All Publications

  • Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Knowles, D. A., Ghahramani, Z. 2015; 37 (2): 271-289
  • Relational Learning and Network Modelling Using Infinite Latent Attribute Models IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Palla, K., Knowles, D. A., Ghahramani, Z. 2015; 37 (2): 462-474
  • Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. American journal of human genetics Li, X., Battle, A., Karczewski, K. J., Zappala, Z., Knowles, D. A., Smith, K. S., Kukurba, K. R., Wu, E., Simon, N., Montgomery, S. B. 2014; 95 (3): 245-256


    Recent and rapid human population growth has led to an excess of rare genetic variants that are expected to contribute to an individual's genetic burden of disease risk. To date, much of the focus has been on rare protein-coding variants, for which potential impact can be estimated from the genetic code, but determining the impact of rare noncoding variants has been more challenging. To improve our understanding of such variants, we combined high-quality genome sequencing and RNA sequencing data from a 17-individual, three-generation family to contrast expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) within this family to eQTLs and sQTLs within a population sample. Using this design, we found that eQTLs and sQTLs with large effects in the family were enriched with rare regulatory and splicing variants (minor allele frequency < 0.01). They were also more likely to influence essential genes and genes involved in complex disease. In addition, we tested the capacity of diverse noncoding annotation to predict the impact of rare noncoding variants. We found that distance to the transcription start site, evolutionary constraint, and epigenetic annotation were considerably more informative for predicting the impact of rare variants than for predicting the impact of common variants. These results highlight that rare noncoding variants are important contributors to individual gene-expression profiles and further demonstrate a significant capability for genomic annotation to predict the impact of rare noncoding variants.

    View details for DOI 10.1016/j.ajhg.2014.08.004

    View details for PubMedID 25192044

    View details for PubMedCentralID PMC4157143

  • Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues PLOS GENETICS Kukurba, K. R., Zhang, R., Li, X., Smith, K. S., Knowles, D. A., Tan, M. H., Piskol, R., Lek, M., Snyder, M., MacArthur, D. G., Li, J. B., Montgomery, S. B. 2014; 10 (5)


    Personal exome and genome sequencing provides access to loss-of-function and rare deleterious alleles whose interpretation is expected to provide insight into individual disease burden. However, for each allele, accurate interpretation of its effect will depend on both its penetrance and the trait's expressivity. In this regard, an important factor that can modify the effect of a pathogenic coding allele is its level of expression; a factor which itself characteristically changes across tissues. To better inform the degree to which pathogenic alleles can be modified by expression level across multiple tissues, we have conducted exome, RNA and deep, targeted allele-specific expression (ASE) sequencing in ten tissues obtained from a single individual. By combining such data, we report the impact of rare and common loss-of-function variants on allelic expression exposing stronger allelic bias for rare stop-gain variants and informing the extent to which rare deleterious coding alleles are consistently expressed across tissues. This study demonstrates the potential importance of transcriptome data to the interpretation of pathogenic protein-coding variants.

    View details for DOI 10.1371/journal.pgen.1004304

    View details for Web of Science ID 000337145100010

  • Gene expression changes with age in skin, adipose tissue, blood and brain GENOME BIOLOGY Glass, D., Vinuela, A., Davies, M. N., Ramasamy, A., Parts, L., Knowles, D., Brown, A. A., Hedman, A. K., Small, K. S., Buil, A., Grundberg, E., Nica, A. C., Di Meglio, P., Nestle, F. O., Ryten, M., Durbin, R., McCarthy, M. I., Deloukas, P., Dermitzakis, E. T., Weale, M. E., Bataille, V., Spector, T. D. 2013; 14 (7)
  • Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression BAYESIAN ANALYSIS Salimans, T., Knowles, D. A. 2013; 8 (4): 837-881

    View details for DOI 10.1214/13-BA858

    View details for Web of Science ID 000328267400005

Footer Links:

Stanford Medicine Resources: