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A comparison of two collapsing methods in different approaches.

Carmen Dering1, Arne Schillert1, Inke R König1

  • 1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, Haus. 24, 23562 Lübeck, Germany.

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Summary

Two gene collapsing methods for rare variant analysis showed poor performance. One method had high false positives, while the other had low power, suggesting improved filtering or functional data inclusion is needed.

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics
  • Genetic Epidemiology

Background:

  • Next-generation sequencing allows for parallel investigation of whole genomes.
  • Collapsing rare variants within genes can enhance signals for genetic association studies.
  • Understanding the performance of collapsing methods is crucial for disease gene discovery.

Purpose of the Study:

  • To evaluate the Type I error rate and statistical power of two rare variant collapsing methods.
  • To assess the reliability of the combined multivariate and collapsing method and the functional principal component analysis (FPCA)-based statistic.
  • To identify potential improvements for rare variant association analysis.

Main Methods:

  • Utilized case-control data from the Genetic Analysis Workshop 18 (GAW18).
  • Applied a combined multivariate and collapsing method, grouping variants with minor allele frequency (MAF) ≤ 0.05 per gene.
  • Applied a functional principal component analysis (FPCA)-based statistic for collapsing rare variants.

Main Results:

  • Neither collapsing method reliably detected truly associated genes.
  • The combined multivariate and collapsing method showed a high false-positive rate (75%) despite identifying one gene (power=0.66).
  • The FPCA-based statistic maintained Type I error control but exhibited low statistical power.

Conclusions:

  • Current collapsing methods demonstrate limitations in accurately identifying disease-associated genes.
  • Stricter filtering of variants by minor allele frequency may improve method performance.
  • Incorporating variant functionality information could enhance the effectiveness of collapsing strategies.