bioRxivGenomicsPreprint
Population-scale variability at short tandem repeat loci reveals pathogenicity signature
Matt Christopher DanziiD, Isaac R. L. XuiD, Sarah FazaliD, Egor DolzhenkoiD, David PelleriniD, Ben WeisburdiD, Liedewei Van de VondeliD, Chloe M. ReuteriD, Jacinda B. SampsoniD, Chiara L FollandiD, Carolin K. ScribaiD, Gavin MonahaniD, Phillipa J. LamontiD, Julie WertziD, Adriana Rebelo, Sophia B. Gibson, Daniel G. CalameiD, Haloom RafehiiD, Penny SnelliD, Kate Kotschet, Kayli C. DaviesiD, Igor StevanovskiiD, Ira W. DevesoniD, Danny E. MilleriD, Chia-Lin WeiiD, , Stephan L. ZuchneriD
Abstract
Short tandem repeat (STR) instability is a major cause of disease association, yet their full length-distributions and sequence content have been difficult to resolve with short-read sequencing. By analyzing long-read sequencing genomes from 2,645 participants of the All of Us Research Program, we establish a comprehensive STR resource of 23 billion alleles covering a catalog of 4.4 million loci. We find that known disease-associated STR loci exhibit markedly elevated allelic variability relative to genome-wide distributions, which is most pronounced when based on the length of uninterrupted, pure repeat motifs (Odds Ratio: 244, p=1.62e-29). We establish the Pure Length Variability Index (PLVI) as a measure of this instability, which is robust across ancestries and replicated in 500 samples from the 1000 Genomes Project (Odds Ratio: 84, p=2.72e-28). To illustrate, we identify 55 unstable coding CAG and 5-UTR CGG STR loci with this metric, 60% of which have not been associated with disease. From this set, we document potential pathogenic repeat expansions for FAM193B and EP400 by integrating PLVI with single-genome length outlier analysis. These data establish a population-scale framework for STR discovery in rare and common diseases using long-read sequencing cohorts.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: variant calling
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 13
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