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DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.

Publication ,  Journal Article
Li, X; Pura, J; Allen, A; Owzar, K; Lu, J; Harms, M; Xie, J
Published in: Genet Epidemiol
February 2024

Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, EPG5, harboring possibly pathogenic mutations.

Duke Scholars

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

February 2024

Volume

48

Issue

1

Start / End Page

42 / 55

Location

United States

Related Subject Headings

  • Vesicular Transport Proteins
  • Trees
  • Mutation
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Genetic Variation
  • Genetic Association Studies
  • Epidemiology
  • Autophagy-Related Proteins
 

Citation

APA
Chicago
ICMJE
MLA
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Li, X., Pura, J., Allen, A., Owzar, K., Lu, J., Harms, M., & Xie, J. (2024). DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree. Genet Epidemiol, 48(1), 42–55. https://doi.org/10.1002/gepi.22542
Li, Xuechan, John Pura, Andrew Allen, Kouros Owzar, Jianfeng Lu, Matthew Harms, and Jichun Xie. “DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.Genet Epidemiol 48, no. 1 (February 2024): 42–55. https://doi.org/10.1002/gepi.22542.
Li X, Pura J, Allen A, Owzar K, Lu J, Harms M, et al. DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree. Genet Epidemiol. 2024 Feb;48(1):42–55.
Li, Xuechan, et al. “DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.Genet Epidemiol, vol. 48, no. 1, Feb. 2024, pp. 42–55. Pubmed, doi:10.1002/gepi.22542.
Li X, Pura J, Allen A, Owzar K, Lu J, Harms M, Xie J. DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree. Genet Epidemiol. 2024 Feb;48(1):42–55.
Journal cover image

Published In

Genet Epidemiol

DOI

EISSN

1098-2272

Publication Date

February 2024

Volume

48

Issue

1

Start / End Page

42 / 55

Location

United States

Related Subject Headings

  • Vesicular Transport Proteins
  • Trees
  • Mutation
  • Models, Genetic
  • Humans
  • Genome-Wide Association Study
  • Genetic Variation
  • Genetic Association Studies
  • Epidemiology
  • Autophagy-Related Proteins