Outlier detection and false discovery rates for whole-genome DNA matching

Jung Ying Tzeng, William Byerley, B. Devlin, Kathryn Roeder, Larry Wasserman

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


We define a statistic, called the matching statistic, for locating regions of the genome that exhibit excess similarity among cases when compared to controls. Such regions are reasonable candidates for harboring disease genes. We find the asymptotic distribution of the statistic while accounting for correlations among sampled individuals. We then use the Benjamini and Hochberg false discovery rate (FDR) method for multiple hypothesis testing to find regions of excess sharing. The p values for each region involve estimated nuisance parameters. Under appropriate conditions, we show that the FDR method based on p values and with estimated nuisance parameters asymptotically preserves the FDR property. Finally, we apply the method to a pilot study on schizophrenia.

Original languageEnglish
Pages (from-to)236-246
Number of pages11
JournalJournal of the American Statistical Association
Issue number461
Publication statusPublished - 2003 Mar

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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