Reference: Wang J and He X (2025) The Trait Coding Rule in Phenotype Space. Phenomics 5(6):646-663

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Abstract


Unlabelled: Genotype and phenotype are both the themes of modern biology. Despite the elegant protein coding rules recognized decades ago in genotype, little is known on how traits are coded in a phenotype space (P). Mathematically, P can be partitioned into a subspace determined by genetic factors (P G) and a subspace affected by non-genetic factors (P NG). Evolutionary theory predicts P G is composed of limited dimensions while P NG may have infinite dimensions, which suggests a dimension decomposition method, termed as uncorrelation-based high-dimensional dependence (UBHDD), to separate them. We applied UBHDD to a yeast phenotype space comprising ~ 400 traits in ~ 1000 individuals. The obtained tentative P G matches the actual genetic components of the yeast traits, explains the broad-sense heritability, and facilitates the mapping of quantitative trait loci, suggesting the tentative P G be the yeast genetic subspace. A limited number of latent dimensions in the P G were found to be recurrently used for coding the diverse yeast traits, while dimensions in the P NG tend to be trait specific and increase constantly with trait sampling. A similar separation success was achieved when applying UBHDD to the UK Biobank human brain phenotype space that comprises ~ 700 traits in ~ 26,000 individuals. The obtained P G helped elucidate the genetic versus non-genetic origins of the left-right asymmetry of human brain, and reveal several hundred novel genetic correlations between brain regions and dozens of mental traits/diseases. In sum, by developing a dimension decomposition method we show that phenotypic traits are coded by a limited number of genetically determined common dimensions and unlimited trait-specific dimensions shaped by non-genetic factors, a rule fundamental to the emerging field of phenomics.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00195-5.

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Journal Article
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Wang J, He X
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