Sources of variability and effect of experimental approach on expression profiling data interpretation

BACKGROUND: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). RESULTS: We studied muscle biopsies from 28 human subjects as well as murine myogenic cell cultures, muscle, and spleens. Human MuscleChip arrays (4,601 probe sets) and murine U74Av2 Affymetrix microarrays were used for expression profiling. RNAs were profiled both singly, and as mixed groups. Variables studied included tissue heterogeneity, cRNA probe production, patient diagnosis, and GeneChip hybridizations. We found that the greatest source of variability was often different regions of the same patient muscle biopsy, reflecting variation in cell type content even in a relatively homogeneous tissue such as muscle. Inter-patient variation was also very high (SNP noise). Experimental variation (RNA, cDNA, cRNA, or GeneChip) was minor. Pre-profile mixing of patient cRNA samples effectively normalized both intra- and inter-patient sources of variation, while retaining a high degree of specificity of the individual profiles (86% of statistically significant differences detected by absolute analysis; and 85% by a 4-pairwise comparison survival method). CONCLUSIONS: Using unsupervised cluster analysis and correlation coefficients of 92 RNA samples on 76 oligonucleotide microarrays, we found that experimental error was not a significant source of unwanted variability in expression profiling experiments. Major sources of variability were from use of small tissue biopsies, particularly in humans where there is substantial inter-patient variability (SNP noise)

Building a Coherent Data Pipeline in Microarray Data Analyses: Optimization of Signal/Noise Ratios Using an Interactive Visualization Tool and a Novel Noise Filtering Method (2003)

Motivation: Sources of uncontrolled noise strongly influence data analysis in microarray studies, yet signal/noise ratios are rarely considered in microarray data analyses. We hypothesized that different research projects would have different sources and levels of confounding noise, and built an interactive visual analysis tool to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Results: Five probe set algorithms were studied with and without statistical weighting of probe sets using Microarray Suite (MAS) 5.0 probe set detection p values. The signal/noise optimization method was tested in two large novel microarray datasets with different levels of confounding noise; a 105 sample U133A human muscle biopsy data set (11 groups) (extensive noise), and a 40 sample U74A inbred mouse lung data set (8 groups) (little noise). Success was measured using F-measure value of success of unsupervised clustering into appropriate biological groups (signal). We show that both probe set signal algorithm and probe set detection p-value weighting have a strong effect on signal/noise ratios, and that the different methods performed quite differently in the two data sets. Among the signal algorithms tested, dChip difference model with p-value weighting was the most consistent at maximizing the effect of the target biological variables on data interpretation of the two data sets. Availability: The Hierarchical Clustering Explorer 2.0 is [url=http://www.cs.umd.edu/hcil/hce/]available[/url] online and the improved version of the Hierarchical Clustering Explorer 2.0 with p-value weighting and Fmeasure is available upon request to the first author. Murine arrays (40 samples) are publicly available at the [url=http://microarray.cnmcresearch.org/pgadatatable.asp]PEPR resource.[/url] (Chen et al., 2004)

Interactive Color Mosaic and Dendogram Displays for Signal/Noise Optimization in Microarray Data Analysis (2003)

Data analysis and visualization is strongly influenced by noise and noise filters. There are multiple sources of oisein microarray data analysis, but signal/noise ratios are rarely optimized, or even considered. Here, we report a noise analysis of a novel 13 million oligonucleotide dataset - 25 human U133A (~500,000 features) profiles of patient muscle biposies. We use our recently described interactive visualization tool, the Hierarchical Clustering Explorer (HCE) to systemically address the effect of different noise filters on resolution of arrays into orrectbiological groups (unsupervised clustering into three patient groups of known diagnosis). We varied probe set interpretation methods (MAS 5.0, RMA), resent callfilters, and clustering linkage methods, and investigated the results in HCE. HCE interactive features enabled us to quickly see the impact of these three variables. Dendrogram displays showed the clustering results systematically, and color mosaic displays provided a visual support for the results. We show that each of these three variables has a strong effect on unsupervised clustering. For this dataset, the strength of the biological variable was maximized, and noise minimized, using MAS 5.0, 10% present call filter, and Average Group Linkage. We propose a general method of using interactive tools to identify the optimal signal/noise balance or the optimal combination of these three variables to maximize the effect of the desired biological variable on data interpretation

The Autoimmune Disorder Susceptibility Gene CLEC16A Restrains NK Cell Function in YTS NK Cell Line and Clec16a Knockout Mice

CLEC16A locus polymorphisms have been associated with several autoimmune diseases. We overexpressed CLEC16A in YTS natural killer (NK) cells and observed reduced NK cell cytotoxicity and IFN-γ release, delayed dendritic cell (DC) maturation, decreased conjugate formation, cell-surface receptor downregulation and increased autophagy. In contrast, siRNA mediated knockdown resulted in increased NK cell cytotoxicity, reversal of receptor expression and disrupted mitophagy. Subcellular localization studies demonstrated that CLEC16A is a cytosolic protein that associates with Vps16A, a subunit of class C Vps-HOPS complex, and modulates receptor expression via autophagy. Clec16a knockout (KO) in mice resulted in altered immune cell populations, increased splenic NK cell cytotoxicity, imbalance of dendritic cell subsets, altered receptor expression, upregulated cytokine and chemokine secretion. Taken together, our findings indicate that CLEC16A restrains secretory functions including cytokine release and cytotoxicity and that a delicate balance of CLEC16A is needed for NK cell function and homeostasis

Genome-wide association study for acute otitis media in children identifies FNDC1 as disease contributing gene

Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, Pmeta=2.15 × 10-09), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10-06), and further show it is an eQTL for FNDC1 (P=9.3 × 10-05). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM

Association of CLEC16A with human common variable immunodeficiency disorder and role in murine B cells

Common variable immunodeficiency disorder (CVID) is the most common symptomatic primary immunodeficiency in adults, characterized by B-cell abnormalities and inadequate antibody response. CVID patients have considerable autoimmune comorbidity and we therefore hypothesized that genetic susceptibility to CVID may overlap with autoimmune disorders. Here, in the largest genetic study performed in CVID to date, we compare 778 CVID cases with 10, 999 controls across 123, 127 single-nucleotide polymorphisms (SNPs) on the Immunochip. We identify the first non-HLA genome-wide significant risk locus at CLEC16A (rs17806056, P = 2.0 x 10(-9)) and confirm the previously reported human leukocyte antigen (HLA) associations on chromosome 6p21 (rs1049225, P = 4.8 x 10(-16)). Clec16a knockdown (KD) mice showed reduced number of B cells and elevated IgM levels compared with controls, suggesting that CLEC16A may be involved in immune regulatory pathways of relevance to CVID. In conclusion, the CLEC16A associations in CVID represent the first robust evidence of non-HLA associations in this immunodeficiency condition

A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases