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.
Jacobs
Kevin B
KB
Yeager
Meredith
M
Wacholder
Sholom
S
Craig
David
D
Kraft
Peter
P
Hunter
David J
DJ
Paschal
Justin
J
Manolio
Teri A
TA
Tucker
Margaret
M
Hoover
Robert N
RN
Thomas
Gilles D
GD
Chanock
Stephen J
SJ
Chatterjee
Nilanjan
N
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