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Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function.

Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.

Animals↗

Genome-wide association studies for common diseases and complex traits.

Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.

Animals↗

DNA pooling as a tool for large-scale association studies in complex traits.

Pooled genotyping is a powerful and efficient tool for high throughput association analysis, both case-control and family-based. The use of pooling designs can significantly reduce the consumable and labour costs of a study. At the same time, since it is also extremely efficient with DNA resources, pooling can be an extremely effective method for conserving DNA. Here, we consider the main points in study design, and illustrate the application of pooling using psychiatric phenotypes as an example.

DNA↗

Haplotype analysis and identification of genes for a complex trait: examples from schizophrenia.

For more than a decade there has been intensive research into the genetic etiology of schizophrenia, yet it is only recently that the first findings of promising genes associating with the disorder have been reported. Linkage analyses in families collected from different populations have provided relatively well defined genomic loci. These have been typically followed by fine mapping studies using single nucleotide polymorphisms (SNPs). A number of analysis programs have been produced to test SNPs and their haplotypes for association. Typically association has been established to specific haplotypes representing an allelic variant of the corresponding gene. The inherent problem of multiple testing in the analysis of haplotypes needs to be addressed fully, to determine if any of these recent findings can be considered as confirmed susceptibility genes for schizophrenia. However, informative haplotypes have provided a way to define allelic variants of genes associated with schizophrenia in numerous study samples, and are a useful tool in characterizing the extent of allelic diversity of putative schizophrenia susceptibility genes within different populations.

Chromosome Mapping↗