Co-regulation and multilocus determinants of gene expression in humans

Center for Neurobehavioral Genetics, Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, 695 Charles E, Young Drive South, Los Angeles, California 90095-1761, USA.
BMC proceedings 12/2007; 1 Suppl 1(Suppl 1):S88. DOI: 10.1186/1753-6561-1-s1-s88
Source: PubMed


The regulation of gene expression is an emerging area of investigation. Increased knowledge can deepen our understanding of the genetic contributions to variations in complex traits. The purpose of this study is to explore the feasibility of detecting regulatory elements of gene expression with multivariate analyses.
Peripheral blood lymphocyte expression levels of 30 genes on chromosome 5 and a single gene, DEAD, on chromosome 22 were analyzed in single-point variance-component linkage analyses in multiplex families to identify putative regulatory regions. To explore the possibility of regulatory regions having individual relationships with the expression levels of a single gene, we utilized stepwise regression. To explore the possibility of pleiotropy of a single regulatory locus for multiple genes, bivariate linkage analysis was applied.
Twenty-one loci were linked to five expression levels. The two most significant were for the known region on chromosome 22 (LOD = 4.62). On chromosome 5 a LOD of 4.57 was found for the gene leukocyte-derived arginine aminopeptidase (LRAP) with a single-nucleotide polymorphism (SNP) within 5 Mb. Both genes showed evidence of linkage to multiple SNPs. When 194 family members were treated as independent, stepwise regression identified fewer single-nucleotide polymorphisms with significant predictive values (p < 0.05), providing evidence for multiple regulatory regions of unequal effect. However, when corrections for non-independence were applied these results could no longer be detected.
The complex nature of gene regulation can be explored by linkage analysis with single-nucleotide polymorphisms followed by multivariate methods to explore co-regulation.

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