Professional Education


  • Doctor of Philosophy, Stanford University, BIOM-PHD (2017)
  • Master of Science, Stanford University, STATS-MS (2016)
  • Bachelor of Science, McGill University, Biology and Computer Science (2012)

All Publications


  • The impact of structural variation on human gene expression NATURE GENETICS Chiang, C., Scott, A. J., Davis, J. R., Tsang, E. K., Li, X., Kim, Y., Hadzic, T., Damani, F. N., Ganel, L., Montgomery, S. B., Battle, A., Conrad, D. F., Hall, I. M. 2017; 49 (5): 692-?

    Abstract

    Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance 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 common- and rare-variant association studies.

    View details for DOI 10.1038/ng.3834

    View details for Web of Science ID 000400051400009

    View details for PubMedID 28369037

  • Small RNA Sequencing in Cells and Exosomes Identifies eQTLs and 14q32 as a Region of Active Export G3-GENES GENOMES GENETICS Tsang, E. K., Abell, N. S., Li, X., Anaya, V., Karczewski, K. J., Knowles, D. A., Sierra, R. G., Smith, K. S., Montgomery, S. B. 2017; 7 (1): 31-39
  • The landscape of genomic imprinting across diverse adult human tissues GENOME RESEARCH Baran, Y., Subramaniam, M., Biton, A., Tukiainen, T., Tsang, E. K., Rivas, M. A., Pirinen, M., Gutierrez-Arcelus, M., Smith, K. S., Kukurba, K. R., Zhang, R., Eng, C., Torgerson, D. G., Urbanek, C., Li, J. B., Rodriguez-Santana, J. R., Burchard, E. G., Seibold, M. A., MacArthur, D. G., Montgomery, S. B., Zaitlen, N. A., Lappalainen, T. 2015; 25 (7): 927-936

    Abstract

    Genomic imprinting is an important regulatory mechanism that silences one of the parental copies of a gene. To systematically characterize this phenomenon, we analyze tissue specificity of imprinting from allelic expression data in 1582 primary tissue samples from 178 individuals from the Genotype-Tissue Expression (GTEx) project. We characterize imprinting in 42 genes, including both novel and previously identified genes. Tissue specificity of imprinting is widespread, and gender-specific effects are revealed in a small number of genes in muscle with stronger imprinting in males. IGF2 shows maternal expression in the brain instead of the canonical paternal expression elsewhere. Imprinting appears to have only a subtle impact on tissue-specific expression levels, with genes lacking a systematic expression difference between tissues with imprinted and biallelic expression. In summary, our systematic characterization of imprinting in adult tissues highlights variation in imprinting between genes, individuals, and tissues.

    View details for DOI 10.1101/gr.192278.115

    View details for Web of Science ID 000357356900001

    View details for PubMedID 25953952

    View details for PubMedCentralID PMC4484390

  • Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., Ferreira, P. G., Smith, K. S., Zhang, R., Zhao, F., Banks, E., Poplin, R., Ruderfer, D. M., Purcell, S. M., Tukiainen, T., Minikel, E. V., Stenson, P. D., Cooper, D. N., Huang, K. H., Sullivan, T. J., Nedzel, J., Bustamante, C. D., Li, J. B., Daly, M. J., Guigo, R., Donnelly, P., Ardlie, K., Sammeth, M., Dermitzakis, E. T., McCarthy, M. I., Montgomery, S. B., Lappalainen, T., MacArthur, D. G. 2015; 348 (6235): 666-669

    Abstract

    Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.

    View details for DOI 10.1126/science.1261877

    View details for PubMedID 25954003

    View details for PubMedCentralID PMC4537935

  • Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse. Nature genetics Babak, T., Deveale, B., Tsang, E. K., Zhou, Y., Li, X., Smith, K. S., Kukurba, K. R., Zhang, R., Li, J. B., van der Kooy, D., Montgomery, S. B., Fraser, H. B. 2015; 47 (5): 544-549

    Abstract

    Genomic imprinting is an epigenetic process that restricts gene expression to either the maternally or paternally inherited allele. Many theories have been proposed to explain its evolutionary origin, but understanding has been limited by a paucity of data mapping the breadth and dynamics of imprinting within any organism. We generated an atlas of imprinting spanning 33 mouse and 45 human developmental stages and tissues. Nearly all imprinted genes were imprinted in early development and either retained their parent-of-origin expression in adults or lost it completely. Consistent with an evolutionary signature of parental conflict, imprinted genes were enriched for coexpressed pairs of maternally and paternally expressed genes, showed accelerated expression divergence between human and mouse, and were more highly expressed than their non-imprinted orthologs in other species. Our approach demonstrates a general framework for the discovery of imprinting in any species and sheds light on the causes and consequences of genomic imprinting in mammals.

    View details for DOI 10.1038/ng.3274

    View details for PubMedID 25848752

    View details for PubMedCentralID PMC4414907

  • Mining TCGA data using Boolean implications. PloS one Sinha, S., Tsang, E. K., Zeng, H., Meister, M., Dill, D. L. 2014; 9 (7)

    Abstract

    Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.

    View details for DOI 10.1371/journal.pone.0102119

    View details for PubMedID 25054200

    View details for PubMedCentralID PMC4108374