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Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

Publication ,  Journal Article
Brucker, A; Lu, W; Marceau West, R; Yu, Q-Y; Hsiao, CK; Hsiao, T-H; Lin, C-H; Magnusson, PKE; Sullivan, PF; Szatkiewicz, JP; Lu, T-P; Tzeng, J-Y
Published in: PLoS computational biology
May 2020

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Duke Scholars

Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

May 2020

Volume

16

Issue

5

Start / End Page

e1007797

Related Subject Headings

  • Spatial Analysis
  • Polymorphism, Single Nucleotide
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genome, Human
  • Genetic Variation
  • Genetic Predisposition to Disease
  • DNA Copy Number Variations
  • Computational Biology
 

Citation

APA
Chicago
ICMJE
MLA
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Brucker, A., Lu, W., Marceau West, R., Yu, Q.-Y., Hsiao, C. K., Hsiao, T.-H., … Tzeng, J.-Y. (2020). Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis. PLoS Computational Biology, 16(5), e1007797. https://doi.org/10.1371/journal.pcbi.1007797
Brucker, Amanda, Wenbin Lu, Rachel Marceau West, Qi-You Yu, Chuhsing Kate Hsiao, Tzu-Hung Hsiao, Ching-Heng Lin, et al. “Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.PLoS Computational Biology 16, no. 5 (May 2020): e1007797. https://doi.org/10.1371/journal.pcbi.1007797.
Brucker A, Lu W, Marceau West R, Yu Q-Y, Hsiao CK, Hsiao T-H, et al. Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis. PLoS computational biology. 2020 May;16(5):e1007797.
Brucker, Amanda, et al. “Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.PLoS Computational Biology, vol. 16, no. 5, May 2020, p. e1007797. Epmc, doi:10.1371/journal.pcbi.1007797.
Brucker A, Lu W, Marceau West R, Yu Q-Y, Hsiao CK, Hsiao T-H, Lin C-H, Magnusson PKE, Sullivan PF, Szatkiewicz JP, Lu T-P, Tzeng J-Y. Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis. PLoS computational biology. 2020 May;16(5):e1007797.

Published In

PLoS computational biology

DOI

EISSN

1553-7358

ISSN

1553-734X

Publication Date

May 2020

Volume

16

Issue

5

Start / End Page

e1007797

Related Subject Headings

  • Spatial Analysis
  • Polymorphism, Single Nucleotide
  • Humans
  • Genomics
  • Genome-Wide Association Study
  • Genome, Human
  • Genetic Variation
  • Genetic Predisposition to Disease
  • DNA Copy Number Variations
  • Computational Biology