Grant Goldman

TL;DR: This paper conducted a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identified 3,952 approximately uncorrelated single-nucleotide polymorphisms (SNPs).

TL;DR: A theoretical framework is presented to help interpret analyses involving PGIs and it is revealed that a PGI can be understood as an unbiased but noisy measure of a latent variable the authors call the “additive SNP factor.”

TL;DR: In this article, the additive SNP factor was used as a proxy for the PGI to correct the errors-in-variables bias in the regression model, and an estimator that corrects the bias was presented.

TL;DR: Multi-Ancestry Meta-Analysis (MAMA) as mentioned in this paper combines genome-wide association study (GWAS) summary statistics from multiple populations to produce new summary statistics for each population, identifying novel loci that would not have been discovered in either set of GWAS summary statistics alone.

TL;DR: ESM1b was able to distinguish between pathogenic and benign variants across ∼150K variants annotated in ClinVar and HGMD, outperforming existing state-of-the-art methods and exceeded the state of the art at predicting the experimental results of deep mutational scans.