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Decomposing genomic variance using information from GWA, GWE and eQTL analysis.

A Ehsani1, L Janss2, D Pomp3

  • 1Animal Science Department, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115-336, Tehran, Iran.

Animal Genetics
|December 19, 2015
PubMed
Summary

This study introduces a novel top-down genomic variance analysis for complex traits. It reveals that specific regulatory single nucleotide polymorphisms (SNPs) significantly influence trait inheritance more than previously understood.

Keywords:
BayesianGWASSNPsarea under the curveblood glucosebody fatbody weightdecompositionmouse

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Area of Science:

  • Genomics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Traditional genome-wide association (GWA), genome-wide expression (GWE), and expression quantitative trait locus (eQTL) analyses use a bottom-up approach to link molecular variants to complex traits.
  • A top-down modeling approach partitioning overall genomic variance may offer deeper insights into the genetic architecture of complex traits.

Purpose of the Study:

  • To test a whole-genome variance components analysis for partitioning genomic variance.
  • To characterize the genetic architecture of growth-related traits in a mouse F2 population using GWA, GWE, and eQTL data.
  • To introduce and utilize Area Under the Curve (AUC) measures for genomic variance profiles.

Main Methods:

  • Performed whole-genome variance components analysis on growth-related traits in a mouse F2 population.
  • Partitioned genomic variance using GWA, GWE, and eQTL data.
  • Ordered single nucleotide polymorphisms (SNPs) by P-values and calculated AUCs to characterize genetic architecture.

Main Results:

  • Observed traits exhibited a genomic variance profile significantly deviating from the infinitesimal model, particularly body weight and body fat.
  • SNPs with high trait-specific regulatory potential explained more genomic variance than those with high overall regulatory potential.
  • AUC measures effectively quantify SNP importance and deviation from the infinitesimal model.

Conclusions:

  • The top-down variance partitioning approach provides a global understanding of trait genetic architecture.
  • The shape of the genomic variance profile curve indicates the number of SNPs controlling phenotypic variance.
  • Trait-specific regulatory SNPs play a crucial role in the genetic basis of complex traits.