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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Association analysis of rare and common variants with multiple traits based on variable reduction method.

Lili Chen1, Yong Wang1, Yajing Zhou2

  • 1Department of Mathematics,School of Science,Harbin Institute of Technology,Harbin 150001,China.

Genetics Research
|February 2, 2018
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Summary

This study introduces novel methods, MULVR and MULVR-O, for analyzing common and rare genetic variants associated with multiple complex disease traits. MULVR-O enhances power by optimizing trait selection, outperforming existing approaches in simulations and real data applications.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Genomic analysis

Background:

  • Pleiotropy, where one genetic variant affects multiple traits, is common in complex diseases.
  • Existing methods often focus on single variants (common or rare), but complex diseases involve both.
  • Joint analysis of multiple traits increases power for detecting genetic variants and understanding genetic mechanisms.

Purpose of the Study:

  • To develop a region-based method (MULVR) for testing associations of both rare and common variants with multiple traits.
  • To propose an improved method (MULVR-O) that optimizes trait selection to mitigate the impact of noise traits and enhance statistical power.
  • To evaluate the performance of MULVR-O against existing methods using simulations and real-world genetic data.

Main Methods:

  • Developed a variable reduction-based region method (MULVR) to jointly analyze multiple quantitative and qualitative traits for common and rare variants.
  • Introduced MULVR-O, which adaptively selects the optimal subset of traits to analyze, improving robustness against noise.
  • Conducted extensive simulation studies and applied the methods to the GAW19 dataset.

Main Results:

  • MULVR-O demonstrated superior power compared to several existing methods across various scenarios.
  • The proposed methods are applicable to both quantitative and qualitative traits.
  • Application to SHBG and CHRM3 genes with blood pressure phenotypes in GAW19 confirmed feasibility and efficiency.

Conclusions:

  • The proposed MULVR and MULVR-O methods offer a powerful and efficient approach for region-based analysis of multiple traits, considering both common and rare variants.
  • MULVR-O effectively addresses the challenge of noise traits, providing a more robust analysis.
  • These methods advance the joint analysis of genetic variants and complex disease phenotypes.