RT Journal
A1 Surakka, Ida
A1 Kristiansson, Kati
A1 Anttila, Verneri
A1 Inouye, Michael
A1 Barnes, Chris
A1 Moutsianas, Loukas
A1 Salomaa, Veikko
A1 Daly, Mark
A1 Palotie, Aarno
A1 Peltonen, Leena
A1 Ripatti, Samuli
T1 Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging
JF Genome Research 
JO Genome Research 
YR 2010 
FD October 01 
VO 20 
IS 10 
SP 1344 
OP 1351 
DO 10.1101/gr.106534.110 
UL http://genome.cshlp.org/content/20/10/1344.abstract 
AB The combining of genome-wide association (GWA) data across populations represents a major challenge for massive global meta-analyses. Genotype imputation using densely genotyped reference samples facilitates the combination of data across different genotyping platforms. HapMap data is typically used as a reference for single nucleotide polymorphism (SNP) imputation and tagging copy number polymorphisms (CNPs). However, the advantage of having population-specific reference panels for founder populations has not been evaluated. We looked at the properties and impact of adding 81 individuals from a founder population to HapMap3 reference data on imputation quality, CNP tagging, and power to detect association in simulations and in an independent cohort of 2138 individuals. The gain in SNP imputation accuracy was highest among low-frequency markers (minor allele frequency [MAF] < 5%), for which adding the population-specific samples to the reference set increased the median R2 between imputed and genotyped SNPs from 0.90 to 0.94. Accuracy also increased in regions with high recombination rates. Similarly, a reference set with population-specific extension facilitated the identification of better tag-SNPs for a subset of CNPs; for 4% of CNPs the R2 between SNP genotypes and CNP intensity in the independent population cohort was at least twice as high as without the extension. We conclude that even a relatively small population-specific reference set yields considerable benefits in SNP imputation, CNP tagging accuracy, and the power to detect associations in founder populations and population isolates in particular.