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Using transcriptome profiling to characterize QTL regions on chicken chromosome 5.

ABSTRACT: BACKGROUND: Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5). RESULTS: Hepatic transcriptome profiles for 45 offspring of a sire known to be heterozygous for the distal GGA5 AF QTL were obtained using a 20 K chicken oligochip. mRNA levels of 660 genes were correlated with the AF trait. The first approach was to dissect the AF phenotype by identifying animal subgroups according to their 660 transcript profiles. Linkage analysis using some of these subgroups revealed another QTL in the middle of GGA5 and increased the significance of the distal GGA5 AF QTL, thereby refining its localization. The second approach targeted the genes correlated with the AF trait and regulated by the GGA5 AF QTL region. Five of the 660 genes were considered as being controlled either by the AF QTL mutation itself or by a mutation close to it; one having a function related to lipid metabolism (HMGCS1). In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region. The third approach was to use these 5 transcriptome profiles to predict the paternal Q versus q AF QTL mutation for each recombinant offspring and then refine the localization of the QTL from 31 cM (100 genes) at a most probable location confidence interval of 7 cM (12 genes) after determining the recombination breakpoints, an interval consistent with the reductions obtained by the two other approaches. CONCLUSION: The results showed the feasibility and efficacy of the three strategies used, the first revealing a QTL undetected using the whole population, the second providing functional information about a QTL region through genes related to the trait and controlled by this region (HMGCS1), the third could drastically refine a QTL region.

SUBMITTER: Le Mignon G

PROVIDER: S-EPMC2792231 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Using transcriptome profiling to characterize QTL regions on chicken chromosome 5.

Le Mignon Guillaume G Désert Colette C Pitel Frédérique F Leroux Sophie S Demeure Olivier O Guernec Gregory G Abasht Behnam B Douaire Madeleine M Le Roy Pascale P Lagarrigue Sandrine S

BMC genomics 20091202

<h4>Background</h4>Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5).<h4>Results</h4>He ...[more]

PMID: 19954542

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Project description:BACKGROUND: Starvation triggers a complex array of adaptative metabolic responses including energy-metabolic responses, a process which must imply tissue specific alterations in gene expression and in which the liver plays a central role. The present study aimed to describe the evolution of global gene expression profiles in liver of 4-week-old male chickens during a 48 h fasting period using a chicken 20 K oligoarray. RESULTS: A large number of genes were modulated by fasting (3532 genes with a pvalue corrected by Benjamini-Hochberg < 0.01); 2062 showed an amplitude of variation higher than +/- 40% among those, 1162 presented an human ortholog, allowing to collect functional information. Notably more genes were down-regulated than up-regulated, whatever the duration of fasting (16 h or 48 h). The number of genes differentially expressed after 48 h of fasting was 3.5-fold higher than after 16 h of fasting. Four clusters of co-expressed genes were identified by a hierarchical cluster analysis. Gene Ontology, KEGG and Ingenuity databases were then used to identify the metabolic processes associated to each cluster. After 16 h of fasting, genes involved in ketogenesis, gluconeogenesis and mitochondrial or peroxisomal fatty acid beta-oxidation, were up-regulated (cluster-1) whereas genes involved in fatty acid and cholesterol synthesis were down-regulated (cluster-2). For all genes tested, the microarray data was confirmed by quantitative RT-PCR. Most genes were altered by fasting as already reported in mammals. A notable exception was the HMG-CoA synthase 1 gene, which was up-regulated following 16 and 48 h of fasting while the other genes involved in cholesterol metabolism were down-regulated as reported in mammalian studies. We further focused on genes not represented on the microarray and candidates for the regulation of the target genes belonging to cluster-1 and -2 and involved in lipid metabolism. Data are provided concerning PPARa, SREBP1, SREBP2, NR1H3 transcription factors and two desaturases (FADS1, FADS2). CONCLUSION: This study evidences numerous genes altered by starvation in chickens and suggests a global repression of cellular activity in response to this stressor. The central role of lipid and acetyl-CoA metabolisms and its regulation at transcriptional level are confirmed in chicken liver in response to short-term fasting. Interesting expression modulations were observed for NR1H3, FADS1 and FADS2 genes. Further studies are needed to precise their role in the complex regulatory network controlling lipid metabolism.

Project description:Transgressive segregation in cotton (Gossypium spp.) provides an important approach to enhance resistance to the major pest root-knot nematode (RKN) Meloidogyne incognita. Our previous studies reported transgressive RKN resistance in an intraspecific Gossypium hirsutum resistant NemX × susceptible SJ-2 recombinant inbred line (RIL) population and early generations of interspecific cross Gossypium barbadense (susceptible Pima S-7) × G. hirsutum (NemX). However, the underlying functional mechanisms for this phenomenon are not known. In this study, the region of RKN resistance gene rkn1 on chromosome (Chr) 11 and its homoeologous Chr 21 was fine mapped with G. raimondii D5 genome reference sequence. Transgressive resistance was found in the later generation of a new RIL population F2:7 (Pima S-7 × NemX) and one interspecific F2 (susceptible Pima S-7 × susceptible SJ-2). QTL analysis revealed similar contributions to root-galling and egg-production resistance phenotypes associated with SSR marker CIR316 linked to resistance gene rkn1 in NemX on Chr 11 in all seven populations analyzed. In testcross NemX × F1 (Pima S-7 × SJ-2) marker allele CIR069-271 from Pima S-7 linked to CIR316 contributed 63% of resistance to galling phenotype in the presence of rkn1. Similarly, in RIL population F2:8 (NemX × SJ-2), SJ-2 markers closely linked to CIR316 contributed up to 82% of resistance to root-galling. These results were confirmed in BC1F1 SJ-2 × F1 (NemX × SJ-2), F2 (NemX × SJ-2), and F2 (Pima S-7 × SJ-2) populations in which up to 44, 36, and 15% contribution in resistance to galling was found, respectively. Transgressive segregation for resistance was universal in all intra- and inter-specific populations, although stronger transgressive resistance occurred in later than in early generations in the intraspecific cross compared with the interspecific cross. Transgressive effects on progeny from susceptible parents are possibly provided in the rkn1 resistance region of chromosome 11 by tandemly arrayed allele (TAA) or gene (TAG) interactions contributing to transgressive resistance. Complex TAA and TAG recombination and interactions in the rkn1 resistance region provide three genes and a model to study disease and transgressive resistance in polyploid plants, and novel genotypes for plant breeding.

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Using transcriptome profiling to characterize QTL regions on chicken chromosome 5.

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Using transcriptome profiling to characterize QTL regions on chicken chromosome 5.

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