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On combining family- and population-based sequencing data.

Yuriko Katsumata1, David W Fardo1

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Combining population and family sequencing data using family-based sequence kernel association test (famSKAT) can effectively detect gene-phenotype associations. However, sample imbalance can reduce power, highlighting the need for careful study design.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Statistical methods are crucial for detecting gene effects in population- and family-based studies.
  • Unified tests combining these designs for gene-phenotype associations are not well-established.

Purpose of the Study:

  • To investigate efficient combination of population- and family-based sequencing data.
  • To evaluate best practices for combining genetic data using the GAW19 dataset.
  • To assess the performance of family-based sequence kernel association test (famSKAT) in combined datasets.

Main Methods:

  • Utilized the Genetic Analysis Workshop 19 (GAW19) dataset.
  • Examined overlapping variants from whole genome and whole exome sequencing.
  • Applied famSKAT to analyze family-based, population-based, and combined datasets.
  • Compared famSKAT results with meta-analysis.

Main Results:

  • famSKAT demonstrated high power in combined data for detecting associations with blood pressure genes (e.g., MAP4, TNN, CGN) with large effect sizes.
  • Combined famSKAT showed reduced power when family-based data power significantly differed from population-based data.
  • Sample imbalance in combined data influenced the famSKAT test statistic, diluting the signal.
  • Observed inflated Type I errors in simulations, potentially due to inadequate control for population admixture.

Conclusions:

  • famSKAT is powerful for combined genetic data analysis, especially with large effect variants.
  • Study design is critical; significant sample size disparities can negatively impact combined analysis power.
  • Further research is needed to address inflated Type I errors in population-based analyses with admixture.