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P Onkamo

Publications and source records attributed to P Onkamo.

10 recordsLinked to original sources
PubMed2006

Genome scan on Swedish Alzheimer's disease families.

Alzheimer's disease (AD) is an age-related disease, which affects approximately 40% of the population at an age above 90 years. The heritability is estimated to be greater than 60% and there are rare autosomal dominant forms indicating a significant genetic influence on the disease process. Despite the successes in the early 1990s when four genes were identified, which directly cause the disease (APP, PSEN1 and PSEN2) or greatly increase the risk of disease development (APOE), it has proved exceedingly difficult to identify additional genes involved in the pathogenesis. However, several linkage and association studies have repeatedly supported the presence of susceptibility genes on chromosomes (chrms) 9, 10 and 12. The study populations have, however, mostly been of great genetic heterogeneity, and this may have contributed to the meagre successes in identifying the disease associated genetic variants. In this study, we have performed a genome wide linkage study on 71 AD families from the relatively genetically homogeneous Swedish population where it is also possible to study the genetic ancestry in public databases. We have performed nonparametric linkage analyses in the total family material as well as stratified the families with respect to the presence or absence of APOE varepsilon4. Our results suggest that the families included in this study are tightly linked to the APOE region, but do not show evidence of linkage to the previously reported linkages on chrms 9, 10 and 12. Instead, we observed the next highest LOD score on chromosome 5q35 in the total material. Further, the data suggest that the major fraction of families linked to this region is APOE varepsilon4 positive.

Aged↗
PubMed2005

An empirical comparison of case-control and trio based study designs in high throughput association mapping.

Motivated by high throughput genotyping technology, our aim in this study was to experimentally compare the power and accuracy of case-control and family trio based approaches for haplotype based, large scale, association gene mapping. We compared trio based and case-control study designs in different disease models, and partitioned the performance differences into separate components: those from the sample ascertainment, the effective sample size, and the haplotyping approaches. For systematic and controlled tests, we simulated a rapidly expanding and relatively young isolated population. The experiments were also replicated with real asthma data. We used computationally efficient methods that scale up to large amounts of both markers and individuals. Mapping is based on a haplotype association test for haplotypes of 1-10 markers. For population based haplotype reconstruction, we use HaploRec, and compare it to both a simple trio based inference and true haplotypes. Firstly and surprisingly, statistically inferred population based haplotypes can be equally powerful as true haplotypes. Secondly, as expected, the effective sample size has a clear effect on both gene detection power and mapping accuracy. Thirdly, the sample ascertainment method does not have much effect on mapping accuracy. Finally, an interesting side result is that the simple haplotype association test clearly outperformed exhaustive allelic transmission disequilibrium tests. The results suggest that the case-control design is a powerful alternative to the more laborious family based ascertainment approach, especially for large datasets, and wherever population stratification can be controlled.

Algorithms↗
PubMed2002

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.

Chromosome Mapping↗
PubMed2001

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.

Algorithms↗
PubMed2001

Bayesian association mapping for quantitative traits in a mixture of two populations.

We introduce a novel Bayesian approach to estimate and account for population structure simultaneously with association mapping of multiple quantitative trait loci. The method is designed for an analysis of unrelated individuals from a mixture of two populations (no admixture), where the individual population memberships are unknown. In our approach, the population structure is estimated and accounted for by using data on additional "grouping" markers which are assumed to be in Hardy-Weinberg equilibrium within the populations but have different allele frequencies between the populations. We use Bayesian hierarchical modeling and Markov chain Monte Carlo estimation, where we allow both population stratification and genetic heterogeneity. In our model the number of quantitative trait loci and their positions are treated as random variables, and we obtain their posterior distributions. Here we select the candidate and the grouping markers based on results from a preliminary SOLAR analysis.

Bayes Theorem↗
PubMed2000

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.

Adolescent↗
PubMed1999

A Bayesian Markov chain Monte Carlo approach to map disease genes in simulated GAW11 data.

A Bayesian method for multipoint mapping of disease genes based on Markov chain Monte Carlo algorithms was applied to the simulated GAW11 data (Study 2). The method is based on repeated Gibbs and more general Metropolis-Hastings steps. For simplicity we assumed a single disease locus model with two alleles. A normal distribution for the underlying latent variable of the qualitative phenotype was assumed. Based on a single replicate of the data no clear evidence of any of the genes underlying the simulated disease was found. However, when three replicates were combined the method was able to locate the locus C correctly on chromosome 3.

Algorithms↗
PubMed1999

Worldwide increase in incidence of Type I diabetes--the analysis of the data on published incidence trends.

AIMS/HYPOTHESIS: Several reports on the incidence of Type I (insulin-dependent) diabetes mellitus have suggested that the incidence is increasing. The aim of this study was to find out whether the incidence is increasing globally or restricted to a selected populations only and to estimate the magnitude of the change in incidence. METHODS: During 1960 to 1996 37 studies in 27 countries were carried out. To fulfil the inclusion criteria the study periods ranged from 8-32 years. The temporal trend was fitted by linear regression, with the logarithm of the age-standardized incidence as the dependent variable and the calendar year as the independent variable. Then, the regression coefficient (x 100%) is approximately the average relative increase in incidence per year (as percentage). RESULTS: Results from the pooled data from all 37 populations showed that the overall increase in incidence was 3.0% per year (95% CI 2.6; 3.3, p = 0.0001). The statistically significant increase was found in 24 of 37 populations including all high incidence (> 14.6 per 100000 a year) populations. The relative increase was, however, steeper in the populations with a lower incidence. The correlation between logarithm of the incidence and the increase in incidence was r = -0.56, p = 0.0004. CONCLUSION/INTERPRETATION: The incidence of Type I diabetes is increasing worldwide both in low and high incidence populations. By the year 2010 the incidence will be 50 per 100000 a year in Finland and also in many other populations it will exceed 30 per 100000 a year.

Adolescent↗
PubMedDate not supplied

Estimation of transmission probabilities in families ascertained through a proband with variable age-at-onset disease: application to the HLA A, B and DR loci in Finnish families with type 1 diabetes. The DiMe Study Group.

An open problem of some interest in the study of HLA has been the possible existence of transmission distortion in the human HLA complex. In this paper, transmission probabilities are estimated and tested using data on HLA A, B and DR loci genotypes of parents and offspring ascertained from the entire population of Finland (Childhood Diabetes in Finland Study) through one or more offspring diagnosed with insulin-dependent diabetes mellitus (IDDM) during the recruitment period from September 1986 to July 1989. First, we show how to get unbiased estimates of transmission probabilities from the family data collected in the disease registry of incident cases. This is accomplished by assuming that transmission of HLA genes to children in the general population is conditionally independent given the parents' genotypes, and the birth dates of all offspring. Based on the sampling (ascertainment) process in the study on Childhood Diabetes in Finland, younger siblings of the index child (the oldest proband) are independent of the ascertainment and therefore give rise to unbiased inference regarding allele transmission. The hypothesis of Mendelian transmission of alleles at each locus was tested using the standard chi(2) test. Goodness-of-fit of the Mendelian inheritance model to the individual locus data is calculated by maximizing the likelihood function over allele transmission intensities at each locus. The existence of a strong transmission distortion is not supported by this study at the loci considered.

Adolescent↗