The impact of structural variation on human gene expression

Chiang, Colby; Scott, Alexandra J.; Davis, Joe R.; Tsang, Emily K.; Li, Xin; Kim, Yungil; Hadžić, Tarik; Damani, Farhan N.; Ganel, Liron; Montgomery, Stephen B.; Battle, Alexis; Conrad, Donald F.; Hall, Ira M.

doi:10.1038/ng.3834

Cited by 382 publications

(388 citation statements)

References 57 publications

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“…Multi-tissue outliers were strongly enriched for nearby rare variants. The enrichment was most pronounced for structural variants (SVs) as previously described 14 , and greater for short insertions and deletions (indels) than for single nucleotide variants (SNVs) (Fig 2a, Extended Data Fig. 4).…”

supporting

confidence: 54%

The impact of rare variation on gene expression across tissues

Kim

Tsang

et al. 2017

Nature

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238

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Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1,2,3,4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8,9,10,11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

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supporting

confidence: 54%

The impact of rare variation on gene expression across tissues

Kim

Tsang

et al. 2017

Nature

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238

251

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“…CNVs may also alter the expression of genes that are upstream or downstream of the CNV by disrupting regulatory elements like enhancers or promoters [45,46]. A 20 kb deletion which is 2.7 kb upstream of IRGM is associated with altered expression of the gene leading to Crohn’s disease [47]).…”

Section: Mechanisms Of Disease Pathogenesis In Cnv Disordersmentioning

confidence: 99%

Copy Number Variation Disorders

Shaikh

2017

Curr Genet Med Rep

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Purpose of Review Copy number variation (CNV) disorders arise from the dosage imbalance of one or more gene(s), resulting from deletions, duplications or other genomic rearrangements that lead to the loss or gain of genetic material. Several disorders, characterized by multiple birth defects and neurodevelopmental abnormalities, have been associated with relatively large (>1 Mb) and often recurrent CNVs. CNVs have also been implicated in the etiology of neuropsychiatric disorders including autism and schizophrenia as well as other common complex diseases. Thus, CNVs have a significant impact on human health and disease. Recent Findings The use of increasingly higher resolution, genomewide analysis has greatly enhanced the detection of genetic variation, including CNVs. Furthermore, the availability of comprehensive genetic variation data from large cohorts of healthy controls has the potential to greatly improve the identification of disease associated genetic variants in patient samples. Summary This review discusses the current knowledge about CNV disorders, including the mechanisms underlying their formation and phenotypic outcomes, and the advantages and limitations of current methods of detection and disease association.

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“…Pathogenic sequence variation can range in size from single-nucleotide variants (SNVs), small insertions and deletions (‘indels’) of fewer than 50 base-pairs in length, to larger structural variants (SVs) [23], which are generally classified as regions of genomic variation greater than 1 kb, such as copy-number variants (CNVs), insertions, retrotransposon elements, inversions, segmental duplications, and other such genomic rearrangements [24, 25]. Currently, the consequence of non-synonymous variants of the protein-coding elements only can be routinely automatically predicted by algorithms such as SIFT and PolyPhen [26], yet many different types of variants are implicated in disease.…”

Section: Genome Sequencingmentioning

confidence: 99%

Genome annotation for clinical genomic diagnostics: strengths and weaknesses

et al. 2017

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The Human Genome Project and advances in DNA sequencing technologies have revolutionized the identification of genetic disorders through the use of clinical exome sequencing. However, in a considerable number of patients, the genetic basis remains unclear. As clinicians begin to consider whole-genome sequencing, an understanding of the processes and tools involved and the factors to consider in the annotation of the structure and function of genomic elements that might influence variant identification is crucial. Here, we discuss and illustrate the strengths and weaknesses of approaches for the annotation and classification of important elements of protein-coding genes, other genomic elements such as pseudogenes and the non-coding genome, comparative-genomic approaches for inferring gene function, and new technologies for aiding genome annotation, as a practical guide for clinicians when considering pathogenic sequence variation. Complete and accurate annotation of structure and function of genome features has the potential to reduce both false-negative (from missing annotation) and false-positive (from incorrect annotation) errors in causal variant identification in exome and genome sequences. Re-analysis of unsolved cases will be necessary as newer technology improves genome annotation, potentially improving the rate of diagnosis.

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The impact of structural variation on human gene expression

Cited by 382 publications

References 57 publications

The impact of rare variation on gene expression across tissues

The impact of rare variation on gene expression across tissues

Copy Number Variation Disorders

Genome annotation for clinical genomic diagnostics: strengths and weaknesses

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