isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease

Foley, C. N.; Kuncheva, Z.; Marioni, R.; Runz, H.; Sun, B.

doi:10.1101/2023.07.21.550074

Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.07.21.550074

isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease

Foley, C. N.·Kuncheva, Z.·Marioni, R.·Runz, H.·Sun, B.

View original source ↗Export metadata ↓

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.

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Foley, C. N., Kuncheva, Z., Marioni, R., Runz, H., Sun, B.. 2023-07-25. isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease. https://doi.org/10.1101/2023.07.21.550074

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Generation of a transgenic cephalopod

Coleoid cephalopods (cuttlefish, octopus, and squid) are marine mollusks with elaborate nervous systems that support a diverse repertoire of complex behaviors. These include the neural control of the color, pattern, and texture of the skin, facilitating both adaptive camouflage and innate patterning that may reflect internal state. The development of transgenic cephalopods expressing fluorescent proteins, optogenetic actuators, and reporters of neural activity would contribute a new and important technology to cephalopod biology. The generation of transgenic cephalopods, however, has remained a major challenge. Here, we report the development of stable transgenic dwarf cuttlefish (Ascarosepion bandense) expressing ubiquitous nuclear-localized mScarlet, a red fluorescent protein. We evaluated multiple strategies for transgenesis, and established cuttlefish lines using both CRISPR and the transposons Sleeping Beauty and Minos. The stable expression of transgenes enabled live imaging of cell dynamics during embryonic development. The Minos transposon emerged as the most efficient transgenesis strategy and is adaptable to promoters and transgenes of choice. These strategies now enable the generation of diverse genetic tools for mechanistic studies of cephalopod biology.

genetics↗

bioRxiv2026

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗

bioRxiv2026

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗