DNA expression microarrays may be the wrong tool to identify biological pathways

DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen’s k- score. The identification of specific biological pathways showed only moderate agreement (0.30 < k < 0.79) between the analysis methods used. Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used

On the necessity of different statistical treatment for Illumina BeadChip and Affymetrix GeneChip data and its significance for biological interpretation

<p>Abstract</p> <p>Background</p> <p>The original spotted array technology with competitive hybridization of two experimental samples and measuring relative expression levels is increasingly displaced by more accurate platforms that allow determining absolute expression values for a single sample (for example, Affymetrix GeneChip and Illumina BeadChip). Unfortunately, cross-platform comparisons show a disappointingly low concordance between lists of regulated genes between the latter two platforms.</p> <p>Results</p> <p>Whereas expression values determined with a single Affymetrix GeneChip represent single measurements, the expression results obtained with Illumina BeadChip are essentially statistical means from several dozens of identical probes. In the case of multiple technical replicates, the data require, therefore, different stistical treatment depending on the platform. The key is the computation of the squared standard deviation within replicates in the case of the Illumina data as weighted mean of the square of the standard deviations of the individual experiments. With an Illumina spike experiment, we demonstrate dramatically improved significance of spiked genes over all relevant concentration ranges. The re-evaluation of two published Illumina datasets (membrane type-1 matrix metalloproteinase expression in mammary epithelial cells by Golubkov et al. Cancer Research (2006) 66, 10460; spermatogenesis in normal and teratozoospermic men, Platts et al. Human Molecular Genetics (2007) 16, 763) significantly identified more biologically relevant genes as transcriptionally regulated targets and, thus, additional biological pathways involved.</p> <p>Conclusion</p> <p>The results in this work show that it is important to process Illumina BeadChip data in a modified statistical procedure and to compute the standard deviation in experiments with technical replicates from the standard errors of individual BeadChips. This change leads also to an improved concordance with Affymetrix GeneChip results as the spermatogenesis dataset re-evaluation demonstrates.</p> <p>Reviewers</p> <p>This article was reviewed by I. King Jordan, Mark J. Dunning and Shamil Sunyaev.</p

DNA expression microarrays may be the wrong tool to identify biological pathways

DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen's k- score. The identification of specific biological pathways showed only moderate agreement (0.30 < k < 0.79) between the analysis methods used. Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream

The Australian Corneal Graft Registry 2012 Report

The Australian Corneal Graft Registry (ACGR) opened in May 1985 and thus has now been in operation for over 26 years. However, the census dates for this report was 01/06/2010 for penetrating grafts and 12/10/2011 for lamellar grafts. Over the years, we have collected data on more than 23,000 corneal grafts. The majority of corneal grafts registered have been penetrating, but increasing numbers of lamellar grafts have also been registered over recent years, as patterns of surgical practice change. At registration, we seek information on the recipient, the donor, the eye bank practices and the operative procedure. Follow-up then occurs at approximately yearly intervals for an indefinite period, and ceases upon loss of the graft, or the death or loss-to-follow-up of the patient. At each round of follow-up, we request information on the graft and visual outcome, and upon relevant post-operative events and treatments. The data are entered into an Access database and checked for consistency. Descriptive, univariate and multivariate analyses are subsequently performed using SPSS and Stata software, and the report is eventually collated

Evaluation Results of an Innovative Pilot Program to Increase Access to Fresh Fruits and Vegetables in Cobb County, GA

Background: This abstract describes a public health practice initiative called the Farm Fresh Market (FFM) and presented pilot evaluation results. Methods: The FFM, developed by Cobb and Douglas Public Health, the McCleskey-East Cobb Family YMCA, and Cobb2020, sold low-cost fruits and vegetables to families living in the 30168 zip code of Austell, Georgia. The evaluation focused on documenting to what extent the FFM reached its intended population and increased perceived access to fresh fruits and vegetables among customers. A convenience sample of 100 returning FFM customers completed self-administered, written intercept surveys at the end of the 2014 market season. Results: The market served customers from a range of socioeconomic backgrounds. Most customers strongly agreed that the FFM made it easier (69%) and less expensive (79%) for them to buy fresh fruits and vegetables and easier for them (63%) and their families (64%) to eat a healthy diet. Most customers reported that they ate more vegetables (65%) and fruit (55%) as a result of shopping at the FFM and reported high levels of satisfaction with all aspects of the FFM. Conclusions: The results suggest that the FFM served customers from the local area and that the FFM may have increased perceived access to healthy food options among customers. Community-level interventions to increase access to healthy foods may play an important role in chronic disease prevention

Investigation of Genetic Variation Underlying Central Obesity amongst South Asians

South Asians are 1/4 of the world’s population and have increased susceptibility to central obesity and related cardiometabolic disease. Knowledge of genetic variants affecting risk of central obesity is largely based on genome-wide association studies of common SNPs in Europeans. To evaluate the contribution of DNA sequence variation to the higher levels of central obesity (defined as waist hip ratio adjusted for body mass index, WHR) among South Asians compared to Europeans we carried out: i) a genome-wide association analysis of \u3e6M genetic variants in 10,318 South Asians with focused analysis of population-specific SNPs; ii) an exome-wide association analysis of ~250K SNPs in protein-coding regions in 2,637 South Asians; iii) a comparison of risk allele frequencies and effect sizes of 48 known WHR SNPs in 12,240 South Asians compared to Europeans. In genome-wide analyses, we found no novel associations between common genetic variants and WHR in South Asians at P\u3c5x10-8; variants showing equivocal association with WHR (P\u3c1x10-5) did not replicate at P\u3c0.05 in an independent cohort of South Asians (N = 1,922) or in published, predominantly European meta-analysis data. In the targeted analyses of 122,391 population-specific SNPs we also found no associations with WHR in South Asians at P\u3c0.05 after multiple testing correction. Exome-wide analyses showed no new associations between genetic variants and WHR in South Asians, either individually at P\u3c1.5x10-6 or grouped by gene locus at P\u3c2.5x10−6. At known WHR loci, risk allele frequencies were not higher in South Asians compared to Europeans (P = 0.77), while effect sizes were unexpectedly smaller in South Asians than Europeans (P\u3c5.0x10-8). Our findings argue against an important contribution for population-specific or cosmopolitan genetic variants underlying the increased risk of central obesity in South Asians compared to Europeans

Impact of BMI and waist circumference on epigenome-wide DNA methylation and identification of epigenetic biomarkers in blood: an EWAS in multi-ethnic Asian individuals

Background The prevalence of obesity and its related chronic diseases have been increasing especially in Asian countries. Obesity-related genetic variants have been identified, but these explain little of the variation in BMI. Recent studies reported associations between DNA methylation and obesity, mostly in non-Asian populations. Methods We performed an epigenome-wide association study (EWAS) on general adiposity (body mass index, BMI) and abdominal adiposity (waist circumference, WC) in 409 multi-ethnic Asian individuals and replicated BMI and waist-associated DNA methylation CpGs identified in other populations. The cross-lagged panel model and Mendelian randomization were used to assess the temporal relationship between methylation and BMI. The temporal relationship between the identified CpGs and inflammation and metabolic markers was also examined. Results EWAS identified 116 DNA methylation CpGs independently associated with BMI and eight independently associated with WC at false discovery rate P-FDR < 0.05 in 409 Asian samples. We replicated 110 BMI-associated CpGs previously reported in Europeans and identified six novel BMI-associated CpGs and two novel WC-associated CpGs. We observed high consistency in association direction of effect compared to studies in other populations. Causal relationship analyses indicated that BMI was more likely to be the cause of DNA methylation alteration, rather than the consequence. The causal analyses using BMI-associated methylation risk score also suggested that higher levels of the inflammation marker IL-6 were likely the consequence of methylation change. Conclusion Our study provides evidence of an association between obesity and DNA methylation in multi-ethnic Asians and suggests that obesity can drive methylation change. The results also suggested possible causal influence that obesity-related methylation changes might have on inflammation and lipoprotein levels