David A. Cesarini

The paper introduces GRMA, a method that improves genetic association analyses by reducing bias and expanding population coverage compared to GWAS.

The paper updates a polygenic index repository with more phenotypes, datasets, and improved causal inference methods, enhancing predictive performance and interpretability.

Family-based GWAS meta-analysis of 34 phenotypes shows standard GWAS effects carry sizeable confounding uncorrelated with direct genetic effects, while assortative mating likely inflates SNP heritability and genetic correlations.

This study identifies 43 genomic loci associated with reproductive success in 785,604 individuals, showing ongoing natural selection at the FADS locus.

The paper develops a method to impute parental genotypes from sibling data to estimate direct and indirect genetic effects, finding that standard GWAS underestimates direct effects for several traits.

The paper constructs polygenic indexes for 47 phenotypes across 11 datasets and provides a tool to correct errors-in-variables bias when using PGIs as proxies for the additive SNP factor.

This paper introduces MAMA, a method for multi-ancestry GWAS meta-analysis, which identifies novel genetic associations and improves power across populations.

This study identifies genetic loci associated with macronutrient intake and links them to health outcomes using GWAS in over 235,000 individuals.

GWAS in over one million individuals identify 611 loci across risk tolerance and risky behaviors, revealing shared genetic architecture and implicating glutamatergic and GABAergic neurotransmission.

The paper quantifies assortative mating on complex traits by analyzing SNP data from 400,000 individuals, finding significant evidence for height and educational attainment.

MTAG improves GWAS results by combining summary statistics across traits, increasing significant loci and enhancing polygenic score variance.