La formation qualifiante et transférable en milieu de travail

Comment définir une formation qualifiante et transférable? Comment favoriser et obtenir une telle formation en milieu de travail? Qui doit payer pour cette formation? Pourquoi certaines entreprises offrent une formation structurée à leurs employés contrairement à d’autres? Qui reçoit cette formation? Pourquoi plusieurs employés refusent la formation offerte par leur employeur? Pourquoi certaines économies ont senti le besoin de légiférer sur cette question en instaurant une obligation financière de formation et d’autres ne l’ont pas fait? Pour répondre à ces questions et à plusieurs autres questions connexes, nous avons répertorié et analysé plus de 140 travaux provenant essentiellement des écrits des économistes sur le sujet. Notre étude rapporte plusieurs éléments de réponse à ces questions et discute plusieurs autres dimensions connexes aux déterminants de la formation en entreprise (impacts de la formation, incitatifs et barrières à la formation, les politiques publiques en matière de formation). Il est possible cependant que certaines réponses demeurent insatisfaisantes aux yeux de la Commission des Partenaires du marché du travail. C’est notamment ce que nous appréhendons au sujet de la définition d’une formation qualifiante et transférable. Chez les économistes, ces termes ne sont pas comme tels retenus. Ce sont les concepts de formation générale et de formation spécifique qui sont discutés amplement dans la littérature économique. Par formation générale, on entend chez les économistes une formation qui a de la valeur autant au sein de l’entreprise qui choisit de l’offrir à ses employés qu’au sein d’entreprises extérieures. À cet égard, il est reconnu que ce type de formation est pleinement transférable. Par contraste, la formation spécifique n’est utile qu’au sein de l’entreprise qui choisit de l’offrir à ses employés. Ce type de formation n’est donc aucunement transférable. Récemment, on a proposé l’idée que toutes les compétences acquises en cours de formation seraient de nature générale, mais que la combinaison de ces compétences générales serait de nature spécifique à l’entreprise. En ce qui concerne la formation qualifiante, elle peut être définie comme un processus par lequel l’individu développe les compétences nécessaires à l’exercice d’une fonction ou d’un métier sur le marché du travail. Or, ce n’est qu’ex post que la formation est qualifiante, c’est-à-dire lorsqu’elle se traduit par une hausse de productivité, une hausse de salaire, ou une hausse de mobilité professionnelle. Par ailleurs, d’autres éléments ne sont pas de nature à simplifier la recherche d’un cadre opérationnel aux termes de la Loi portant sur la question de la formation « qualifiante » et « transférable ». Par exemple, une formation non qualifiante peut le devenir par l’innovation et il est aussi possible que dans certains cas un délai dans le temps soit nécessaire pour qu’une formation devienne qualifiante. Si plusieurs réponses demeurent incomplètes, il existe néanmoins un certain agrément sur plusieurs points et cela est grandement susceptible d’éclairer le débat entourant la promotion de la formation en entreprise. D’abord, la portée du sujet est indéniable : la question de la formation des travailleurs en entreprise est fondamentale dans un contexte de concurrence mondiale accrue. Cette situation est particulièrement importante pour le Québec avec son économie largement ouverte sur les marchés extérieurs. La formation en milieu de travail est un investissement en capital humain qui est un facteur majeur de la croissance économique et une condition garante d’une meilleure qualité de vie pour les individus. Il existe aussi un consensus à l’effet que la formation en entreprise soit d’abord et avant tout le privilège des travailleurs les plus habiles et les mieux éduqués à la base. Un autre constat bien évident est que plusieurs conditions semblent nécessaires pour favoriser la formation en milieu de travail, que ce soit, par exemple, l’abolition des contraintes institutionnelles ou la simple amélioration de la reconnaissance des acquis, alors qu’aucune n’est en soi suffisante. La Loi favorisant le développement de la formation de la main-d’œuvre, qui oblige toutes les entreprises d’une certaine taille à investir 1% de leur masse salariale dans la formation de la main-d’œuvre n’apparaît pas être une condition suffisante. S’il en était autrement, elle serait universelle et non l’exception dans les économies. Que cette loi soit une condition nécessaire au Québec est une autre question qui nous amène à nous interroger, comme nous l’avons fait dans ce travail, sur les déterminants fondamentaux de la formation en milieu de travail. À la base de ces déterminants, on retrouve la nécessité que la formation soit un investissement rentable autant pour l’individu que pour la firme qui l’embauche. Notre étude se termine en offrant quelques recommandations pour favoriser la formation en entreprise tout en suggérant diverses pistes pour des recherches futures.

Gene expression profiling by DNA microarray analysis in mouse embryonic fibroblasts transformed by ras(V12 )mutated protein and the E1A oncogene

BACKGROUND: Ras is an area of intensive biochemical and genetic studies and characterizing downstream components that relay ras-induced signals is clearly important. We used a systematic approach, based on DNA microarray technology to establish a first catalog of genes whose expression is altered by ras and, as such, potentially involved in the regulation of cell growth and transformation. RESULTS: We used DNA microarrays to analyze gene expression profiles of ras(V12)/E1A-transformed mouse embryonic fibroblasts. Among the ~12,000 genes and ESTs analyzed, 815 showed altered expression in ras(V12)/E1A-transformed fibroblasts, compared to control fibroblasts, of which 203 corresponded to ESTs. Among known genes, 202 were up-regulated and 410 were down-regulated. About one half of genes encoding transcription factors, signaling proteins, membrane proteins, channels or apoptosis-related proteins was up-regulated whereas the other half was down-regulated. Interestingly, most of the genes encoding structural proteins, secretory proteins, receptors, extracellular matrix components, and cytosolic proteins were down-regulated whereas genes encoding DNA-associated proteins (involved in DNA replication and reparation) and cell growth-related proteins were up-regulated. These data may explain, at least in part, the behavior of transformed cells in that down-regulation of structural proteins, extracellular matrix components, secretory proteins and receptors is consistent with reversion of the phenotype of transformed cells towards a less differentiated phenotype, and up-regulation of cell growth-related proteins and DNA-associated proteins is consistent with their accelerated growth. Yet, we also found very unexpected results. For example, proteases and inhibitors of proteases as well as all 8 angiogenic factors present on the array were down-regulated in transformed fibroblasts although they are generally up-regulated in cancers. This observation suggests that, in human cancers, proteases, protease inhibitors and angiogenic factors could be regulated through a mechanism disconnected from ras activation. CONCLUSIONS: This study established a first catalog of genes whose expression is altered upon fibroblast transformation by ras(V12)/E1A. This catalog is representative of the genome but not exhaustive, because only one third of expressed genes was examined. In addition, contribution to ras signaling of post-transcriptional and post-translational modifications was not addressed. Yet, the information gathered should be quite useful to future investigations on the molecular mechanisms of oncogenic transformation

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P \u3c .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P \u3c .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

Abstract Background Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. Methods Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer–related cell types. Results We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P &amp;lt; .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P &amp;lt; .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. Conclusions CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention. </jats:sec