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Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape.

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This study introduces a new multivariate genotype-phenotype approach to link gene collections to craniofacial shape variation in mice. It reveals how coordinated gene actions influence complex traits, offering broader implications for genetic research.

Keywords:
complex traitscraniofacialdiversity outcrossgeneticsgenomicsmousemultivariate genotype-phenotype map

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Area of Science:

  • Genetics and Genomics
  • Developmental Biology
  • Quantitative Trait Genetics

Background:

  • Morphological traits result from complex, multivariate gene interactions.
  • Current genetic studies often focus on single genes, overlooking coordinated effects.
  • High-dimensional phenotypic data, like craniofacial shape, are underutilized in genetic analyses.

Purpose of the Study:

  • To develop and apply a process-centered, multivariate genotype-phenotype (process MGP) approach.
  • To model the joint effects of biologically coherent gene collections on craniofacial shape variation.
  • To investigate the contributions of specific biological processes to complex trait variation.

Main Methods:

  • Utilized Gene Ontology (GO) annotations to define gene sets for skeletal and facial development.
  • Applied a multivariate statistical framework to analyze genotype-phenotype relationships in 1145 Diversity Outbred (DO) mice.
  • Solved for axes of shape variation that maximally covary with gene set marker variation.

Main Results:

  • Identified specific biological processes contributing to craniofacial variation.
  • Characterized the correspondence between gene set variation and multivariate axes of shape.
  • Demonstrated the ability to predict phenotypic effects of mutations based on pathway associations.

Conclusions:

  • The process MGP approach effectively links collections of genes to complex morphological phenotypes.
  • This method advances our understanding of coordinated gene action in development and evolution.
  • The approach is broadly applicable to various continuously varying phenotypes in complex trait genetics.