title: MetaSeq: privacy preserving meta-analysis of sequencing-based association studies.
abstract: Human genetics recently transitioned from GWAS to studies based on NGS data. For GWAS, small effects dictated large sample sizes, typically made possible through meta-analysis by exchanging summary statistics across consortia. NGS studies groupwise-test for association of multiple potentially-causal alleles along each gene. They are subject to similar power constraints and therefore likely to resort to meta-analysis as well. The problem arises when considering privacy of the genetic information during the data-exchange process. Many scoring schemes for NGS association rely on the frequency of each variant thus requiring the exchange of identity of the sequenced variant. As such variants are often rare, potentially revealing the identity of their carriers and jeopardizing privacy. We have thus developed MetaSeq, a protocol for meta-analysis of genome-wide sequencing data by multiple collaborating parties, scoring association for rare variants pooled per gene across all parties. We tackle the challenge of tallying frequency counts of rare, sequenced alleles, for metaanalysis of sequencing data without disclosing the allele identity and counts, thereby protecting sample identity. This apparent paradoxical exchange of information is achieved through cryptographic means. The key idea is that parties encrypt identity of genes and variants. When they transfer information about frequency counts in cases and controls, the exchanged data does not convey the identity of a mutation and therefore does not expose carrier identity. The exchange relies on a 3rd party, trusted to follow the protocol although not trusted to learn about the raw data. We show applicability of this method to publicly available exome-sequencing data from multiple studies, simulating phenotypic information for powerful meta-analysis. The MetaSeq software is publicly available as open source.
authors: Department of Computer Science, Columbia University, New York, New York 10027-7003, USA. aps2157@columbia.edu
authors: Angad Pal
authors: AP
authors: Singh
authors: Samreen
authors: S
authors: Zafer
authors: Itsik
authors: I
authors: Pe'er
mesh_terms: D019295:Computational Biology; D016494:Computer Security / Q000706:statistics & numerical data; D003198:Computer Simulation; D005787:Gene Frequency; D030661:Genetic Privacy; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D015201:Meta-Analysis as Topic; D017421:Sequence Analysis / Q000706:statistics & numerical data; D012984:Software