The genetic algorithm applied to haplotype data at the LDL receptor locus

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

A nonsynonymous polymorphism of IRAK4 associated with increased prevalence of gram-positive infection and decreased response to toll-like receptor ligands

Mutations in IRAK4 have been associated with recurrent Gram-positive infections in children. Given the central role of IRAK4 in innate immunity signaling, we hypothesized that common genetic variants of IRAK4 may be associated with prevalence of Gram-positive infection in critically ill adults. Haplotype clade tag single nucleotide polymorphisms (SNPs) of the IRAK4 gene were selected and genotyped in a cohort of 1,029 critically ill patients with systemic inflammatory response syndrome (SIRS). We found that a haplotype clade tagged by the A allele of the htSNP G29429A (Ala428Thr) was associated with increased relative risk of Gram-positive infection at admission to ICU (RR = 1.2, p < 0.05). Furthermore, the 29429A allele was associated with decreased lymphoblastoid cell response to CpG (as measured by IL-6 production) (raw values ± 95% CI 40.3 ± 32.3 vs. 85.8 ± 29.4 pg/ml; log-transformed values ± 95% CI 1.13 ± 0.37 vs. 1.55 ± 0.18, p < 0.04). We also found that IRAK4-deficient fibroblasts transfected with an IRAK4 expression plasmid containing the 29429A allele produced less IL-6 in response to lipopolysaccharide (p = 0.07). Our data suggest that the IRAK4 haplotype clade marked by 29429A (428Thr) alters susceptibility to Gram-positive bacteria, by decreasing cellular response to TLR ligands.

Single-nucleotide polymorphism detection using nanomolar nucleotides and single-molecule fluorescence

We have exploited three methods for discriminating single-nucleotide polymorphisms (SNPs) by detecting the incorporation or otherwise of labeled dideoxy nucleotides at the end of a primer chain using single-molecule fluorescence detection methods. Good discrimination of incorporated vs free nucleotide may be obtained in a homogeneous assay (without washing steps) via confocal fluorescence correlation spectroscopy or by polarization anisotropy obtained from confocal fluorescence intensity distribution analysis. Moreover, the ratio of the fluorescence intensities on each polarization channel may be used directly to discriminate the nucleotides incorporated. Each measurement took just a few seconds and was done in microliter volumes with nanomolar concentrations of labeled nucleotides. Since the confocal volumes interrogated are approximately 1fL and the reaction volume could easily be lowered to nanoliters, the possibility of SNP analysis with attomoles of reagents opens up a route to very rapid and inexpensive SNP detection. The method was applied with success to the detections of SNPs that are known to occur in the BRCA1 and CFTR genes.