Using biobin to explore rare variant population stratification

Carrie B. Moore, John R. Wallace, Alex T. Frase, Sarah A. Pendergrass, Marylyn D. Ritchie

Research output: Contribution to journalConference article

14 Scopus citations

Abstract

Rare variants (RVs) will likely explain additional heritability of many common complex diseases; however, the natural frequencies of rare variation across and between human populations are largely unknown. We have developed a powerful, flexible collapsing method called BioBin that utilizes prior biological knowledge using multiple publicly available database sources to direct analyses. Variants can be collapsed according to functional regions, evolutionary conserved regions, regulatory regions, genes, and/or pathways without the need for external files. We conducted an extensive comparison of rare variant burden differences (MAF < 0.03) between two ancestry groups from 1000 Genomes Project data, Yoruba (YRI) and European descent (CEU) individuals. We found that 56.86% of gene bins, 72.73% of intergenic bins, 69.45% of pathway bins, 32.36% of ORegAnno annotated bins, and 9.10% of evolutionary conserved regions (shared with primates) have statistically significant differences in RV burden. Ongoing efforts include examining additional regional characteristics using regulatory regions and protein binding domains. Our results show interesting variant differences between two ancestral populations and demonstrate that population stratification is a pervasive concern for sequence analyses.

Original languageEnglish (US)
Pages (from-to)332-343
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - Jan 1 2013
Event18th Pacific Symposium on Biocomputing, PSB 2013 - Kohala Coast, United States
Duration: Jan 3 2013Jan 7 2013

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computational Theory and Mathematics

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    Moore, C. B., Wallace, J. R., Frase, A. T., Pendergrass, S. A., & Ritchie, M. D. (2013). Using biobin to explore rare variant population stratification. Pacific Symposium on Biocomputing, 332-343.