Identifying individual risk rare variants using protein structure guided local tests (POINT)

Rachel Marceau West, Wenbin Lu, Daniel M. Rotroff, Melaine A. Kuenemann, Sheng Mao Chang, Michael C. Wu, Michael J. Wagner, John B. Buse, Alison A. Motsinger-Reif, Denis Fourches, Jung Ying Tzeng

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.

Original languageEnglish
Article numbere1006722
JournalPLoS Computational Biology
Volume15
Issue number2
DOIs
Publication statusPublished - 2019 Feb 1

Fingerprint

Protein Structure
protein structure
Aggregation
Agglomeration
Kernel Machines
Proteins
Genetic Association
protein
Prioritization
Rare Events
Information Structure
Diabetes
Information Content
Genetic Association Studies
p-Value
Medical problems
Mutant
Clinical Trials
Assign
Annotation

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

West, Rachel Marceau ; Lu, Wenbin ; Rotroff, Daniel M. ; Kuenemann, Melaine A. ; Chang, Sheng Mao ; Wu, Michael C. ; Wagner, Michael J. ; Buse, John B. ; Motsinger-Reif, Alison A. ; Fourches, Denis ; Tzeng, Jung Ying. / Identifying individual risk rare variants using protein structure guided local tests (POINT). In: PLoS Computational Biology. 2019 ; Vol. 15, No. 2.
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West, RM, Lu, W, Rotroff, DM, Kuenemann, MA, Chang, SM, Wu, MC, Wagner, MJ, Buse, JB, Motsinger-Reif, AA, Fourches, D & Tzeng, JY 2019, 'Identifying individual risk rare variants using protein structure guided local tests (POINT)', PLoS Computational Biology, vol. 15, no. 2, e1006722. https://doi.org/10.1371/journal.pcbi.1006722

Identifying individual risk rare variants using protein structure guided local tests (POINT). / West, Rachel Marceau; Lu, Wenbin; Rotroff, Daniel M.; Kuenemann, Melaine A.; Chang, Sheng Mao; Wu, Michael C.; Wagner, Michael J.; Buse, John B.; Motsinger-Reif, Alison A.; Fourches, Denis; Tzeng, Jung Ying.

In: PLoS Computational Biology, Vol. 15, No. 2, e1006722, 01.02.2019.

Research output: Contribution to journalArticle

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