A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data.

The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs

An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform

The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina HumanMethylation450 BeadChip is widely used to quantify DNA methylation; nevertheless, the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate data sets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished data sets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that: (1) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; (2) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); (3) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive data set suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450K arrays

Interaction between PNPLA3 I148M variant and age at infection in determining fibrosis progression in chronic hepatitis C

BACKGROUND AND AIMS: The PNPLA3 I148M sequence variant favors hepatic lipid accumulation and confers susceptibility to hepatic fibrosis and hepatocellular carcinoma. The aim of this study was to estimate the effect size of homozygosity for the PNPLA3 I148M variant (148M/M) on the fibrosis progression rate (FPR) and the interaction with age at infection in chronic hepatitis C (CHC). METHODS: FPR was estimated in a prospective cohort of 247 CHC patients without alcohol intake and diabetes, with careful estimation of age at infection and determination of fibrosis stage by Ishak score. RESULTS: Older age at infection was the strongest determinant of FPR (p<0.0001). PNPLA3 148M/M was associated with faster FPR in individuals infected at older age (above the median, 21 years; -0.64\ub10.2, n\u200a=\u200a8 vs. -0.95\ub10.3, n\u200a=\u200a166 log10 FPR respectively; p\u200a=\u200a0.001; confirmed for lower age thresholds, p<0.05), but not in those infected at younger age (p\u200a=\u200ans). The negative impact of PNPLA3 148M/M on fibrosis progression was more marked in subjects at risk of altered hepatic lipid metabolism (those with grade 2-3 steatosis, genotype 3, and overweight; p<0.05). At multivariate analysis, PNPLA3 148M/M was associated with FPR (incremental effect 0.08\ub10.03 log10 fibrosis unit per year; p\u200a=\u200a0.022), independently of several confounders, and there was a significant interaction between 148M/M and older age at infection (p\u200a=\u200a0.025). The association between 148M/M and FPR remained significant even after adjustment for steatosis severity (p\u200a=\u200a0.032). CONCLUSIONS: We observed an interaction between homozygosity for the PNPLA3 I148M variant and age at infection in determining fibrosis progression in CHC patients

IL28B genotype is associated with cirrhosis or transition to cirrhosis in treatment-naive patients with chronic HCV genotype 1 infection: the international observational Gen-C study

Background and purpose: Contradictory data exist on the association between host interleukin-28B (IL28B) rs12979860 genotype and liver fibrosis in patients with chronic hepatitis C (CHC). This large, international, observational study (NCT01675427/MV25600) investigated relationships between IL28B rs12979860 genotype and liver fibrosis stage in CHC patients. Methods: A total of 3003 adult, treatment-naive CHC patients were enrolled into the study. Patients made one study visit to provide a blood sample for genotyping; other data were obtained from medical records. Results: 2916 patients comprised the analysis population; the majority were enrolled in Europe (n = 2119), were Caucasian (n = 2582) and had hepatitis C virus (HCV) genotype (G) 1 infection (n = 1702) (G2 = 323, G3 = 574, G4 = 260). Distribution of IL28B genotypes varied according to region of enrolment, patient ethnicity and HCV genotype. A significant association was observed between increasing number of IL28B T alleles and the prevalence of cirrhosis/transition to cirrhosis (based on biopsy or non-invasive assessments) in G1-infected patients (CC = 22.2% [111/499], CT = 27.5% [255/928], TT = 32.3% [87/269]; p = 0.0018). The association was significant in the large subgroup of European Caucasian G1 patients (n = 1245) but not in the smaller Asian (n = 25), Latin American (n = 137) or Middle Eastern (n = 289) G1 subgroups. IL28B genotype was not associated with liver fibrosis stage in patients with HCV G2, G3 or G4 infection. Conclusion: This large, international study found that IL28B rs12979860 genotype is significantly associated with liver fibrosis stage in CHC patients with HCV G1 infection. This association was evident in European Caucasians but not in G1-infected patients from Asia, Latin America or the Middle EastF. Hoffmann-La Roche Ltd, Basel, Switzerlan

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe