Telomeres protect the ends of linear chromosomes and as humans age, telomere length (TL) decreases. When telomeres become critically short, a senescence or apoptosis signal prevents further telomere loss. Individuals with extremely short TL present with Short Telomere Syndromes (STS), including bone marrow failure and immunodeficiency, while individuals with extremely long TL are predisposed to cancer. To gain insight into TL genetic regulation, prior work from our group and others used genome-wide association studies to examine the role of common genetic variation in TL. This strategy identified novel genes involved in TL regulation, some of which we experimentally validated. However, this approach ignores the effects of rare variation, which can have larger effect sizes and uniquely impact genes under strong constraint.

Studies of rare variant effects on TL have improved our understanding of TL biology, but have largely required laborious STS patient pedigree studies. We leveraged TL estimates and rare variant data from the Trans-Omics for Precision Medicine (TOPMed) Program to broadly examine the impact of rare variation on TL. Previously we developed Watershed, a Bayesian hierarchical model, which uses whole genome sequencing with paired multi-omic data (expression, splicing, methylation, and/or protein levels) to prioritize rare variants causing significant disruption of molecular phenotypes. This multi-omic signature generates interpretable hypotheses for coding and non-coding rare variants, providing a posterior probability that the variant causes outlier status for each molecular signal, for example that splicing is disrupted but expression is not. We used data from 5,310 MESA individuals to train Watershed and observed that in 40/86 individuals with extremely short TL (<1% in TOPMed), Watershed prioritized rare variants in at least one gene from a panel of 16 STS genes. The variant with the largest posterior probability (0.984) was predicted to affect expression of TPP1, which encodes a protein critical for TL regulation.

We will expand our analysis to another 103,812 TOPMed individuals and incorporate multi-omic data where available. Examination of highly weighted variants in individuals with extreme TL relative to average TL will potentially identify novel genes involved in TL regulation. In addition, we will examine the interplay between TL regulation and multi-omic signals over age (0-98 years old). Finally, we will apply our model to data from STS patients to improve their genetic diagnosis. Together this work has utility in improving STS patient diagnosis and furthering our understanding of the molecular mechanisms governing TL.