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An adaptive gene-level association test for pedigree data.

Jun Young Park1, Chong Wu1, Wei Pan2

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We developed a computationally efficient gene-level association test using linear mixed models (LMMs) to analyze genetic data from pedigrees. This method effectively identified potential genes associated with high-density lipoprotein (HDL) ratios in genome-wide scans.

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GWASHDLLinear mixed modelsScore testaSPU

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genetic association studies are crucial for understanding disease etiology.
  • Accounting for relatedness and population structure in genetic data is essential for accurate association testing.
  • Existing gene-level tests may lack efficiency or adaptability in complex pedigree datasets.

Purpose of the Study:

  • To develop a novel, computationally efficient gene-level association test.
  • To incorporate individual relatedness and population structures within a linear mixed model (LMM) framework.
  • To identify genes associated with the high-density lipoprotein (HDL) ratio using GAW20 data.

Main Methods:

  • Proposed a data-adaptive gene-level association test.
  • Combined results from a class of score-based tests.
  • Utilized a single null model fitting for genome-wide analysis within LMMs.
  • Applied the method to GAW20 data for HDL ratio association testing.

Main Results:

  • Identified two nearly significant genes (APOA5 and ZNF259) near rs964184 associated with HDL ratio.
  • The proposed method outperformed standard single-nucleotide polymorphism (SNP) and other gene-level tests in genome-wide scans.
  • Demonstrated the utility of LMMs in gene-level association studies with pedigree data.

Conclusions:

  • Gene-level association testing offers a complementary approach to SNP-level analysis.
  • The developed method is adaptive and computationally efficient compared to existing gene-level tests.
  • The findings highlight the potential of APOA5 and ZNF259 in HDL ratio regulation.