selectBoost : a general algorithm to enhance the performance of variable selection methods

Motivation: With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting. Results: In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering

Multi-OMICS analyses unveil STAT1 as a potential modifier gene in mevalonate kinase deficiency

International audienceObjectives The objective of the present study was to explain why two siblings carrying both the same homozygous pathogenic mutation for the autoinflammatory disease hyper IgD syndrome, show opposite phenotypes, that is, the first being asymptomatic, the second presenting all classical characteristics of the disease.Methods Where single omics (mainly exome) analysis fails to identify culprit genes/mutations in human complex diseases, multiomics analyses may provide solutions, although this has been seldom used in a clinical setting. Here we combine exome, transcriptome and proteome analyses to decipher at a molecular level, the phenotypic differences between the two siblings.Results This multiomics approach led to the identification of a single gene—STAT1—which harboured a rare missense variant and showed a significant overexpression of both mRNA and protein in the symptomatic versus the asymptomatic sister. This variant was shown to be of gain of function nature, involved in an increased activation of the Janus kinase/signal transducer and activator of transcription signalling (JAK/STAT) pathway, known to play a critical role in inflammatory diseases and for which specific biotherapies presently exist. Pathway analyses based on information from differentially expressed transcripts and proteins confirmed the central role of STAT1 in the proposed regulatory network leading to an increased inflammatory phenotype in the symptomatic sibling.Conclusions This study demonstrates the power of a multiomics approach to uncover potential clinically actionable targets for a personalised therapy. In more general terms, we provide a proteogenomics analysis pipeline that takes advantage of subject-specific genomic and transcriptomic information to improve protein identification and hence advance individualised medicine.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Compatibility at amino acid position 98 of MICB reduces the incidence of graft-versus host disease in conjunction with the CMV status

International audienceGraft-versus-host disease (GVHD) and cytomegalovirus (CMV)-related complications are leading causes of mortality after unrelated-donor hematopoietic cell transplantation (UD-HCT). The non-conventional MHC class I gene MICB, alike MICA, encodes a stress-induced polymorphic NKG2D ligand. However, unlike MICA, MICB interacts with the CMV-encoded UL16, which sequestrates MICB intracellularly, leading to immune evasion. Here, we retrospectively analyzed the impact of mismatches in MICB amino acid position 98 (MICB98), a key polymorphic residue involved in UL16 binding, in 943 UD-HCT pairs who were allele-matched at HLA-A, -B, -C, -DRB1, -DQB1 and MICA loci. HLA-DP typing was further available. MICB98 mismatches were significantly associated with an increased incidence of acute (grade II-IV: HR, 1.20; 95% CI, 1.15 to 1.24; P < 0.001; grade III-IV: HR, 2.28; 95% CI, 1.56 to 3.34; P < 0.001) and chronic GVHD (HR, 1.21; 95% CI, 1.10 to 1.33; P < 0.001). MICB98 matching significantly reduced the effect of CMV status on overall mortality from a hazard ratio of 1.77 to 1.16. MICB98 mismatches showed a GVHD-independent association with a higher incidence of CMV infection/reactivation (HR, 1.84; 95% CI, 1.34 to 2.51; P < 0.001). Hence selecting a MICB98-matched donor significantly reduces the GVHD incidence and lowers the impact of CMV status on overall survival