The gut microbiome is important for nutrition with potential positive and negative on host linear growth. However, current microbiome approaches based on 16S amplicon or short-read (SR) metagenomics from a single ‘cross-sectional’ time point are limited in that they only have the resolution to evaluate a snapshot of changes in taxonomic or functional abundances across participants. We hypothesized that complete metagenome-assembled-genomes (cMAGs), generated from a longitudinal, long-read (LR) metagenomics cohort, were critical for pangenome and microbial GWAS (mGWAS) analyses for identifying microbial genetic associations with pediatric linear growth trajectories. Here, we showed that single molecule sequencing (PacBio ‘PB’ and ONT) approaches generate 51-72x more cMAGs per Gbp than legacy SR approaches and that PB generated the most accurate, complete cMAGs at the lowest cost making the approach feasible and accessible for microbiome research. When applied to a pediatric undernutrition cohort in Malawi Africa, we generated 985 cMAGs (831 circular) from 47 samples, performed independent functional pangenome and mGWAS analyses across multiple clades, and identified microbial genetic associations with various environmental and biological phenotypes related to undernutrition. Our longitudinal pangenome and mGWAS analysis of these cMAGs revealed genetic differences within the same bacterial species found in different participant phenotypes highlighting the importance of this approach for new diagnostic methods or therapeutic development. We revealed new insights to strain abundances and genome evolution at both the collective population along with the individual participant as it related to linear growth trajectories laying the foundation of a new framework and resource to understand undernutrition. The metagenome resource we present here demonstrates that longitudinally sampled LR-derived cMAGs establish a new standard for microbiome association studies as well as providing a rich dataset for others to develop analysis tools. Finally, we describe how this approach can be utilized to improve food production of fish and shellfish by identifying the genetic associations of gut microbes with growth phenotypes.