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PMC:1860061 JSONTXT

Divisions (12) JSON TSV

div. section text # proj. length # Ann.
0 TIAB Combining global genome and transcriptome approaches to identify the candidate genes of small-effect 5 1257 684 show
1 Introduction Susceptibility to most complex diseases is controlled by many genes, each having a small effect on t 5 4567 2607 show
2 Materials and methods Animals, immunisation and assessment of arthritis Both DBA/1 and FVB/N mice used in this study were 5 1833 1142 show
3 Materials and methods Linkage analysis Detailed information on genotyping of the genome screen has been described previous 5 531 302 show
4 Materials and methods Sample preparation and microarray hybridisation Lymph nodes (LNs) draining the immunisation site wer 5 721 398 show
5 Materials and methods Microarray analysis Normalisation of the expression level was done using Affymetrix software MAS 5, 5 1369 711 show
6 Results Small-effect QTL of CIA in (DBA/1 × FVB/N) F2 progeny In a previous study, we carried out a genome s 5 2853 1888 show
7 Results Strain-specific differentially expressed genes We detected the gene expression profiles using three 5 1557 886 show
8 Results Disease-specific differentially expressed genes To identify the disease-specific differentially expr 5 2366 1378 show
9 Results Candidate genes for the small-effect QTL of CIA To identify candidate susceptibility genes for the C 5 1919 1199 show
10 Discussion In this study, we attempted for the first time to identify small-effect QTL in an F2 progeny. Small- 5 5729 3410 show
11 Conclusion We present a strategy to search candidate genes for small-effect QTL. With this strategy, we identif 5 843 474 show