The promoter polymorphism -232C/G of the PCK1 gene is associated with type 2 diabetes in a UK-resident South Asian population

Background - The PCK1 gene, encoding cytosolic phosphoenolpyruvate carboxykinase (PEPCKC), has previously been implicated as a candidate gene for type 2 diabetes (T2D) susceptibility. Rodent models demonstrate that over-expression of Pck1 can result in T2D development and a single nucleotide polymorphism (SNP) in the promoter region of human PCK1 (-232C/G) has exhibited significant association with the disease in several cohorts. Within the UK-resident South Asian population, T2D is 4 to 6 times more common than in indigenous white Caucasians. Despite this, few studies have reported on the genetic susceptibility to T2D in this ethnic group and none of these has investigated the possible effect of PCK1 variants. We therefore aimed to investigate the association between common variants of the PCK1 gene and T2D in a UK-resident South Asian population of Punjabi ancestry, originating predominantly from the Mirpur area of Azad Kashmir, Pakistan. Methods - We used TaqMan assays to genotype five tagSNPs covering the PCK1 gene, including the -232C/G variant, in 903 subjects with T2D and 471 normoglycaemic controls. Results - Of the variants studied, only the minor allele (G) of the -232C/G SNP demonstrated a significant association with T2D, displaying an OR of 1.21 (95% CI: 1.03 - 1.42, p = 0.019). Conclusion - This study is the first to investigate the association between variants of the PCK1 gene and T2D in South Asians. Our results suggest that the -232C/G promoter polymorphism confers susceptibility to T2D in this ethnic group.

Identification of candidate genes for dyslexia susceptibility on chromosome 18

Six independent studies have identified linkage to chromosome 18 for developmental dyslexia or general reading ability. Until now, no candidate genes have been identified to explain this linkage. Here, we set out to identify the gene(s) conferring susceptibility by a two stage strategy of linkage and association analysis. Methodology/Principal Findings: Linkage analysis: 264 UK families and 155 US families each containing at least one child diagnosed with dyslexia were genotyped with a dense set of microsatellite markers on chromosome 18. Association analysis: Using a discovery sample of 187 UK families, nearly 3000 SNPs were genotyped across the chromosome 18 dyslexia susceptibility candidate region. Following association analysis, the top ranking SNPs were then genotyped in the remaining samples. The linkage analysis revealed a broad signal that spans approximately 40 Mb from 18p11.2 to 18q12.2. Following the association analysis and subsequent replication attempts, we observed consistent association with the same SNPs in three genes; melanocortin 5 receptor (MC5R), dymeclin (DYM) and neural precursor cell expressed, developmentally down-regulated 4-like (NEDD4L). Conclusions: Along with already published biological evidence, MC5R, DYM and NEDD4L make attractive candidates for dyslexia susceptibility genes. However, further replication and functional studies are still required.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.