Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

This cross-sectional study assesses potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence and estimating the prevalence of potentially actionable prescribing decisions.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene‐drug pairs as “level A,” for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)‐affiliated health systems across the US. Inpatient and/or outpatient electronic‐prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658–15,781) in 2011 to 17,335 (CI: 17,283–17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632–7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype‐guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.

Response to medications, the principal treatment modality for acute and chronic diseases, is highly variable, with 40–70% of patients exhibiting lack of efficacy or adverse drug reactions. With ~ 15–30% of this variability explained by genetic variants, pharmacogenomics has become a valuable tool in our armamentarium for optimizing treatments and is poised to play an increasing role in clinical care. This review presents the progress made toward elucidating genetic underpinnings of drug response including discovery of race/ancestry‐specific pharmacogenetic variants and discusses the current evidence and evidence framework for actionability. The review is framed in the context of changing demographics and evolving views related to race and ancestry. Finally, it highlights the vital role played by cohort studies in elucidating genetic differences in drug response across race and ancestry and the informal collaborations that have enabled the field to bridge the “bench to bedside” translational gap.

BACKGROUND A growing body of evidence indicates a positive correlation between expression of human antimicrobial peptide leucin leucin 37 (LL-37) and progression of epithelial cancers, including prostate cancer (PCa). Although the molecular mechanisms for this correlation has not yet been elucidated, the primary function of LL-37 as a chemotactic molecule for innate immune effector cells suggests its possible association in coordinating protumorigenic mechanisms, mediated by tumor-infiltrating immune cells. METHODS To investigate protumorigenic role(s) of cathelicidin-related antimicrobial peptide (CRAMP), a murine orthologue of LL-37, the present study compared tumor growth kinetics between mouse PCa cell lines with and without CRAMP expression (TRAMP-C1 and TRAMP-C1CRAMP-sh, respectively) in immunocompetent mice. CRAMP-mediated chemotaxis of different innate immune cell types to the tumor microenvironment (TME). The role of CRAMP in differentiation and polarization of immature myeloid progenitors (IMPs) to protumorigenic type 2 macrophages (M2) in TME was determined by adoptive transfer of IMPs into mice bearing CRAMP(+) and CRAMP(−) tumors. To differentiate protumorigenic events mediated by tumor-derived CRAMP from host immune cell-derived CRAMP, tumor challenge study was performed in CRAMP-deficient mice. To identify mechanisms CRAMP function, macrophage colony stimulating factor (M-CSF) and monocyte chemoattractant protein 1 (MCP-1) gene expression was analyzed by QRT-PCR and STAT3 signaling was determined by immunoblotting. RESULTS Significantly delayed tumor growth was observed in wild-type (WT) mice implanted with TRAMP-C1CRAMP-sh cells compared to mice implanted with TRAMP-C1 cells. CRAMP(+) TME induced increased number of IMP differentiation into protumorigenic M2 macrophages compared to CRAMP(−) TME, indicating tumor-derived CRAMP facilitates differentiation and polarization of IMPs toward M2. Tumor challenge study in CRAMP deficient mice showed comparable tumor growth kinetics with WT mice, suggesting tumor-derived CRAMP plays a crucial role in PCa progression. In vitro study demonstrated that overexpressed M-CSF and MCP-1 in TRAMP-C1 cells through CRAMP-mediated autocrine signaling, involving p65, regulates IMP-to-M2 differentiation/polarization through STAT3 activation. CONCLUSION Altogether, the present study suggests that overexpressed CRAMP in prostate tumor initially chemoattracts IMPs to TME and mediates differentiation and polarization of early myeloid progenitors into protumorigenic M2 macrophages during PCa progression. Thus, selective downregulation of CRAMP in tumor cells in situ may benefit overcoming immunosuppressive mechanisms in PCa.