Future directions of research in statistical genetics

Horvath, Stefan; Baur, Max P.

doi:10.1002/1097-0258(20001230)19:24<3337::aid-sim828>3.0.co;2-3

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…Both cis-and trans-acting factors regulate genes, perhaps in combination in a complex disorder, as suggested by our results and others . Improvements in the analytical methods are needed to integrate differential gene expression as a phenotype and linkage to genomic markers (Schadt et al 2003a(Schadt et al ,2003bKraft et al 2003;Sham et al 2002;Horvath and Baur 2000;Middleton et al 2005). However, taken together, linkage analysis of the inheritability of gene expression traits will be useful for mapping regulators of gene expression.…”

Section: Discussionmentioning

confidence: 99%

Genome scans and gene expression microarrays converge to identify gene regulatory loci relevant in schizophrenia

et al. 2006

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Multiple linkage regions have been reported in schizophrenia, and some appear to harbor susceptibility genes that are differentially expressed in postmortem brain tissue derived from unrelated individuals. We combined traditional genome-wide linkage analysis in a multiplex family with lymphocytic genome-wide expression analysis. A genome scan suggested linkage to a chromosome 4q marker (D4S1530, LOD 2.17, θ=0) using a dominant model. Haplotype analysis using flanking microsatellite markers delineated a 14 Mb region that cosegregated with all those affected. Subsequent genome-wide scan with SNP genotypes supported the evidence of linkage to 4q33−35.1 (LOD=2.39) using a dominant model. Genome-wide microarray analysis of five affected and five unaffected family members identified two differentially expressed genes within the haplotype AGA and GALNT7 (aspartylglucosaminidase and UDP-N-acetyl-alpha-D-galactosamine: polypeptide N-acetylgalactosaminyltransferase 7) with nominal significance; however, these genes did not remain significant following analysis of covariance. We carried out genome-wide linkage analyses between the quantitative expression phenotype and genetic markers. AGA expression levels showed suggestive linkage to multiple markers in the haplotype (maximum LOD=2.37) but to no other genomic region. GALNT7 expression levels showed linkage to regulatory loci at 4q28.1 (maximum LOD=3.15) and in the haplotype region at 4q33−35.1 (maximum LOD=2.37). ADH1B (alcohol dehydrogenase IB) was linked to loci at 4q21-q23 (maximum LOD=3.08) and haplotype region at 4q33−35.1 (maximum LOD=2.27). Seven differentially expressed genes were validated with RT-PCR. Three genes in the 4q33−35.1 haplotype region were also differentially expressed in schizophrenia in postmortem dorsolateral prefrontal cortex: AGA, HMGB2, and SCRG1. These results indicate that combining differential gene expression with linkage analysis may help in

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Section: Discussionmentioning

confidence: 99%

Genome scans and gene expression microarrays converge to identify gene regulatory loci relevant in schizophrenia

et al. 2006

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“…Complementary to positional cloning, gene, including protein, expression analyses also may be employed to identify novel candidates for disease susceptibility loci (Niculescu et al 2000). Functional analysis attempts to dissect disease processes and relevant biochemical pathways into component parts, which serve as intermediaries between genotypes and phenotype information and to bridge the gap between DNA information and trait information (Horvath and Baur 2000). We expected that the linkage studies and functional analysis would cross-validate the findings of each method, reducing the uncertainty inherent in the two approaches.…”

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confidence: 99%

Biomarker Identification by Feature Wrappers

Xiong¹,

Fang²,

Zhao³

2001

Genome Res.

243

146

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Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.

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“…New technologies, such as DNA microarrays, measure the relative abundance of mRNA produced by many genes simultaneously. This information may be one step closer to the relevant biology than classically defined clinical traits and opens up interesting strategies for understanding complex diseases (Horvath and Baur 2000). In an increasingly common application, genes are func-tionally classified according to their expression across different conditions (Quackenbush 2001).…”

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confidence: 99%

A Family-Based Test for Correlation between Gene Expression and Trait Values

Kraft

Schadt

Aten

et al. 2003

The American Journal of Human Genetics

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Advances in microarray technology have made it attractive to combine information on clinical traits, marker genotypes, and comprehensive gene expression from family studies to dissect complex disease genetics. Without accounting for family structure, methods that test for association between a trait and gene-expression levels can be misleading. We demonstrate that the standard unstratified test based on Pearson's correlation coefficient can produce spurious results when applied to family data, and we present a stratified family expression association test (FEXAT). We illustrate the utility of the FEXAT via simulation and an application to gene-expression data from lymphoblastoid cell lines from four CEPH families. The FEXAT has a smaller estimated false-discovery rate than the standard test when within-family correlations are of interest, and it detects biologically plausible correlations between beta catenin and genes in the WNT-activation pathway in humans that the standard test does not.

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Future directions of research in statistical genetics

Cited by 13 publications

References 17 publications

Genome scans and gene expression microarrays converge to identify gene regulatory loci relevant in schizophrenia

Genome scans and gene expression microarrays converge to identify gene regulatory loci relevant in schizophrenia

Biomarker Identification by Feature Wrappers

A Family-Based Test for Correlation between Gene Expression and Trait Values

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