been moving from traditional statistical tests to new data mining methods that are capable of detecting complex patterns, such as clusters, regulatory networks, or time series periodicity. Study of periodic gene expression is an interesting research question that also is a good example of challenges involved in the analysis of high-throughput data in general.

Evaluation of BIC and cross validation for model selection on sequence segmentations.

Segmentation is a general data mining technique for summarising and analysing sequential data. Segmentation can be applied, e.g.

Mixture model clustering of phenotype features reveals evidence for association of DTNBP1 to a specific subtype of schizophrenia.

Background: While DTNBP1, DISC1, and NRG1 have been extensively studied as candidate genes of schizophrenia, results remain inconclusive. Possible explanations for this are that the genes might be relevant only to certain subtypes of the disease and/or only in certain populations.

Methods: We performed unsupervised clustering of individuals from Finnish schizophrenia families, based on extensive clinical and neuropsychological data, including Structured Clinical Interview for DSM-IV (SCID) information.

Lower extinction risk in sleep-or-hide mammals.

An ever larger proportion of Earth's biota is affected by the current accelerating environmental change. The mismatches between organisms and their environments are now increasing in both magnitude and frequency, resulting in lowered fitness and hence the decline of populations. Under this scenario, species with behavioral and/or physiological traits that provide them shelter from the environment are predicted to be less vulnerable to population declines than species that are always exposed to the elements.

Determining significance of pairwise co-occurrences of events in bursty sequences.

Background: Event sequences where different types of events often occur close together arise, e.g., when studying potential transcription factor binding sites (TFBS, events) of certain transcription factors (TF, types) in a DNA sequence.

Higher origination and extinction rates in larger mammals.

Do large mammals evolve faster than small mammals or vice versa? Because the answer to this question contributes to our understanding of how life-history affects long-term and large-scale evolutionary patterns, and how microevolutionary rates scale-up to macroevolutionary rates, it has received much attention. A satisfactory or consistent answer to this question is lacking, however. Here, we take a fresh look at this problem using a large fossil dataset of mammals from the Neogene of the Old World (NOW).

Evaluation of HapMap data in six populations of European descent.

We studied how well the European CEU samples used in the Haplotype Mapping Project (HapMap) represent five European populations by analyzing nuclear family samples from the Swedish, Finnish, Dutch, British and Australian (European ancestry) populations. The number of samples from each population (about 30 parent-offspring trios) was similar to that in the HapMap sample sets. A panel of 186 single nucleotide polymorphisms (SNPs) distributed over the 1.

Comparing segmentations by applying randomization techniques.

Background: There exist many segmentation techniques for genomic sequences, and the segmentations can also be based on many different biological features. We show how to evaluate and compare the quality of segmentations obtained by different techniques and alternative biological features.

Results: We apply randomization techniques for evaluating the quality of a given segmentation.

Constrained hidden Markov models for population-based haplotyping.

Background: Haplotype Reconstruction is the problem of resolving the hidden phase information in genotype data obtained from laboratory measurements. Solving this problem is an important intermediate step in gene association studies, which seek to uncover the genetic basis of complex diseases. We propose a novel approach for haplotype reconstruction based on constrained hidden Markov models.

Discovering isochores by least-squares optimal segmentation.

The isochore structure of a genome is observable by variation in the G+C (guanine and cytosine) content within and between the chromosomes. Describing the isochore structure of vertebrate genomes is a challenging task, and many computational methods have been developed and applied to it. Here we apply a well-known least-squares optimal segmentation algorithm to isochore discovery.

Seriation in paleontological data using markov chain Monte Carlo methods.

Given a collection of fossil sites with data about the taxa that occur in each site, the task in biochronology is to find good estimates for the ages or ordering of sites. We describe a full probabilistic model for fossil data. The parameters of the model are natural: the ordering of the sites, the origination and extinction times for each taxon, and the probabilities of different types of errors.

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.

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.

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.

Investigatory and analytical approaches to differential gene expression profiling in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a non-Hodgkin's lymphoma of B-cell lineage. The blastoid variant of MCL, characterized by high mitotic rate, is clinically more aggressive than common MCL. We used the cDNA array technology to examine the gene expression profiles of both blastoid variant and common MCL.

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.

Genome segmentation using piecewise constant intensity models and reversible jump MCMC.

The existence of whole genome sequences makes it possible to search for global structure in the genome. We consider modeling the occurrence frequencies of discrete patterns (such as starting points of ORFs or other interesting phenomena) along the genome. We use piecewise constant intensity models with varying number of pieces, and show how a reversible jump Markov Chain Monte Carlo (RJMCMC) method can be used to obtain a posteriori distribution on the intensity of the patterns along the genome.

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.

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.

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.

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.

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.