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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Distance-based phenotypic association analysis of DNA sequence data.

Doyoung Chung1, Qunyuan Zhang, Aldi T Kraja

  • 1Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St, Louis, MO 63110, USA. doyoung.chung@wustl.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary

Multivariate distance matrix regression (MDMR) effectively detects causative genes using DNA sequence data. This study found MDMR performs comparably well with Euclidean distance, even for rare variants.

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Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Decreasing DNA sequencing costs drive demand for comprehensive genetic association tests.
  • Multivariate distance matrix regression (MDMR) is an association test utilizing extensive DNA sequence information.

Purpose of the Study:

  • To explore features of MDMR using Genetic Analysis Workshop 17 simulated data.
  • To investigate potential improvements in distance measures for MDMR.
  • To evaluate MDMR's performance in detecting causative genes and its false-positive rate.

Main Methods:

  • Utilized genotype data from 697 unrelated individuals across 200 replications.
  • Applied MDMR with the Euclidean distance metric to detect 13 trait-associated genes.
  • Estimated the false-positive rate using 508 control genes.
  • Compared MDMR performance against Mantel's test and collapsing analysis for rare variants.

Main Results:

  • MDMR demonstrated comparable performance even when using the Euclidean distance metric.
  • The study assessed MDMR's power in identifying causative genes and its accuracy in controlling false positives.

Conclusions:

  • MDMR is a viable association test for DNA sequence data.
  • The Euclidean distance measure is effective within the MDMR framework, including for rare variants.