Accounting for linkage disequilibrium in genome-wide association studies: A penalized regression method

Jin Liu, Kai Wang, Shuangge Ma, Jian Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Penalized regression methods are becoming increasingly popular in genome-wide association studies (GWAS) for identifying genetic markers associated with disease. However, standard penalized methods such as LASSO do not take into account the possible linkage disequilibrium between adjacent markers. We propose a novel penalized approach for GWAS using a dense set of single nucleotide polymorphisms (SNPs). The proposed method uses the minimax concave penalty (MCP) for marker selection and incorporates linkage disequilibrium (LD) information by penalizing the difference of the genetic effects at adjacent SNPs with high correlation. A coordinate descent algorithm is derived to implement the proposed method. This algorithm is efficient in dealing with a large number of SNPs. A multi-split method is used to calculate the p-values of the selected SNPs for assessing their significance. We refer to the proposed penalty function as the smoothed MCP and the proposed approach as the SMCP method. Performance of the proposed SMCP method and its comparison with LASSO and MCP approaches are evaluated through simulation studies, which demonstrate that the proposed method is more accurate in selecting associated SNPs. Its applicability to real data is illustrated using heterogeneous stock mice data and a rheumatoid arthritis.
Original languageEnglish
Pages (from-to)99-115
Number of pages17
JournalStatistics and its Interface
Volume6
Issue number1
Publication statusPublished - 23 Apr 2013
Externally publishedYes

Keywords

  • Feature selection
  • Genetic association
  • Linkage disequilibrium
  • Penalized regression
  • Single nucleotide polymorphism

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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