PT  - JOURNAL ARTICLE
AU  - Kévin Vervier
AU  - Jacob J Michaelson
TI  - TiSAn: Estimating Tissue Specific Effects of Genetic Variants
AID  - 10.1101/141408
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 141408
4099  - http://biorxiv.org/content/early/2017/07/24/141408.short
4100  - http://biorxiv.org/content/early/2017/07/24/141408.full
AB  - The impact of non-coding genetic variation on molecular functions, such as gene expression, varies across tissues and cell types. Consequently, whether or not a genetic variant is deleterious depends on its tissue-specific context. We introduce a functional Tissue-Specific Annotation (TiSAn) tool that predicts how related a genomic position is to a given tissue (http://github.com/kevinVervier/TiSAn). These predictions can then be used to contextualize and filter variants of interest in whole genome sequencing or genome wide association studies (GWAS). We demonstrate the accuracy and versatility of TiSAn by introducing predictive models for human heart and human brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder and coronary artery disease.Author Summary Genome-wide association studies (GWAS) provide insights into the mechanisms underlying diseases and other traits, yet interpreting the effect of non-coding genetic variation (which represents the majority of GWAS hits) is an ongoing and unresolved challenge. This is evident because while we as a field are sequencing more and more whole genomes, we often retreat to the exome portion whenever we want to propose a clear interpretation of the variants we discover. Current annotation approaches are effective at predicting whether a given variant is damaging, and potentially disease-associated. However, these methods do not provide information on which organs or tissues are most susceptible to the variant’s effect. The work presented here addresses these challenges. It describes the function and implementation of a machine learning approach (TiSAn) to annotate genetic variants, especially non-coding variants, according to their function in a specific tissue. Such annotation is critical in prioritizing genetic variations and making inferences about which tissues, organs, and systems they will most likely impact. We detail several use cases, including interpreting variants from whole genome sequencing studies of autism, as well as GWAS hits from studies of cardiovascular disease. Further analyses demonstrating the power of tissue-specific variant annotation are included in the supplementary materials. Thorough documentation for TiSAn, including tutorials, can be found at http://github.com/kevinVervier/TiSAn.