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Colby Chiang

Publications and source records attributed to Colby Chiang.

3 recordsLinked to original sources

The impact of rare variation on gene expression across tissues

Rare genetic variants are abundant in humans yet their functional effects are often unknown and challenging to predict. The Genotype-Tissue Expression (GTEx) project provides a unique opportunity to identify the functional impact of rare variants through combined analyses of whole genomes and multi-tissue RNA-sequencing data. Here, we identify gene expression outliers, or individuals with extreme expression levels, across 44 human tissues, and characterize the contribution of rare variation to these large changes in expression. We find 58% of underexpression and 28% of overexpression outliers have underlying rare variants compared with 9% of non-outliers. Large expression effects are enriched for proximal loss-of-function, splicing, and structural variants, particularly variants near the TSS and at evolutionarily conserved sites. Known disease genes have expression outliers, underscoring that rare variants can contribute to genetic disease risk. To prioritize functional rare regulatory variants, we develop RIVER, a Bayesian approach that integrates RNA and whole genome sequencing data from the same individual. RIVER predicts functional variants significantly better than models using genomic annotations alone, and is an extensible tool for personal genome interpretation. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues with potential health consequences, and provide an integrative method for interpreting rare variants in individual genomes.

Genomics

The impact of structural variation on human gene expression

Structural variants (SVs) are an important source of human genetic diversity but their contribution to traits, disease, and gene regulation remains unclear. The Genotype-Tissue Expression (GTEx) project presents an unprecedented opportunity to address this question due to the availability of deep whole genome sequencing (WGS) and multi-tissue RNA-seq data from 147 individuals. We used comprehensive methods to identify 24,157 high confidence SVs, and mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single nucleotide (SNV) and short insertion/deletion (indel) variants. We identified 24,801 eQTLs affecting the expression of 10,101 distinct genes. Based on haplotype structure and heritability partitioning, we estimate that SVs are the causal variant at 3.3-7.0% of eQTLs, which is nearly an order of magnitude higher than prior estimates from low coverage WGS and represents a 26- to 54-fold enrichment relative to their scarcity in the genome. Expression-altering SVs also have significantly larger effect sizes than SNVs and indels. We identified 787 putatively causal SVs predicted to directly alter gene expression, most of which (88.3%) are noncoding variants that show significant enrichment at enhancers and other regulatory elements. By evaluating linkage disequilibrium between SVs, SNVs and indels, we nominate 49 SVs as plausible causal variants at published genome-wide association study (GWAS) loci. Remarkably, 29.9% of the common SV-eQTLs are not well tagged by flanking SNVs, and we observe a notable abundance (relative to SNVs and indels) of rare, high impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of both common and rare variant association studies.

Genomics

SpeedSeq: Ultra-fast personal genome analysis and interpretation

Comprehensive interpretation of human genome sequencing data is a challenging bioinformatic problem that typically requires weeks of analysis, with extensive hands-on expert involvement. This informatics bottleneck inflates genome sequencing costs, poses a computational burden for large-scale projects, and impedes the adoption of time-critical clinical applications such as personalized cancer profiling and newborn disease diagnosis, where the actionable timeframe can measure in hours or days. We developed SpeedSeq, an open-source genome analysis platform that vastly reduces computing time. SpeedSeq accomplishes read alignment, duplicate removal, variant detection and functional annotation of a 50X human genome in <24 hours, even using one low-cost server. SpeedSeq offers competitive or superior performance to current methods for detecting germline and somatic single nucleotide variants (SNVs), indels, and structural variants (SVs) and includes novel functionality for SV genotyping, SV annotation, fusion gene detection, and rapid identification of actionable mutations. SpeedSeq will help bring timely genome analysis into the clinical realm.\n\nAvailability: SpeedSeq is available at https://github.com/cc2qe/speedseq.

Bioinformatics