Chad M. Brummett

This study identifies 26 risk loci for fibromyalgia through a genome-wide association analysis of 2.5 million individuals, revealing genetic links to neural functions and chronic pain disorders.

The Michigan Genomics Initiative links genotypes with electronic clinical records in Michigan Medicine patients, showing higher disease prevalence and validating GWAS findings.

This study identifies 277 T2D loci using diverse populations and improves fine-mapping to assess causal genes and mechanisms, enhancing clinical translation.

This study conducts a meta-analysis of EHR-based lab measurements across two biobanks, identifying novel genetic associations and validating EHR data as a source for GWAS.

Meta-MultiSKAT improves power for region-based genetic association tests by meta-analyzing multiple correlated phenotypes using summary statistics and variance component tests.

This study constructs and evaluates polygenic risk scores for skin cancer subtypes using the Michigan Genomics Initiative and UK Biobank data, and develops a visual catalog to compare PRS methods.

This study identifies 147 genetic loci associated with eGFR, including 53 novel ones, and finds sex-specific effects on kidney function.

The paper identifies loss-of-function variants affecting liver-related blood traits that reduce CVD risk without increasing metabolic disease risk, highlighting potential therapeutic targets.

This study identifies 111 genetic loci and 163 risk variants associated with atrial fibrillation using a GWAS of over 1 million individuals.

This study uses polygenic risk scores (PRS) for cancers in a phenome-wide association study (PheWAS) to identify associations with multiple phenotypes, finding significant links to both cancer and non-cancer diagnoses.

This paper estimates narrow-sense heritability using identity-by-descent information in unrelated individuals and finds that IBD-based methods can capture rare variant contributions but are affected by stratification.

This study identifies two new genetic loci associated with aortic valve stenosis through a large genome-wide association study.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.