Identification of biological pathways specific to phases preceding rheumatoid arthritis development through gene expression profiling.

The etiopathogenesis of rheumatoid arthritis is partially understood; however, it is believed to result from a multi-step process. The immune onset followed by pre-clinical phases will eventually lead to the development of symptomatic disease. We aim at identifying differentially expressed genes in order to highlight pathways involved in the pre-clinical stages of rheumatoid arthritis development. The study population consisted of first-degree relatives of patients with rheumatoid arthritis, known to have an increased risk of developing disease as compared to the general population. Whole transcriptome analysis was performed in four groups: asymptomatic without autoantibodies or symptoms associated with possible rheumatoid arthritis (controls); having either clinically suspect arthralgias, undifferentiated arthritis or autoimmunity associated with RA (pre-clinical stages of RA: Pcs-RA); having subsequently developed classifiable RA (pre-RA); and early untreated rheumatoid arthritis patients (RA). Differentially expressed genes were determined, and enrichment analysis was performed. Functional enrichment analysis revealed 31 pathways significantly enriched in differentially expressed genes for Pcs-RA, pre-RA and RA compared to the controls. Osteoclast pathway is among the seven pathways specific for RA. In Pcs-RA and in pre-RA, several enriched pathways include TP53 gene connections, such as P53 and Wnt signalling pathways. Analysis of whole transcriptome for phenotypes related to rheumatoid arthritis allows highlighting which pathways are requested in the pre-clinical stages of disease development. After validation in replication studies, molecules belonging to some of these pathways could be used to identify new specific biomarkers for individuals with impending rheumatoid arthritis.

Eigen-Epistasis for detecting gene-gene interactions.

BACKGROUND: A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects. RESULTS: In this paper we propose an original approach for detecting epistasis at the gene level, without systematically filtering on significant genes. We first compute interaction variables for each gene pair by finding its Eigen-Epistasis component, defined as the linear combination of Gene SNPs having the highest correlation with the phenotype. The selection of significant effects is done using a penalized regression method based on Group Lasso controlling the False Discovery Rate. CONCLUSION: The method is tested against two recent alternative proposals from the literature using synthetic data, and shows good performances in different settings. We demonstrate the power of our approach by detecting new gene-gene interactions on three genome-wide association studies.

Patterns of chromosomal copy-number alterations in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinomas (ICC) are relatively rare malignant tumors associated with a poor prognosis. Recent studies using genome-wide sequencing technologies have mainly focused on identifying new driver mutations. There is nevertheless a need to investigate the spectrum of copy number aberrations in order to identify potential target genes in the altered chromosomal regions. The aim of this study was to characterize the patterns of chromosomal copy-number alterations (CNAs) in ICC. METHODS: 53 patients having ICC with frozen material were selected. In 47 cases, DNA hybridization has been performed on a genomewide SNP array. A procedure with a segmentation step and a calling step classified genomic regions into copy-number aberration states. We identified the exclusively amplified and deleted recurrent genomic areas. These areas are those showing the highest estimated propensity level for copy loss (resp. copy gain) together with the lowest level for copy gain (resp. copy loss). We investigated ICC clustering. We analyzed the relationships between CNAs and clinico-pathological characteristics. RESULTS: The overall genomic profile of ICC showed many alterations with higher rates for the deletions. Exclusively deleted genomic areas were 1p, 3p and 14q. The main exclusively amplified genomic areas were 1q, 7p, 7q and 8q. Based on the exclusively deleted/amplified genomic areas, a clustering analysis identified three tumors groups: the first group characterized by copy loss of 1p and copy gain of 7p, the second group characterized by 1p and 3p copy losses without 7p copy gain, the last group characterized mainly by very few CNAs. From univariate analyses, the number of tumors, the size of the largest tumor and the stage were significantly associated with shorter time recurrence. We found no relationship between the number of altered cytobands or tumor groups and time to recurrence. CONCLUSION: This study describes the spectrum of chromosomal aberrations across the whole genome. Some of the recurrent exclusive CNAs harbor candidate target genes. Despite the absence of correlation between CNAs and clinico-pathological characteristics, the co-occurence of 7p gain and 1p loss in a subgroup of patients may suggest a differential activation of EGFR and its downstream pathways, which may have a potential effect on targeted therapies.

Weighted multiple testing procedures in genome-wide association studies.

Multiple testing procedures controlling the false discovery rate (FDR) are increasingly used in the context of genome wide association studies (GWAS), and weighted multiple testing procedures that incorporate covariate information are efficient to improve the power to detect associations. In this work, we evaluate some recent weighted multiple testing procedures in the specific context of GWAS through a simulation study. We also present a new efficient procedure called wBHa that prioritizes the detection of genetic variants with low minor allele frequencies while maximizing the overall detection power. The results indicate good performance of our procedure compared to other weighted multiple testing procedures. In particular, in all simulated settings, wBHa tends to outperform other procedures in detecting rare variants while maintaining good overall power. The use of the different procedures is illustrated with a real dataset.

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping.

Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability requiring explanation, and this missing heritability may be due to the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials might potentially have a role in guiding the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior genetic analysis and 2) the concomitant clustering of clinical and genetic variables. Both of these strategies have limitations that we propose to address. Contribution: This work proposes an original method for disease subtyping on the basis of both longitudinal clinical variables and high-dimensional genetic markers via a sparse mixture-of-regressions model. The added value of our approach lies in its interpretability in relation to two aspects. First, our model links both clinical and genetic data with regard to their initial nature (i.e., without transformation) and does not require post-processing where the original information is accessed a second time to interpret the subtypes. Second, it can address large-scale problems because of a variable selection step that is used to discard genetic variables that may not be relevant for subtyping. Results: The proposed method was validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease and genetic variants that potentially have a role in this typology were identified. Software availability: The R code for the proposed method, named DiSuGen, and a tutorial are available for download (see the references).

Genomic aberrations associated with outcome in anaplastic oligodendroglial tumors treated within the EORTC phase III trial 26951.

Despite similar morphological aspects, anaplastic oligodendroglial tumors (AOTs) form a heterogeneous clinical subgroup of gliomas. The chromosome arms 1p/19q codeletion has been shown to be a relevant biomarker in AOTs and to be perfectly exclusive from EGFR amplification in gliomas. To identify new genomic regions associated with prognosis, 60 AOTs from the EORTC trial 26951 were analyzed retrospectively using BAC-array-based comparative genomic hybridization. The data were processed using a binary tree method. Thirty-three BACs with prognostic value were identified distinguishing four genomic subgroups of AOTs with different prognosis (p < 0.0001). Type I tumors (25%) were characterized by: (1) an EGFR amplification, (2) a poor prognosis, (3) a higher rate of necrosis, and (4) an older age of patients. Type II tumors (21.7%) had: (1) loss of prognostic BACs located on 1p tightly associated with 19q deletion, (2) a longer survival, (3) an oligodendroglioma phenotype, and (4) a frontal location in brain. Type III AOTs (11.7%) exhibited: (1) a deletion of prognostic BACs located on 21q, and (2) a short survival. Finally, type IV tumors (41.7%) had different genomic patterns and prognosis than type I, II and III AOTs. Multivariate analysis showed that genomic type provides additional prognostic data to clinical, imaging and pathological features. Similar results were obtained in the cohort of 45 centrally reviewed-validated cases of AOTs. Whole genome analysis appears useful to screen the numerous genomic abnormalities observed in AOTs and to propose new biomarkers particularly in the non-1p/19q codeleted AOTs.

Detection of chromosomal abnormalities using high resolution arrays in clinical cancer research.

In clinical cancer research, high throughput genomic technologies are increasingly used to identify copy number aberrations. However, the admixture of tumor and stromal cells and the inherent karyotypic heterogeneity of most of the solid tumor samples make this task highly challenging. Here, we propose a robust two-step strategy to detect copy number aberrations in such a context. A spatial mixture model is first used to fit the preprocessed data. Then, a calling algorithm is applied to classify the genomic segments in three biologically meaningful states (copy loss, copy gain and modal copy). The results of a simulation study show the good properties of the proposed procedure with complex patterns of genomic aberrations. The interest of the proposed procedure in clinical cancer research is then illustrated by the analysis of real lung adenocarcinoma samples.

Procalcitonin in Preterm Neonates: A Different Threshold and Prolonged Interpretation.

Objectives: To evaluate the positive threshold of PCT for neonates of <32 weeks of gestation for the diagnosis of early-onset sepsis and to determine if the level of PCT collected within 6 h of life could be used. Design: Retrospective and bicentric study from May 2016 to April 2018. Setting: Two groups were established, neonates evaluated for PCT at birth (CordPCT) and within 6 h of life (delPCT). Patients: Two hundred and sixty neonates of <32 weeks of gestation born in Nice and South Paris (Bicêtre) University Hospitals, had been evaluated for PCT level. Main Outcomes Measures: The value of the PCT positive threshold was determined for the total population and each groups thanks ROC curves. Results: The threshold level of PCT for the total population was 0.98 ng/mL. The threshold value of cordPCT group was 1.00 vs. 0.98 ng/mL for delPCT group. The area under the Receiver Operating Characteristics curve for PCT sampled in delPCT group was significantly higher than in cordPCT group (0.94 compared to 0.75). Conclusions: The threshold level of PCT was higher in this cohort of neonates of <32 weeks of gestation compared to the value generally described for term neonates. The secondary sampling PCT level seems to be usable in screening algorithm for early-onset neonatal sepsis.

A weighted-Holm procedure accounting for allele frequencies in genomewide association studies.

In the context of genomewide association studies where hundreds of thousand of polymorphisms are tested, stringent thresholds on the raw association test P-values are generally used to limit false-positive results. Instead of using thresholds based on raw P-values as in Bonferroni and sequential Sidak (SidakSD) corrections, we propose here to use a weighted-Holm procedure with weights depending on allele frequency of the polymorphisms. This method is shown to substantially improve the power to detect associations, in particular by favoring the detection of rare variants with high genetic effects over more frequent ones with lower effects.

Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples.

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we systematically test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human biological reference materials, using standard (1 mg), low (100 ng and 10 ng) and ultra-low (<1 ng) input amounts, and for mRNA and total RNA, stranded and unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows decreased performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts <1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of an RNA-seq library preparation kit.

A constrained polynomial regression procedure for estimating the local False Discovery Rate.

BACKGROUND: In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR) for subsets of genes. RESULTS: In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. CONCLUSION: A novel and efficient procedure for estimating lFDR was developed and evaluated.

Genetics of VEGF serum variation in human isolated populations of cilento: importance of VEGF polymorphisms.

Vascular Endothelial Growth Factor (VEGF) is the main player in angiogenesis. Because of its crucial role in this process, the study of the genetic factors controlling VEGF variability may be of particular interest for many angiogenesis-associated diseases. Although some polymorphisms in the VEGF gene have been associated with a susceptibility to several disorders, no genome-wide search on VEGF serum levels has been reported so far. We carried out a genome-wide linkage analysis in three isolated populations and we detected a strong linkage between VEGF serum levels and the 6p21.1 VEGF region in all samples. A new locus on chromosome 3p26.3 significantly linked to VEGF serum levels was also detected in a combined population sample. A sequencing of the gene followed by an association study identified three common single nucleotide polymorphisms (SNPs) influencing VEGF serum levels in one population (Campora), two already reported in the literature (rs3025039, rs25648) and one new signal (rs3025020). A fourth SNP (rs41282644) was found to affect VEGF serum levels in another population (Cardile). All the identified SNPs contribute to the related population linkages (35% of the linkage explained in Campora and 15% in Cardile). Interestingly, none of the SNPs influencing VEGF serum levels in one population was found to be associated in the two other populations. These results allow us to exclude the hypothesis that the common variants located in the exons, intron-exon junctions, promoter and regulative regions of the VEGF gene may have a causal effect on the VEGF variation. The data support the alternative hypothesis of a multiple rare variant model, possibly consisting in distinct variants in different populations, influencing VEGF serum levels.

Combined genomic and phenotype screening reveals secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer.

Secretory factors that drive cancer progression are attractive immunotherapeutic targets. We used a whole-genome data-mining approach on multiple cohorts of breast tumours annotated for clinical outcomes to discover such factors. We identified Serine protease inhibitor Kazal-type 1 (SPINK1) to be associated with poor survival in estrogen receptor-positive (ER+) cases. Immunohistochemistry showed that SPINK1 was absent in normal breast, present in early and advanced tumours, and its expression correlated with poor survival in ER+ tumours. In ER- cases, the prognostic effect did not reach statistical significance. Forced expression and/or exposure to recombinant SPINK1 induced invasiveness without affecting cell proliferation. However, down-regulation of SPINK1 resulted in cell death. Further, SPINK1 overexpressing cells were resistant to drug-induced apoptosis due to reduced caspase-3 levels and high expression of Bcl2 and phospho-Bcl2 proteins. Intriguingly, these anti-apoptotic effects of SPINK1 were abrogated by mutations of its protease inhibition domain. Thus, SPINK1 affects multiple aggressive properties in breast cancer: survival, invasiveness and chemoresistance. Because SPINK1 effects are abrogated by neutralizing antibodies, we suggest that SPINK1 is a viable potential therapeutic target in breast cancer.

Genomic profiles specific to patient ethnicity in lung adenocarcinoma.

PURPOSE: East-Asian (EA) patients with non-small-cell lung cancer (NSCLC) are associated with a high proportion of nonsmoking women, epidermal growth factor receptor (EGFR)-activating somatic mutations, and clinical responses to tyrosine kinase inhibitors. We sought to identify novel molecular differences between NSCLCs from EA and Western European (WE) patients. EXPERIMENTAL DESIGN: A total of 226 lung adenocarcinoma samples from EA (n = 90) and WE (n = 136) patients were analyzed for copy number aberrations (CNA) by using a common high-resolution SNP (single nucleotide polymorphism) microarray platform. Univariate and multivariate analyses were carried out to identify CNAs specifically related to smoking history, EGFR mutation status, and ethnicity. RESULTS: The overall genomic profiles of adenocarcinomas from EA and WE patients were highly similar. Univariate analyses revealed several CNAs significantly associated with ethnicity, EGFR mutation, and smoking, but not to gender, and KRAS or p53 mutations. A multivariate model identified four ethnic-specific recurrent CNAs-significantly higher rates of copy number gain were observed on 16p13.13 and 16p13.11 in EA tumors, whereas higher rates of genomic loss on 19p13.3 and 19p13.11 were observed in tumors from WE patients. We identified several potential driver genes in these regions, showing a positive correlation between cis-localized copy number changes and transcriptomic changes. CONCLUSION: 16p copy number gains (EA) and 19p losses (WE) are ethnic-specific chromosomal aberrations in lung adenocarcinoma. Patient ethnicity should be considered when evaluating future NSCLC therapies targeting genes located on these areas.

Impaired performance of FDR-based strategies in whole-genome association studies when SNPs are excluded prior to the analysis.

With recent advances in genomewide microarray technologies, whole-genome association (WGA) studies have aimed at identifying susceptibility genes for complex human diseases using hundreds of thousands of single nucleotide polymorphisms (SNPs) genotyped at the same time. In this context and to take into account multiple testing, false discovery rate (FDR)-based strategies are now used frequently. However, a critical aspect of these strAtegies is that they are applied to a collection or a family of hypotheses and, thus, critically depend on these precise hypotheses. We investigated how modifying the family of hypotheses to be tested affected the performance of FDR-based procedures in WGA studies. We showed that FDR-based procedures performed more poorly when excluding SNPs with high prior probability of being associated. Results of simulation studies mimicking WGA studies according to three scenarios are reported, and show the extent to which SNPs elimination (family contraction) prior to the analysis impairs the performance of FDR-based procedures. To illustrate this situation, we used the data from a recent WGA study on type-1 diabetes (Clayton et al. [2005] Nat. Genet. 37:1243-1246) and report the results obtained when excluding or not SNPs located inside the human leukocyte antigen region. Based on our findings, excluding markers with high prior probability of being associated cannot be recommended for the analysis of WGA data with FDR-based strategies.

Identification and replication of a novel obesity locus on chromosome 1q24 in isolated populations of Cilento.

OBJECTIVE: Obesity is a complex trait with a variety of genetic susceptibility variants. Several loci linked to obesity and/or obesity-related traits have been identified, and relatively few regions have been replicated. Studying isolated populations can be a useful approach to identify rare variants that will not be detected with whole-genome association studies in large populations. RESEARCH DESIGN AND METHODS: Random individuals were sampled from Campora, an isolated village of the Cilento area in South Italy, phenotyped for BMI, and genotyped using a dense microsatellite marker map. An efficient pedigree-breaking strategy was applied to perform genome-wide linkage analyses of both BMI and obesity. Significance was assessed with ad hoc simulations for the two traits and with an original local false discovery rate approach to quantitative trait linkage analysis for BMI. A genealogy-corrected association test was performed for a single nucleotide polymorphism located in one of the linkage regions. A replication study was conducted in the neighboring village of Gioi. RESULTS: A new locus on chr1q24 significantly linked to BMI was identified in Campora. Linkage at the same locus is suggested with obesity. Three additional loci linked to BMI were also detected, including the locus including the INSIG2 gene region. No evidence of association between the rs7566605 variant and BMI or obesity was found. In Gioi, the linkage on chr1q24 was replicated with both BMI and obesity. CONCLUSIONS: Overall, our results confirm that successful linkage studies can be accomplished in these populations both to replicate known linkages and to identify novel quantitative trait linkages.

Distinct genetic loci control plasma HIV-RNA and cellular HIV-DNA levels in HIV-1 infection: the ANRS Genome Wide Association 01 study.

Previous studies of the HIV-1 disease have shown that HLA and Chemokine receptor genetic variants influence disease progression and early viral load. We performed a Genome Wide Association study in a cohort of 605 HIV-1-infected seroconverters for detection of novel genetic factors that influence plasma HIV-RNA and cellular HIV-DNA levels. Most of the SNPs strongly associated with HIV-RNA levels were localised in the 6p21 major histocompatibility complex (MHC) region and were in the vicinity of class I and III genes. Moreover, protective alleles for four disease-associated SNPs in the MHC locus (rs2395029, rs13199524, rs12198173 and rs3093662) were strikingly over-represented among forty-five Long Term HIV controllers. Furthermore, we show that the HIV-DNA levels (reflecting the HIV reservoir) are associated with the same four SNPs, but also with two additional SNPs on chromosome 17 (rs6503919; intergenic region flanked by the DDX40 and YPEL2 genes) and chromosome 8 (rs2575735; within the Syndecan 2 gene). Our data provide evidence that the MHC controls both HIV replication and HIV reservoir. They also indicate that two additional genomic loci may influence the HIV reservoir.

A mixture model approach to multiple testing for the genetic analysis of gene expression.

With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1.

Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15.

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

A simple procedure for estimating the false discovery rate.

MOTIVATION: The most used criterion in microarray data analysis is nowadays the false discovery rate (FDR). In the framework of estimating procedures based on the marginal distribution of the P-values without any assumption on gene expression changes, estimators of the FDR are necessarily conservatively biased. Indeed, only an upper bound estimate can be obtained for the key quantity pi0, which is the probability for a gene to be unmodified. In this paper, we propose a novel family of estimators for pi0 that allows the calculation of FDR. RESULTS: The very simple method for estimating pi0 called LBE (Location Based Estimator) is presented together with results on its variability. Simulation results indicate that the proposed estimator performs well in finite sample and has the best mean square error in most of the cases as compared with the procedures QVALUE, BUM and SPLOSH. The different procedures are then applied to real datasets. AVAILABILITY: The R function LBE is available at http://ifr69.vjf.inserm.fr/lbe CONTACT: broet@vjf.inserm.fr.