Nous montrons l’utilisation de la puce exon d’Affymetrix pour l’analyse simultanée de l’expression des gènes et de la variation d’isoformes. Nous avons utilisé les échantillons d’ARN du cerveau et des tissus de référence qui ont été antérieurement utilisés dans l’étude du consortium MicroArray Quality Control (MAQC). Nous démontrons une forte concordance de la quantification de l’expression des gènes entre trois plateformes d’expression populaires à savoir la puce exon d’Affymetrix, la puce Illumina et la puce U133A d’Affymetrix. Plus intéressant nous montrons que la majorité des discordances entre les trois plateformes résulterait des positions différentes des sondes à travers les plateformes et que les variations d’isoforme exactes ne peuvent être identifiées que par la puce exon. Nous avons détecté avec succès, entre les tissus de référence et ceux du cerveau, une centaine de cas d’évènements d’épissage alternatif. La puce exon est requise dans l’analyse de l’épissage alternatif associé aux pathologies telles que les cancers et les troubles neurologiques. Comme application de cette technologie, nous avons analysé les variations d’épissage dans la métastase du cancer de sein développé dans le model de la souris. Nous avons utilisé une gamme bien définie de trois lignées de tumeur mammaire ayant différents potentiels métastatiques. Par des analyses statistiques, nous avons répertorié 2623 transcripts présentant des variations d’expression et d’isoformes entre les types de tumeur. Une analyse du réseau de gènes montre qu’environ la moitié d’entre eux est impliquée dans plusieurs activités cellulaires, ainsi que dans nombreux cancers et désordres génétiques. / We demonstrate how the Affymetrix Exon Array, can be used to simultaneously profile gene expression level, and detect variations at the isoform level. We use a well studied set of brain and reference RNA samples previously used by the MicroArray Quality Control (MAQC) consortium study. We demonstrate a high concordance of gene expression measurements among three popular expression platforms – Affymetrix Exon Array, Illumina, and Affymetrix 3’ targeted array (U133A). More interestingly, we show that in many cases of discordant results, the effect can be explained by differential probe placements across platforms, and that the exact isoform change can only be captured by the Exon Array. Finally, we are able to detect hundreds of cases of splicing, transcript initiation, and termination differences between the brain and reference tissue samples. We propose that the Exon Array is a highly effective tool for transcript isoform profiling, and that it should be used in a variety of systems where such changes are known to be associated with diseases, such as neurological disorders and cancer. As application, we used the Affymetrix Exon Array to identify metastatis-specific alternative splicing in mouse model of breast cancer at the whole genome level. We utilize a well characterized series of three mouse mammary tumor lines exhibiting varying levels of metastatic potential. We catalogued 2623 transcripts which exhibit splicing aberrations during the progression of cancer. A genetic pathway analysis shows the half of them implicated in several cell activities, cancers and genetic disorders.

Nous montrons l’utilisation de la puce exon d’Affymetrix pour l’analyse simultanée de l’expression des gènes et de la variation d’isoformes. Nous avons utilisé les échantillons d’ARN du cerveau et des tissus de référence qui ont été antérieurement utilisés dans l’étude du consortium MicroArray Quality Control (MAQC). Nous démontrons une forte concordance de la quantification de l’expression des gènes entre trois plateformes d’expression populaires à savoir la puce exon d’Affymetrix, la puce Illumina et la puce U133A d’Affymetrix. Plus intéressant nous montrons que la majorité des discordances entre les trois plateformes résulterait des positions différentes des sondes à travers les plateformes et que les variations d’isoforme exactes ne peuvent être identifiées que par la puce exon. Nous avons détecté avec succès, entre les tissus de référence et ceux du cerveau, une centaine de cas d’évènements d’épissage alternatif. La puce exon est requise dans l’analyse de l’épissage alternatif associé aux pathologies telles que les cancers et les troubles neurologiques. Comme application de cette technologie, nous avons analysé les variations d’épissage dans la métastase du cancer de sein développé dans le model de la souris. Nous avons utilisé une gamme bien définie de trois lignées de tumeur mammaire ayant différents potentiels métastatiques. Par des analyses statistiques, nous avons répertorié 2623 transcripts présentant des variations d’expression et d’isoformes entre les types de tumeur. Une analyse du réseau de gènes montre qu’environ la moitié d’entre eux est impliquée dans plusieurs activités cellulaires, ainsi que dans nombreux cancers et désordres génétiques. / We demonstrate how the Affymetrix Exon Array, can be used to simultaneously profile gene expression level, and detect variations at the isoform level. We use a well studied set of brain and reference RNA samples previously used by the MicroArray Quality Control (MAQC) consortium study. We demonstrate a high concordance of gene expression measurements among three popular expression platforms – Affymetrix Exon Array, Illumina, and Affymetrix 3’ targeted array (U133A). More interestingly, we show that in many cases of discordant results, the effect can be explained by differential probe placements across platforms, and that the exact isoform change can only be captured by the Exon Array. Finally, we are able to detect hundreds of cases of splicing, transcript initiation, and termination differences between the brain and reference tissue samples. We propose that the Exon Array is a highly effective tool for transcript isoform profiling, and that it should be used in a variety of systems where such changes are known to be associated with diseases, such as neurological disorders and cancer. As application, we used the Affymetrix Exon Array to identify metastatis-specific alternative splicing in mouse model of breast cancer at the whole genome level. We utilize a well characterized series of three mouse mammary tumor lines exhibiting varying levels of metastatic potential. We catalogued 2623 transcripts which exhibit splicing aberrations during the progression of cancer. A genetic pathway analysis shows the half of them implicated in several cell activities, cancers and genetic disorders.

Recent large-scale genome-wide studies for many human disorders have identified associations with numerous genetic variants. The biological interpretation of these variants presents a major challenge. In particular, the identification of biological pathways underlying the association could provide crucial insights into the disease aetiologies. In this thesis, I used functional genomics approaches to increase our understanding of neuropsychiatric and neurodevelopmental disorders. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them.