Biomedical subjects

Konstantin Strauch

Publications and source records attributed to Konstantin Strauch.

10 recordsLinked to original sources

PubMed2004

High factor VIII levels in venous thromboembolism show linkage to imprinted loci on chromosomes 5 and 11.

High factor VIII (FVIII) levels are known to be a risk factor for deep venous thrombosis, but the mechanisms responsible for high FVIII levels remain unclear. Here, a new phenotype "FVIII level residuum" (FVIII-R) was defined in order to eliminate the impact of common determinants on FVIII levels. We studied 13 families of patients with thrombosis and reproducibly high FVIII levels of unknown origin. Since familial clustering was evident, we looked for a possible genetic basis. A genome scan was performed with 402 evenly spaced microsatellite markers. A quantitative linkage analysis using variance component methods showed suggestive evidence for linkage of FVIII-R with a locus on chromosome 8 (logarithm of odds [LOD] = 2.1). In addition, we performed parametric exploratory linkage analysis of dichotomized FVIII-R, taking a parent-of-origin effect into account. Single-trait-locus MOD-score analysis showed suggestive evidence for linkage under an imprinting model at chromosomes 5 and 11. Furthermore, a 2-trait-locus analysis under a multiplicative model for the loci of chromosomes 5 and 11 yielded a remarkable LOD of 4.44. It confirmed the finding of paternal imprinting, obtained by single-trait-locus analysis, at both loci. Our results suggest that high FVIII levels in venous thromboembolism represent a complex trait caused by several genetic factors.

Chromosomes, Human, Pair 11↗

PubMed2004

Affected-sib-pair test for linkage based on constraints for identical-by-descent distributions corresponding to disease models with imprinting.

Holmans' possible triangle test for affected sib pairs has proven to be a powerful tool for linkage analysis. This test is a likelihood-ratio test for which maximization is restricted to the set of possible sharing probabilities. Here, we extend the possible triangle test to take into account genomic imprinting, which is also known as parent-of-origin effect. While the classical test without imprinting looks at whether affected sib pairs share 0, 1, or 2 alleles identical-by-descent, the likelihood-ratio test allowing for imprinting further distinguishes whether the sharing of exactly one allele is through the father or mother. Thus, if the disease gene is indeed subject to imprinting, the extended test presented here can take into account that affecteds will have inherited the mutant allele preferentially from one particular parent. We calculate the sharing probabilities at a marker locus linked to a disease susceptibility locus. Using our formulation, the constraints on these probabilities given by Dudoit and Speed ([1999] Statistics in Genetics; New York: Springer) can easily be verified. Next, we derive the asymptotic distribution of the restricted likelihood-ratio test statistic under the null hypothesis of no linkage, and give LOD-score criteria for various test sizes. We show, for various disease models, that the test allowing for imprinting has significantly higher power to detect linkage if imprinting is indeed present, at the cost of only a small reduction in power in case of no imprinting. Altogether, unlike many methods currently available, our novel model-free sib-pair test adequately models the epigenetic parent-of-origin effect, and will hopefully prove to be a useful tool for the genetic mapping of complex traits.

Alleles↗

PubMed2004

Multilocus haplotype analyses reveal association between 5 novel IL-15 polymorphisms and asthma.

BACKGROUND: IL-15 is a T(H)1-related cytokine that is involved in the inflammatory response in various infectious and autoimmune diseases. IL-15 has recently been shown to be upregulated in T-cell-mediated inflammatory disorders. The observations suggest a potential role for this cytokine in a variety of pathologic conditions, including T(H)1-mediated and T(H)2-mediated inflammatory diseases. OBJECTIVE: In this study, we searched for single nucleotide polymorphisms in the whole IL-15 gene and investigated their association with inflammatory and/or atopic phenotypes. METHODS: The screening for single nucleotide polymorphisms was performed by single-strand conformation polymorphism analysis. Genotyping of the identified polymorphisms was performed by restriction fragment length polymorphism. Genotypic association analysis used the Armitage trend test. Haplotype frequency estimation and subsequent testing for differences between cases and controls were performed by using the programs FASTEHPLUS and FAMHAP. RESULTS: We identified 5 novel noncoding nucleotide sequence variants, all of which were typed in our asthmatic, our atopic, and our control population. According to the Armitage trend test, none of the 5 polymorphisms is associated with the phenotype bronchial asthma or atopy. However, multilocus haplotype analysis based on simulations to find out whether the haplotype frequencies differed between cases and controls by using the program FAMHAP yielded a P value of 6.1 x 10(-5) in the asthmatic versus the control population, which is highly significant. Furthermore, we obtained a nominally significant result of P=.0232 for the atopic versus the control population by using FAMHAP. CONCLUSION: These results strongly underscore previous findings that suggest a potential role of this cytokine in allergic diseases.

Adolescent↗

PubMed2004

Efficient two-trait-locus linkage analysis through program optimization and parallelization: application to hypercholesterolemia.

We have optimized and parallelized the GENEHUNTER-TWOLOCUS program that allows to perform linkage analysis with two trait loci in the multimarker context. The optimization of the serial program, before parallelization, results in a speedup of a factor of more than 10. The parallelization affects the two-locus-score calculation, which is predominant in terms of computation time. We obtain perfect speedup, that is, the computation time decreases exactly by a factor of the number of processors. In addition, two-locus LOD and NPL scores are now calculated for varying genetic positions of both disease loci, not just one locus varied and the position of the other disease locus fixed, as before. This results in easily interpretable 3-D plots. We have reanalyzed a pedigree with hypercholesterolemia using our new version of GENEHUNTER-TWOLOCUS. Whereas originally, two individuals had to be discarded due to excessive computation-time demands, the entire 17-bit pedigree could now be analyzed as a whole. We obtain a two-trait-locus LOD score of 5.49 under a multiplicative model, compared to LOD scores of 3.08 and 2.87 under a heterogeneity and additive model, respectively. This further increases evidence for linkage to both 1p36.1-p35 and 13q22-q32 regions, and corroborates the hypothesis that the two genes act in a multiplicative way on LDL cholesterol level. Furthermore, we compare the computation times for two-trait-locus analysis needed by the programs GENEHUNTER-TWOLOCUS, TLINKAGE, and SUPERLINK. Altogether, our algorithmic improvements of GENEHUNTER-TWOLOCUS allow researchers to analyze complex diseases under realistic two-trait-locus models with pedigrees of reasonable size and using many markers.

Computer Simulation↗

PubMed2004

A recoding scheme for X-linked and pseudoautosomal loci to be used with computer programs for autosomal LOD-score analysis.

We present a recoding scheme that allows for a parametric multipoint X-chromosomal linkage analysis of dichotomous traits in the context of a computer program for autosomes that can use trait models with imprinting. Furthermore, with this scheme, it is possible to perform a joint multipoint analysis of X-linked and pseudoautosomal loci. It is required that (1) the marker genotypes of all female nonfounders are available and that (2) there are no male nonfounders who have daughters in the pedigree. The second requirement does not apply if the trait locus is pseudoautosomal. The X-linked marker loci are recorded by adding a dummy allele to the males' hemizygous genotypes. For modelling an X-linked trait locus, five different liability classes are defined, in conjunction with a paternal imprinting model for male nonfounders. The formulation aims at the mapping of a diallelic trait locus relative to an arbitrary number of codominant markers with known genetic distances, in cases where a program for a genuine X-chromosomal analysis is not available.

Chromosome Mapping↗

PubMed2003

Quantitative trait linkage analysis of longitudinal change in body weight.

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.

Adult↗

PubMed2003

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13.

The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.

Cardiovascular Diseases↗

PubMed2003

How to model a complex trait. 1. General considerations and suggestions.

Usually, when complex traits are at issue, not only are the loci of the responsible genes a priori unknown; the same also holds for the mode of inheritance of the trait, and sometimes even for the phenotype definition. The term mode of inheritance relates to both the genetic mechanism, i.e., the number of loci implicated in the etiology of the disease, and the genotype-phenotype relation, which describes the influence of these loci on the trait. Having an idea of the genetic model can crucially facilitate the mapping process. This holds especially in the context of linkage analysis, where an appropriate parametric model or a suitable nonparametric allele sharing statistic may accordingly be selected. Here, we review the difficulties with parametric and nonparametric linkage analysis when applied to multifactorial diseases. We address the question why it is necessary to adequately model a genetically complex trait in a linkage study, and elucidate the steps to do so. Furthermore, we discuss the value of including unaffected individuals into the analysis, as well as of looking at larger pedigrees, both with parametric and nonparametric methods. Our considerations and suggestions aim at guiding researchers to genotyping individuals at a trait locus as accurately as possible.

Chromosome Mapping↗

PubMed2003

How to model a complex trait. 2. Analysis with two disease loci.

Complex traits are often governed by more than one trait locus. The first step towards an adequate model for such diseases is a linkage analysis with two trait loci. Such an analysis can be expected to have higher power to detect linkage than a standard single-trait-locus linkage analysis. However, it is crucial to accurately specify the parameters of the two-locus model. Here, we recapitulate the general two-locus model with and without genomic imprinting. We relate heterogeneity, multiplicative, and additive two-locus models to biological or pathophysiological mechanisms, and give the corresponding averaged ("best-fitting") single-trait-locus models for each of the two loci. Furthermore, we derive the two-locus penetrances from the averaged single-locus models, under the assumption of one of the three model classes mentioned above. Using these formulae, if the best-fitting single-locus models are available, investigators may perform a two-trait-locus linkage analysis under a realistic model. This procedure will maximize the power to detect linkage for traits which are governed by two or more loci, and lead to more accurate estimates of the disease-locus positions.

Computer Simulation↗

PubMed2002

Mutation in the ARH gene and a chromosome 13q locus influence cholesterol levels in a new form of digenic-recessive familial hypercholesterolemia.

We studied a Syrian family with 3 children who had low-density lipoprotein cholesterol (LDL) concentrations of 13.3, 12.2, and 8.6 mmol/L, respectively. Three other siblings and the parents all had LDL values <4.52 mmol/L, suggesting an autosomal-recessive mode of inheritance. The extended pedigree had 66 additional persons with normal LDL values. A genome-wide scan in the core family with 427 markers showed support for linkage on both chromosomes 1 and 13. Markers on chromosome 1 revealed a 3.07 multipoint LOD score between 1p36.1-p35, an 18-cM interval. Surprisingly, we also found linkage to 13q22-q32, a 14-cM interval, with a 3.08 LOD score. We had identified this locus earlier as containing a gene strongly influencing LDL in another Arab family with autosomal-dominant familial hypercholesterolemia and in normal dizygotic twins. We found evidence for an interaction between these loci. We next genotyped our twin panel and confirmed linkage of the 1p36.1-p35 locus to LDL (P<0.002) in this normal population. Elucidation of ARH, the LDL receptor adaptor protein at chromosome 1p35, caused us to sequence that gene. We first identified the genomic structure of ARH gene and then sequenced the gene in our family. We found an intron 1 acceptor splice-site mutation. This mutation was not found in any other family members, in 31 nonrelated Syrian persons, or in 30 Germans. Our results underscore the importance of ARH on chromosome 1 and the chromosome 13q locus to LDL, not only in families with unusual illnesses, but also to the general population.

Base Sequence↗