Yuna Blum

Publications (3)6.82 Total impact

• Source

  Available from: PubMed Central

  Article: Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken.

  Yuna Blum, Guillaume Le Mignon, David Causeur, Olivier Filangi, Colette Désert, Olivier Demeure, Pascale Le Roy, Sandrine Lagarrigue
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  ABSTRACT: Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM. Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region. Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.
  BMC Genomics 11/2011; 12:567. · 4.07 Impact Factor
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  Available from: PubMed Central

  Article: A factor model to analyze heterogeneity in gene expression.

  Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigue, David Causeur
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  ABSTRACT: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes. The use of factors to characterize simple patterns of heterogeneity is first demonstrated on illustrative gene expression data sets. An expression data set primarily generated to map QTL for fatness in chickens is then analyzed. Contrarily to the analysis based on the raw data, a relevant functional information about a QTL region is revealed by factor-adjustment of the gene expressions. Additionally, the interpretation of the independent factors regarding known information about both experimental design and genes shows that some factors may have different and complex origins. As biological information and technological biases are identified in what was before simply considered as statistical noise, analyzing heterogeneity in gene expression yields a new point of view on transcriptomic data.
  BMC Bioinformatics 01/2010; 11:368. · 2.75 Impact Factor
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  Available from: archives-ouvertes.fr

  Article: Inférence sur réseaux géniques par Analyse en Facteurs

  Yuna Blum, Chloé Friguet, Sandrine Lagarrigue, David Causeur
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  ABSTRACT: La technologie des puces à ADN permet l'analyse simultanée du niveau d'expression de plusieurs milliers de gènes. Un des enjeux de l'analyse de ce type de données est de comprendre la structure de dépendance, qui rend compte des relations biologiques entre les gènes. En particulier, on s'intéresse ici à la modélisation du réseau de régulation des gènes impliqués dans le contrôle d'un caractère phénotypique. Dans un premier temps, on définit un cadre général pour la prise en compte de la dépendance par l'identification de facteurs latents, modélisant la variation commune à l'ensemble des gènes. On montre que l'introduction de ces facteurs dans les procédures d'analyse différentielle en améliore la puissance ainsi que la stabilité des taux d'erreurs. De plus, dans le contexte des modèles graphiques gaussiens pour la modélisation des réseaux d'interactions entre gènes, on présente une méthode d'estimation des corrélations partielles s'appuyant sur la réduction de la dimension des données par les variables latentes. La méthode est illustrée par son application à une étude visant à identifier les gènes impliqués dans le métabolisme des lipides chez le poulet (UMR INRA Génétique Animale de Rennes).
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