An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.
BACKGROUND
Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers.


RESULTS
In this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10 -5 for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers.


CONCLUSIONS
Our results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.
Laboratory of Genetics and Molecular Cardiology, Heart Institute, InCor, Hospital das Clínicas, FMUSP- Universidade de São Paulo, São Paulo, Brazil.
Marcio A A
MA
Almeida
Paulo S L
PS
Oliveira
Tiago V
TV
Pereira
José E
JE
Krieger
Alexandre C
AC
Pereira
D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005189:False Positive Reactions; D005787:Gene Frequency; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D013223:Statistics as Topic
D005819:Genetic Markers
PLoS Genet. 2007 Jul;3(7):e114
17676998
BMC Genet. 2008 Dec 12;9:85
19077279
PLoS One. 2008;3(10):e3551
18958166
Am J Hum Genet. 2008 Jul;83(1):112-9
18589396
Nat Genet. 2006 Jun;38(6):659-62
16715099
Hum Genet. 2007 Dec;122(5):495-504
17851696
Nat Rev Genet. 2006 Oct;7(10):781-91
16983374
Bioinformatics. 2007 Mar 1;23(5):644-5
17267436
Hum Genet. 2009 Mar;125(2):163-71
19089453
Nature. 2007 Jun 7;447(7145):661-78
17554300
Hum Mol Genet. 2008 Oct 15;17(R2):R122-8
18852200
Nat Genet. 2009 Jun;41(6):666-76
19430483
Nat Genet. 2007 Jul;39(7):906-13
17572673
Curr Genomics. 2009 Apr;10(2):110-8
19794883