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IntroductionPrivacy protection is a core principle of genomic research but needs further refinement for high-throughput proteomic platforms.MethodsWe identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS) and then calculated genotype probabilities by protein level for each protein-genotype combination (training). Using the most significant 100 proteins, we applied a naïve Bayesian approach to match proteomes to genomes for 2,812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA) with SomaScan 1.3K proteomes and also 2,646 COPDGene subjects with SomaScan 5K proteomes (testing). We tested whether subtracting mean genotype effect for each pQTL SNP would obscure genetic identity.ResultsIn the four testing cohorts, we were able to correctly match 90%-95% their proteomes to their correct genome and for 95%-99% we could match the proteome to the 1% most likely genome. With larger profiling (SomaScan 5K), correct identification was > 99%. The accuracy of matching in subjects with African ancestry was lower (∼60%) unless training included diverse subjects. Mean genotype effect adjustment reduced identification accuracy nearly to random guess.ConclusionLarge proteomic datasets (> 1,000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered deidentified. These findings suggest that large scale proteomic data be given privacy protections of genomic data, or that bioinformatic transformations (such as adjustment for genotype effect) should be applied to obfuscate identity.

BACKGROUNDAsthma is a complex disease with striking disparities across racial and ethnic groups, which may be partly attributable to genetic factors. One of the main goals of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to discover genes conferring risk to asthma in populations of African descent.METHODSWe performed a genome-wide meta-analysis of asthma across 11 CAAPA datasets (4,827 asthma cases and 5,397 controls), genotyped on the African Diaspora Power Chip (ADPC) and including existing GWAS array data. The genotype data were imputed up to a whole genome sequence reference panel from n=880 African ancestry individuals for a total of 61,904,576 SNPs. Statistical models appropriate to each study design were used to test for association, and results were combined using the weighted Z-score method. We also used admixture mapping as a complementary approach to identify loci involved in asthma pathogenesis in subjects of African ancestry.RESULTSSNPs rs787160 and rs17834780 on chromosome 2q22.3 were significantly associated with asthma (p=6.57 × 10−9 and 2.97 × 10−8, respectively). These SNPs lie in the intergenic region between the Rho GTPase Activating Protein 15 (ARHGAP15) and Glycosyltransferase Like Domain Containing 1 (GTDC1) genes. Four low frequency variants on chromosome 1q21.3, which may be involved in the “atopic march” and which are not polymorphic in Europeans, also showed evidence for association with asthma (1.18 ×10−6 ≤ p ≤ 3.06 ×10−6). SNP rs11264909 on chromosome 1q23.1, close to a region previously identified by the EVE asthma meta-analysis as having a putative African ancestry specific effect, only showed differences in counts in subjects homozygous for alleles of African ancestry. Admixture mapping also identified a significantly associated region on chromosome 6q23.2, which includes the Transcription Factor 21 (TCF21) gene, previously shown to be differentially expressed in bronchial tissues of asthmatics and non-asthmatics.CONCLUSIONSWe have identified a number of novel asthma association signals warranting further investigation.