isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease
Foley, C. N.; Kuncheva, Z.; Marioni, R.; Runz, H.; Sun, B.
Show abstract
Genome-wide association studies (GWAS) have proven a powerful tool for human geneticists to generate biological insights or hypotheses for drug discovery. Nevertheless, a dependency on sensitive individual-level data together with ever-increasing cohort sample sizes, numbers of variants and phenotypes studied put a strain on existing algorithms, limiting the GWAS approach from maximising potential. Here we present in-silico GWAS (isGWAS), a uniquely scalable algorithm to infer regression parameters in case-control GWAS from cohort-level summary data. For any sample size, isGWAS computes a variant-disease association parameter in [~]1 millisecond, or [~]11m variants in UK-Biobank within [~]4 minutes ([~]1500-fold faster than state-of-the-art). Extensive simulations and empirical tests demonstrate that isGWAS results are highly comparable to traditional regression-based approaches. We further introduce a heuristic re-sampling algorithm, leapfrog re-sampler (LRS), to extrapolate association results to semi-virtually enlarged cohorts. Owing to significant computational gains we anticipate a broad use of isGWAS and LRS which are customizable on a web interface.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts 96%
- A new method for multi-ancestry polygenic prediction improves performance across diverse populations 96%
- LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes 96%
Similar papers in this journal
- SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification 97%
- Co-expression-wide association studies link genetically regulated interactions with complex traits 96%
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics 96%
Similar papers in this journal
- A fast non-parametric test of association for multiple traits 95%
- Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits 95%
- Optimizing and benchmarking polygenic risk scores with GWAS summary statistics 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.