Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data

Upwards of 40% of reads in sequencing datasets may be unmapped and discarded by standard protocols. Recent work has shown the utility of re-analyzing these unmapped reads to construct meaningful features, such as immune diversity repertoires or copy number variation in mtDNA and rDNA. While previous analyses of these features have produced significant correlations with diverse traits, they have generally been limited to analyses of RNA-sequencing data in phenotype-specific cohorts. Here, we explore whether associations can be identified using population-scale, whole-exome sequencing data in the UK BioBank. Using recently developed tools, we constructed multiple features including T-cell receptor diversity metrics, microbial load, and mtDNA and rDNA copy numbers for nearly 50,000 individuals in the UK BioBank. We first verify the validity of our method by showing that GWAS on these constructed traits results in replication of associations from studies in which the phenotypes were explicitly measured. Next, across several GWAS, we identified 21 novel independent significant loci in 11 genes, most of them in genes implicated in the innate immune response. Finally, we further analyzed the read-constructed features by establishing correlations to other population-level biobank traits such as immune disorders, metabolic disorders, neuropsychiatric disorders, and blood cell counts. Our results suggest that existing tools for feature construction from unmapped reads can offer novel information at the population level, and that these features can be used to establish novel genetic associations.