A new statistic and its power to infer membership in a genome-wide association study using genotype frequencies.
Aggregate results from genome-wide association studies (GWAS), such as genotype frequencies for cases and controls, were until recently often made available on public websites because they were thought to disclose negligible information concerning an individual's participation in a study. Homer et al. recently suggested that a method for forensic detection of an individual's contribution to an admixed DNA sample could be applied to aggregate GWAS data. Using a likelihood-based statistical framework, we developed an improved statistic that uses genotype frequencies and individual genotypes to infer whether a specific individual or any close relatives participated in the GWAS and, if so, what the participant's phenotype status is. Our statistic compares the logarithm of genotype frequencies, in contrast to that of Homer et al., which is based on differences in either SNP probe intensity or allele frequencies. We derive the theoretical power of our test statistics and explore the empirical performance in scenarios with varying numbers of randomly chosen or top-associated SNPs.
Kevin B
KB
Jacobs
Meredith
M
Yeager
Sholom
S
Wacholder
David
D
Craig
Peter
P
Kraft
David J
DJ
Hunter
Justin
J
Paschal
Teri A
TA
Manolio
Margaret
M
Tucker
Robert N
RN
Hoover
Gilles D
GD
Thomas
Stephen J
SJ
Chanock
Nilanjan
N
Chatterjee
D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012680:Sensitivity and Specificity