Applying compressed sensing to genome-wide association studies

Shashaank Vattikuti, James J. Lee, Christopher C. Chang, Stephen D.H. Hsu, Carson C. Chow

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Background: The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated. Results: Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h2 = 1), there is a sharp phase transition from poor performance to complete selection as the sample size is increased. For heritability below one, complete selection still occurs, but the transition is smoothed. We find for h2 ~ 0.5 that a sample size of approximately thirty times the number of markers with nonzero coefficients is sufficient for full selection. This boundary is only weakly dependent on the number of genotyped markers. Conclusion: Practical measures of signal recovery are robust to linkage disequilibrium between a true causal variant and markers residing in the same genomic region. Given a limited sample size, it is possible to discover a phase transition by increasing the penalization; in this case a subset of the support may be recovered. Applying this approach to the GWAS analysis of height, we show that 70-100% of the selected markers are strongly correlated with height-associated markers identified by the GIANT Consortium.

Original languageEnglish (US)
Article number10
JournalGigaScience
Volume3
Issue number1
DOIs
StatePublished - 2014

Bibliographical note

Funding Information:
We thank Nick Patterson for comments on earlier versions of this work and Phil Schniter for input on the EM-GM-AMP algorithm [52]. This work was supported by the Intramural Program of the NIH, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The authors thank the staff and participants of the ARIC study for their important contributions. Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle).

Keywords

  • Compressed sensing
  • GWAS
  • Genomic selection
  • Lasso
  • Phase transition
  • Sparsity
  • Underdetermined system

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