Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37: 233-242

University of Alabama at Birmingham, Birmingham, Alabama, United States
Nature Genetics (Impact Factor: 29.65). 04/2005; 37(3):233-42. DOI: 10.1038/ng1518
Source: PubMed

ABSTRACT 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.

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Available from: Nicole Elizabeth Baldwin, Aug 26, 2015
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    • "We converted permutationderived P-values to q-values with the QVALUE software, using the bootstrap method to estimate p 0 and the default l tuning parameters (Storey et al. 2004). We set the significance threshold for declaring an eQTL at a false discovery rate of 1% (Chesler et al. 2005). "
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    ABSTRACT: Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance. Fine-tuning of read alignment algorithms does not correct this problem. We have developed Seqnature software to construct individualized diploid genomes and transcriptomes for multiparent populations and have implemented a complete analysis pipeline that incorporates other existing software tools. We demonstrate in simulated and real data sets that alignment to individualized transcriptomes increases read mapping accuracy, improves estimation of transcript abundance, and enables the direct estimation of allele-specific expression. Moreover, when applied to expression QTL mapping we find that our individualized alignment strategy corrects false-positive linkage signals and unmasks hidden associations. We recommend the use of individualized diploid genomes over reference sequence alignment for all applications of high-throughput sequencing technology in genetically diverse populations.
    Genetics 09/2014; 198(1):59-73. DOI:10.1534/genetics.114.165886 · 4.87 Impact Factor
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    • "One of the main benefits of eQTL studies is the ability to form networks based on the correlation/covariation structure of the expression data across the experimental populations (Chesler et al. 2005). This allows relationships between expression traits to be expressed, for example, Trait A and Trait B are correlated and therefore there is potentially a relationship between the two traits. "
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    ABSTRACT: Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.
    Mammalian Genome 12/2013; 25(1-2). DOI:10.1007/s00335-013-9495-6 · 2.88 Impact Factor
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    • "Specific gene coexpression networks, once defined, are amenable to causal manipulation, deep investigation and dissection of the developmental, dynamic and compensatory effects of polymorphic variation. The large contaminated clusters of genetically co-expressed genes emerging from earlier studies (Chesler et al. 2005) rarely could lead directly to such experimentation. "
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    ABSTRACT: The historical origins of classical laboratory mouse strains have led to a relatively limited range of genetic and phenotypic variation, particularly for the study of behavior. Many recent efforts have resulted in improved diversity and precision of mouse genetic resources for behavioral research, including the Collaborative Cross and Diversity Outcross population. These two populations, derived from an eight way cross of common and wild-derived strains, have high precision and allelic diversity. Behavioral variation in the population is expanded, both qualitatively and quantitatively. Variation that had once been canalized among the various inbred lines has been made amenable to genetic dissection. The genetic attributes of these complementary populations, along with advances in genetic and genomic technologies, makes a systems genetic analyses of behavior more readily tractable, enabling discovery of a greater range of neurobiological phenomena underlying behavioral variation.
    Mammalian Genome 11/2013; 25(1-2). DOI:10.1007/s00335-013-9492-9 · 2.88 Impact Factor
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