Format

Send to

Choose Destination
Ann Hum Genet. 2008 Jul;72(Pt 4):547-56. doi: 10.1111/j.1469-1809.2008.00434.x. Epub 2008 Mar 18.

Investigation into the ability of SNP chipsets and microsatellites to detect association with a disease locus.

Author information

1
Centre for Psychiatry, Queen Mary's School of Medicine and Dentistry, London E1 1BB, UK. david.curtis@qmul.ac.uk

Abstract

We wished to investigate the ability of different SNP chipsets to detect association with a disease and to investigate the linkage disequilibrium (LD) relationships between microsatellites and nearby SNPs in order to assess their potential usefulness to detect association. SNP genotypes were obtained from HapMap and microsatellite genotypes from CEPH. 5000 SNPs were simulated as disease genes which increased penetrance from 0.01 to 0.02 in a sample of 400 cases and 400 controls. The power of flanking SNPs to detect association was tested using sets of 1, 2, 3 or 4 markers analysed with haplotype analysis or logistic regression and using either all HapMap markers or those from the Affymetrix 500K, Illumina 300K or Illumina 550K chipsets. Additionally, LD relationships between 10 microsatellites and SNPs within 2Mb of each other were studied. The power for one of the markers to detect association at p = 0.001 was around 0.4. Power was slightly better for logistic regression than haplotype analysis and for two-marker as opposed to single marker analysis but analysing with larger numbers markers had little benefit. The Illumina 550K marker set was better able to detect association than the other two and was almost as powerful as using all HapMap markers. Microsatellites had detectable LD with only a small number of nearby SNPs and the pattern of LD was very variable. Available chipsets have quite good ability to detect association although obviously results will be critically dependent on the nature of the genetic effect on risk, sample size and the actual LD relationships of the susceptibility polymorphisms involved. Microsatellites seem ill-suited for systematic studies to detect association.

PMID:
18355389
PMCID:
PMC2592259
DOI:
10.1111/j.1469-1809.2008.00434.x
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center