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Unlinked Regions (unlinked + regions)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

Haplotype interaction analysis of unlinked regions

GENETIC EPIDEMIOLOGY, Issue 4 2005
Tim Becker
Abstract Genetically complex diseases are caused by interacting environmental factors and genes. As a consequence, statistical methods that consider multiple unlinked genomic regions simultaneously are desirable. Such consideration, however, may lead to a vast number of different high-dimensional tests whose appropriate analysis pose a problem. Here, we present a method to analyze case-control studies with multiple SNP data without phase information that considers gene-gene interaction effects while correcting appropriately for multiple testing. In particular, we allow for interactions of haplotypes that belong to different unlinked regions, as haplotype analysis often proves to be more powerful than single marker analysis. In addition, we consider different marker combinations at each unlinked region. The multiple testing issue is settled via the minP approach; the P value of the "best" marker/region configuration is corrected via Monte-Carlo simulations. Thus, we do not explicitly test for a specific pre-defined interaction model, but test for the global hypothesis that none of the considered haplotype interactions shows association with the disease. We carry out a simulation study for case-control data that confirms the validity of our approach. When simulating two-locus disease models, our test proves to be more powerful than association methods that analyze each linked region separately. In addition, when one of the tested regions is not involved in the etiology of the disease, only a small amount of power is lost with interaction analysis as compared to analysis without interaction. We successfully applied our method to a real case-control data set with markers from two genes controlling a common pathway. While classical analysis failed to reach significance, we obtained a significant result even after correction for multiple testing with our proposed haplotype interaction analysis. The method described here has been implemented in FAMHAP. Genet. Epidemiol. 2005. © 2005 Wiley-Liss, Inc. [source]

Multipoint analysis using affected sib pairs: Incorporating linkage evidence from unlinked regions

GENETIC EPIDEMIOLOGY, Issue 2 2001
Kung-Yee Liang
Abstract In this paper, we proposed a multipoint method to assess evidence of linkage to one region by incorporating linkage evidence from another region. This approach uses affected sib pairs in which the number of alleles shared identical by descent (IBD) is the primary statistic. This generalized estimating equation (GEE) approach is robust in that no assumption about the mode of inheritance is required, other than assuming the two regions being considered are unlinked and that there is no more than one susceptibility gene in each region. The method proposed here uses data from all available families to simultaneously test the hypothesis of statistical interaction between regions and to estimate the location of the susceptibility gene in the target region. As an illustration, we have applied this GEE method to an asthma sib pair study (Wjst et al. [1999] Genomics 58:1,8), which earlier reported evidence of linkage to chromosome 6 but showed no evidence for chromosome 20. Our results yield strong evidence to chromosome 20 (P value = 0.0001) after incorporating linkage information from chromosome 6. Furthermore, it estimates with 95% certainty that the map location of the susceptibility gene is flanked by markers D20S186 and D20S101, which are approximately 16.3 cM apart. Genet. Epidemiol. 21:105,122, 2001. © 2001 Wiley-Liss, Inc. [source]

Functional analysis of aromatic biosynthetic pathways in Pseudomonas putida KT2440

MICROBIAL BIOTECHNOLOGY, Issue 1 2009
M. Antonia Molina-Henares
Summary Pseudomonas putida KT2440 is a non-pathogenic prototrophic bacterium with high potential for biotechnological applications. Despite all that is known about this strain, the biosynthesis of essential chemicals has not been fully analysed and auxotroph mutants are scarce. We carried out massive mini-Tn5 random mutagenesis and screened for auxotrophs that require aromatic amino acids. The biosynthesis of aromatic amino acids was analysed in detail including physical and transcriptional organization of genes, complementation assays and feeding experiments to establish pathway intermediates. There is a single pathway from chorismate leading to the biosynthesis of tryptophan, whereas the biosynthesis of phenylalanine and tyrosine is achieved through multiple convergent pathways. Genes for tryptophan biosynthesis are grouped in unlinked regions with the trpBA and trpGDE genes organized as operons and the trpI, trpE and trpF genes organized as single transcriptional units. The pheA and tyrA gene-encoding multifunctional enzymes for phenylalanine and tyrosine biosynthesis are linked in the chromosome and form an operon with the serC gene involved in serine biosynthesis. The last step in the biosynthesis of these two amino acids requires an amino transferase activity for which multiple tyrB -like genes are present in the host chromosome. [source]