Genetic ancestry and population differences in levels of inflammatory cytokines in women: Role for evolutionary selection and environmental factors.

Selection pressure due to exposure to infectious pathogens endemic to Africa may explain distinct genetic variations in immune response genes. However, the impact of those genetic variations on human immunity remains understudied, especially within the context of modern lifestyles and living environments, which are drastically different from early humans in sub Saharan Africa. There are few data on population differences in constitutional immune environment, where genetic ancestry and environment are likely two primary sources of variation. In a study integrating genetic, molecular and epidemiologic data, we examined population differences in plasma levels of 14 cytokines involved in innate and adaptive immunity, including those implicated in chronic inflammation, and possible contributing factors to such differences, in 914 AA and 855 EA women. We observed significant differences in 7 cytokines, including higher plasma levels of CCL2, CCL11, IL4 and IL10 in EAs and higher levels of IL1RA and IFNα2 in AAs. Analyses of a wide range of demographic and lifestyle factors showed significant impact, with age, education level, obesity, smoking, and alcohol intake, accounting for some, but not all, observed population differences for the cytokines examined. Levels of two pro-inflammatory chemokines, CCL2 and CCL11, were strongly associated with percent of African ancestry among AAs. Through admixture mapping, the signal was pinpointed to local ancestry at 1q23, with fine-mapping analysis refined to the Duffy-null allele of rs2814778. In AA women, this variant was a major determinant of systemic levels of CCL2 (p = 1.1e-58) and CCL11 (p = 2.2e-110), accounting for 19% and 40% of the phenotypic variance, respectively. Our data reveal strong ancestral footprints in inflammatory chemokine regulation. The Duffy-null allele may indicate a loss of the buffering function for chemokine levels. The substantial immune differences by ancestry may have broad implications to health disparities between AA and EA populations.

Targeting CD38 with daratumumab is lethal to Waldenström macroglobulinaemia cells.

CD38 is expressed on Waldenström macroglobulinaemia (WM) cells, but its role as a therapeutic target remains undefined. With recent approval of the anti-CD38 monoclonal antibody, daratumumab (Dara), we hypothesized that blocking CD38 would be lethal to WM cells. In vitro Dara treatment of WM cells (including ibrutinib-resistant lines) elicited antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), antibody-dependent cell phagocytosis (ADCP) and direct apoptosis. In vivo, Dara treatment was well tolerated and delayed tumour growth in RPCI-WM1-xenografted mice. CD38 is reported to augment B-cell receptor (BCR) signalling; we noted that Dara significantly attenuated phosphorylated SYK, LYN, BTK, PLCγ2, ERK1/2, AKT, mTOR, and S6 levels, and this effect was augmented by cotreatment with ibrutinib. Indeed, WM cells, including ibrutinib-resistant WM cell lines treated with the ibrutinib + Dara combination, showed significantly more cell death through ADCC, CDC, ADCP and apoptosis relative to single-agent Dara or ibrutinib. In summary, we are the first to report the in vitro and in vivo anti-WM activity of Dara. Furthermore, we show a close connection between BCR and CD38 signalling, which can be co-targeted with ibrutinib + Dara to induce marked WM cell death, irrespective of acquired resistance to ibrutinib.

COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults.

OBJECTIVES: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. DESIGN: A cross-sectional study. SETTING: AncestryDNA customers in the USA who consented to research. PARTICIPANTS: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. RESULTS: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study. CONCLUSIONS: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects.

Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four 'expanded' phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four 'expanded' COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification.

Genome-wide analysis provides genetic evidence that ACE2 influences COVID-19 risk and yields risk scores associated with severe disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2-2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10-8) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10-13), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.

Genetic variations, reproductive aging, and breast cancer risk in African American and European American women: The Women's Circle of Health Study.

Reproductive aging phenotypes, including age at menarche (AM) and age at natural menopause (ANM), are well-established risk factors for breast cancer. In recent years, many genetic variants have been identified in association with AM and ANM in genome-wide association studies among European populations. Using data from the Women's Circle of Health Study (WCHS) of 1,307 European-American (EA) and 1,365 African-American (AA) breast cancer cases and controls, we aimed to replicate 53 earlier GWAS variants for AM and ANM in AA and EA groups and to perform analyses on total and net reproductive lifespan (TRLS; NRLS). Breast cancer risk was also examined in relation to a polygenic risk score (PRS) for each of the reproductive aging phenotypes. We replicated a number of variants in EA women, including rs7759938 in LIN28B for AM and rs16991615 in MCM8 for ANM; whereas in the AA group, only one SNP (rs2947411 in TMEM18) for AM was directionally consistent and nominally significant. In analysis of TRLS and NRLS, several SNPs were significant, including rs466639 in RXRG that was associated with both phenotypes in both AA and EA groups. None of the PRS was associated with breast cancer risk. Given the paucity of data available among AA populations, our study contributes to the literature of genetics of reproductive aging in AA women and highlights the importance of cross population replication of GWAS variants.