hail-is/hail: 0.2.13

Poterba, Tim; cseed,; Bloom, Jon; King, Daniel J.; jigold,; Wang, Arcturus; Schultz, P.; Vittal, Christopher; Goldstein, Daniel E.; alexb,; Aleksey,; maccum,; Francioli, Laurent C.; Karczewski, Konrad J.; Goldsmith, Jack L.; White, Tom; bw,; Kurki, Mitja; Abbott, Liam; Wong, Theodore M.; Berecz, Szabolcs; Compitello, John; Husson, Shane; Baya, Nik; Tong, Damon Da; Colley, Charles; Zamoshchin, Alex; Davidge, Henry; Kien, Dang Trung; Cajes, Brian

doi:10.5281/zenodo.2646680

Cited by 2 publications

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“…Conversely, a study of a small number of rare binary phenotypes would be unlikely to benefit much from the indGWAS approach, as these phenotypes are unlikely to be well-approximated linearly with few features. Additionally, our comparison did not use highly optimized parallel implementations such as SAIGE implemented in Hail [15] or REGENIE in Glow [16]. Our implementation of indGWAS could also be improved, and the majority of its runtime is spent reading and writing GWAS summary statistics from disk.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation of the current study is that our runtime comparisons are certainly imperfect. Our comparison did not use highly optimized parallel implementations such as SAIGE implemented in Hail [11] or REGENIE in Glow [12]. Our implementation of Indirect GWAS could also be improved, and the majority of its runtime is spent reading and writing GWAS summary statistics from disk.…”

Section: Discussionmentioning

confidence: 99%

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Phenotype projections accelerate biobank-scale GWAS

Zietz,

Gisladottir,

Brown

et al. 2023

Preprint

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Understanding the genetic basis of complex disease is a critical research goal due to the immense, worldwide burden of these diseases. Pan-biobank genome-wide association studies (GWAS) provide a powerful resource in complex disease genetics, generating shareable summary statistics on thousands of phenotypes. Biobank-scale GWAS have two notable limitations: they are resource-intensive to compute and do not inform about hand-crafted phenotype definitions, which are often more relevant to study. Here we present Indirect GWAS (indGWAS), a summary-statistic-based method that addresses these limitations. IndGWAS computes GWAS statistics for any phenotype defined as a linear combination of other phenotypes. Our method can reduce runtime by an order of magnitude for large pan-biobank GWAS, and it enables ultra-rapid (less than one minute) GWAS on hand-crafted phenotype definitions using only summary statistics. Overall, this method advances complex disease research by facilitating more accessible and cost-effective genetic studies using large observational data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Phenotype projections accelerate biobank-scale GWAS

Zietz,

Gisladottir,

Brown

et al. 2023

Preprint

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An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

et al. 2020

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Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS followup analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP).This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses.We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 putative causal variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1×10 -4 ). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the putative causal variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics. INTRODUCTIONGenome-wide association studies (GWAS) of biomarkers have been highly successful in identifying novel biological pathways and their impact on health and disease. Biomarkers increase statistical power in GWAS, compared to disease diagnoses, due to their quantitative nature and lack of errors

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hail-is/hail: 0.2.13

Cited by 2 publications

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Phenotype projections accelerate biobank-scale GWAS

Phenotype projections accelerate biobank-scale GWAS

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

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