The trait coding rule in phenotype space

Abstract

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 (PG) and a subspace affected by non-genetic factors (PNG). Evolutionary theory predicts PG is composed of limited dimensions while PNG 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 ~1,000 individuals. The obtained tentative PG 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 PG be the yeast genetic subspace. A limited number of latent dimensions in the PG were found to be recurrently used for coding the diverse yeast traits, while dimensions in the PNG 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 PG 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.