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Updated: Jun 23, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

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Published on: June 23, 2012

Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies.

Yi-Hau Chen1, Nilanjan Chatterjee, Raymond J Carroll

  • 1Yi-Hau Chen is Associate Research Member with the Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China (E-mail: yhchen@stat.sinica.edu.tw ). Nilanjan Chatterjee is Chief and Principle Investigator with the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Department of Health and Human Services, Rockville Maryland 20852 (E-mail: chattern@mail.nih.gov ). Raymond J. Carroll is Distinguished Professor with the Department of Statistics, Texas A&M University, College Station, Texas 77843-3143 (E-mail: carroll@stat.tamu.edu ).

Journal of the American Statistical Association
|May 12, 2009
PubMed
Summary

This study introduces robust statistical methods for estimating genetic haplotype effects and gene-environment interactions in case-control studies. The new approach improves accuracy by relaxing strict population assumptions, enhancing disease risk analysis.

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Last Updated: Jun 23, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical Genetics
  • Epidemiology

Background:

  • Case-control studies are crucial for investigating genetic factors and gene-environment interactions in disease etiology.
  • Haplotypes, combinations of alleles at linked loci, are key to understanding complex genetic influences.
  • Existing methods often rely on assumptions like Hardy-Weinberg Equilibrium and gene-environment independence, limiting their applicability.

Purpose of the Study:

  • To develop robust and efficient statistical methods for estimating disease odds ratios associated with haplotypes.
  • To estimate parameters for haplotype-environment interactions, accounting for potential deviations from standard assumptions.
  • To enhance the analysis of case-control data by incorporating shrinkage estimation techniques.

Main Methods:

  • Development of a novel retrospective approach for case-control data analysis, robust to gene-environment distribution.
  • Application of shrinkage estimation, using empirical Bayes and penalized regression, to refine parameter estimates.
  • Proposed methods for variance estimation based on asymptotic theories.

Main Results:

  • The proposed methods demonstrate robustness to variations in gene-environment distributions.
  • Shrinkage techniques improve the efficiency and precision of haplotype and interaction effect estimations.
  • Simulations and data examples validate the performance of the developed statistical approaches.

Conclusions:

  • The novel retrospective and shrinkage methods offer a more flexible and accurate way to analyze genetic data in case-control studies.
  • These techniques relax restrictive assumptions, leading to more reliable estimates of disease odds ratios for haplotypes and their interactions with environmental factors.
  • The findings contribute to advancing statistical methodologies in genetic epidemiology.