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A multivariable approach for risk markers from pooled molecular data with only partial overlap.

Anne-Sophie Stelzer1,2,3,4, Livia Maccioni5, Aslihan Gerhold-Ay6

  • 1Forest Research Institute Baden-Württemberg (FVA), Wonnhaldestraße 4, Freiburg, 79100, Germany. anne-sophie.stelzer@forst.bwl.de.

BMC Medical Genetics
|July 21, 2019
PubMed
Summary

This study introduces a new componentwise boosting method to analyze genetic data from multiple studies with missing information. This approach increases the power of genetic risk score identification, even with incomplete data across studies.

Keywords:
ConsortiumMultivariable modelPartial overlapRegularized regressionSingle nucleotide polymorphism

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Molecular measurements from multiple studies are increasingly pooled for risk score identification.
  • Partial overlap of measurements and missing data across studies pose challenges for multivariable analyses.
  • Existing methods like univariate meta-analysis are common, but powerful multivariable techniques are limited by data availability.

Purpose of the Study:

  • To adapt a regularized regression approach, componentwise boosting, for handling partial overlap in single nucleotide polymorphisms (SNPs).
  • To develop a method for identifying stable sets of SNPs for genetic risk scores using synthesis regression and resampling.
  • To address the challenge of missing data and differential marker availability in pooled molecular studies.

Main Methods:

  • Adapted componentwise boosting for regularized regression with partially overlapping SNPs.
  • Combined synthesis regression with resampling for stable SNP set determination.
  • Utilized stability selection to address statistical significance in the presence of missing data.

Main Results:

  • Demonstrated that componentwise boosting effectively utilizes all available SNP information, regardless of study overlap.
  • Showed increased statistical power in identifying genetic risk scores compared to univariate analyses or complete case analyses.
  • The approach proved effective even when studies with missing data comprised a small proportion of the total individuals.

Conclusions:

  • The proposed componentwise boosting approach offers enhanced power for genetic risk score identification in pooled studies with partial molecular measurement overlap.
  • This method is recommended for general use in situations with missing data or differential marker availability across studies.
  • A software implementation is available for this novel analytical approach.