Impact of marker density on the accuracy of association mapping.
We studied the impact of marker density on the accuracy of association mapping using Genetic Analysis Workshop 15 simulated dense single-nucleotide polymorphism (SNP) data on chromosome 6. A total of 1500 cases and 2000 unaffected controls genotyped for 17,820 SNPs were analyzed. We applied the approach that combines information from multiple SNPs under the framework of the Malecot model and composite likelihood to non-overlapping regions of the chromosome. We successfully detected the associations with disease Loci C and D and predicted their locations as small as zero distance to Locus C when it was "typed" and 112 kb from the untyped rare Locus D. Reducing marker density decreased the accuracy of location estimates. However, the predicted locations were robust to variations in the number of SNPs. Generally, the linkage disequilibrium (LD) map reflecting distances between markers in relation to LD produced higher accuracy than the physical map. We also demonstrated that SNP selection based on equal LD distance outperforms that based on equal physical distance or SNP tagging. Furthermore, ignoring rare SNPs diminished the ability to detect rare causal variants.
Zhang
Weihua
W
Section of Cancer Genetics, The Institute of Cancer Research, 15 Cotswold Road, Belmont, Sutton, Surrey SM2 5NG, UK.|Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall, Middlesex, UB1 3HW, UK.
Lau
Winston
W
Human Genetics Division, Duthie Building (Mailpoint 808), Southampton General Hospital, University of Southampton, School of Medicine, Tremona Road, Southampton, SO16 6YD, UK.
Hu
Cheng
C
Shanghai Diabetes Institute, Shanghai Jiaotong University, 600 Yishan Road, Shanghai 200233, People's Republic of China.
Kuo
Tai-Yue
TY
Human Genetics Division, Duthie Building (Mailpoint 808), Southampton General Hospital, University of Southampton, School of Medicine, Tremona Road, Southampton, SO16 6YD, UK.