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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Gene expression in large pedigrees: analytic approaches.

Rita M Cantor1, Heather J Cordell2

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Summary

Gene expression is familial, influenced by relationships within pedigrees. Integrating genotype and phenotype data improves the significance and interpretability of gene expression studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Microarray technologies enable genome-wide quantification of messenger RNA (mRNA) abundance.
  • Genetic Analysis Workshop (GAW) 19 gene expression group analyzed genotype, phenotype, and expression data from 20 pedigrees.
  • Statistical tools were developed and applied to study the complexity of genetics and gene expression.

Purpose of the Study:

  • To investigate gene expression correlations within pedigrees.
  • To explore the relationship between genetics and gene expression.
  • To assess methods for filtering regulatory single-nucleotide polymorphisms (SNPs).

Main Methods:

  • Principal components analysis, weighted gene coexpression network analysis, meta-analyses, and linear mixed models were used for gene expression correlations.
  • Conditional association analyses and interaction analyses explored genotype-gene expression relationships.
  • Linear mixed models, permutation tests, covariance kernels, weighted U statistics, structural equation modeling, and Bayesian frameworks were employed.

Main Results:

  • Gene expression is familial, necessitating the inclusion of pedigree membership or relationship factors in analyses.
  • FaST-LMM and SOLAR-MGA showed similar performance for SNP association and conditional analyses.
  • Expression quantitative trait loci (eQTL) are genetically complex, exhibiting allelic heterogeneity and epistasis.

Conclusions:

  • A method was developed to adjust for familiality before weighted co-expression gene network analysis.
  • Incorporating biological annotations can enhance the power and precision of association tests.
  • Integrating genotype, phenotype, and gene expression data offers advantages in statistical significance and biological interpretability.