Sofia, H. J. | bioRxiv Science

bioRxiv2022

FAVOR: Functional Annotation of Variants Online Resource and Annotator for Variation across the Human Genome

Large-scale whole genome sequencing (WGS) studies and biobanks are rapidly generating a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries or are unable to functionally annotate the genotype data of large WGS studies and biobanks for downstream analysis. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive online multi-faceted portal with summarization and visualization of all possible 9 billion single nucleotide variants (SNVs) across the genome, and allows for rapid variant-, gene-, and region-level online queries. It integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, a scalable annotation tool, FAVORannotator, is provided for functionally annotating and efficiently storing the genotype and variant functional annotation data of a large-scale sequencing study in an annotated GDS file format to facilitate downstream analysis. FAVOR and FAVORannotator are available at https://favor.genohub.org.

genetics↗

bioRxiv2020

Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation

ABSTRACTGenotype imputation is a fundamental step in genomic data analysis such as GWAS, where missing variant genotypes are predicted using the existing genotypes of nearby ‘tag’ variants. Imputation greatly decreases the genotyping cost and provides high-quality estimates of common variant genotypes. As population panels increase, e.g., the TOPMED Project, genotype imputation is becoming more accurate, but it requires high computational power. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. To address this problem, we developed the first fully secure genotype imputation by utilizing ultra-fast homomorphic encryption (HE) techniques that can evaluate millions of imputation models in seconds. In HE-based methods, the genotype data is end-to-end encrypted, i.e., encrypted in transit, at rest, and, most importantly, in analysis, and can be decrypted only by the data owner. We compared secure imputation with three other state-of-the-art non-secure methods under different settings. We found that HE-based methods provide full genetic data security with comparable or slightly lower accuracy. In addition, HE-based methods have time and memory requirements that are comparable and even lower than the non-secure methods. We provide five different implementations and workflows that make use of three cutting-edge HE schemes (BFV, CKKS, TFHE) developed by the top contestants of the iDASH19 Genome Privacy Challenge. Our results provide strong evidence that HE-based methods can practically perform resource-intensive computations for high throughput genetic data analysis. In addition, the publicly available codebases provide a reference for the development of secure genomic data analysis methods.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗