Expanding the genetic landscape of endometriosis: Integrative -omics analyses uncover key pathways from a multi-ancestry study of over 900,000 women.

We report the findings of a genome-wide association study (GWAS) meta-analysis of endometriosis consisting of a large portion (31%) of non-European samples across 14 biobanks worldwide as part of the Global Biobank Meta-Analysis Initiative (GBMI). We identified 45 significant loci using a wide phenotype definition, seven of which are previously unreported and detected first genome-wide significant locus ( ) among only African-ancestry. Our narrow phenotypes and surgically confirmed case definitions for endometriosis analyses replicated the known loci near , , and .

An Integrative Migraine Polygenic Risk Score Is Associated with Age at Onset But Not Chronification.

: Genome-wide association studies (GWASs) demonstrate a complex genetic landscape for migraine risk. Migraine polygenic risk scores (PRSs) developed from GWAS data may have utility for predicting disease course. We analyzed the strength of association between an integrative migraine PRS and age at onset and chronification.

Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system.

Atypical diabetes with overlapping clinical features of type 1 (T1D) and type 2 (T2D) is common and challenging diagnostically and for implementing effective treatment. Here, we validate a recently reported genetic probability of type 1 diabetes (GenProb-T1D) from the UK Biobank (UKB) for differentiating type 1 diabetes and type 2 diabetes in a diabetes patient cohort from a healthcare system-based biobank in the USA. Among 3,363 diabetes patients, we confirmed the performance of GenProb-T1D in differentiating typical type 1 diabetes vs type 2 diabetes.

Protein prediction for trait mapping in diverse populations.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations.

Genetically regulated expression underlies cellular sensitivity to chemotherapy in diverse populations.

Most cancer chemotherapeutic agents are ineffective in a subset of patients; thus, it is important to consider the role of genetic variation in drug response. Lymphoblastoid cell lines (LCLs) in 1000 Genomes Project populations of diverse ancestries are a useful model for determining how genetic factors impact the variation in cytotoxicity. In our study, LCLs from three 1000 Genomes Project populations of diverse ancestries were previously treated with increasing concentrations of eight chemotherapeutic drugs, and cell growth inhibition was measured at each dose with half-maximal inhibitory concentration (IC50) or area under the dose-response curve (AUC) as our phenotype for each drug.