  Discovering all the genetic causes of a phenotype is an important goal in
functional genomics. In this paper we combine an experimental design for
multiple independent detections of the genetic causes of a phenotype, with a
high-throughput sequencing analysis that maximizes sensitivity for
comprehensively identifying them. Testing this approach on a set of 24 mutant
strains generated for a metabolic phenotype with many known genetic causes, we
show that this pathway-based phenotype sequencing analysis greatly improves
sensitivity of detection compared with previous methods, and reveals a wide
range of pathways that can cause this phenotype. We demonstrate our approach on
a metabolic re-engineering phenotype, the PEP/OAA metabolic node in E. coli,
which is crucial to a substantial number of metabolic pathways and under
renewed interest for biofuel research. Out of 2157 mutations in these strains,
pathway-phenoseq discriminated just five gene groups (12 genes) as
statistically significant causes of the phenotype. Experimentally, these five
gene groups, and the next two high-scoring pathway-phenoseq groups, either have
a clear connection to the PEP metabolite level or offer an alternative path of
producing oxaloacetate (OAA), and thus clearly explain the phenotype. These
high-scoring gene groups also show strong evidence of positive selection
pressure, compared with strictly neutral selection in the rest of the genome.
