APOL1 Nephropathy Risk Alleles and Mortality in African American Adults: A Cohort Study

Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations

Key Points

Background

CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.

Methods

This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.

Results

The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.

Conclusions

In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

SMOC2 gene interacts with APOL1 in the development of end-stage kidney disease: A genome-wide association study

Background

Some but not all African-Americans (AA) who carry APOL1 nephropathy risk variants (APOL1) develop kidney failure (end-stage kidney disease, ESKD). To identify genetic modifiers, we assessed gene-gene interactions in a large prospective cohort of the REasons for Geographic and Racial Differences in Stroke (REGARDS) study.

Methods

Genotypes from 8,074 AA participants were obtained from Illumina Infinium Multi-Ethnic AMR/AFR Extended BeadChip. We compared 388 incident ESKD cases with 7,686 non-ESKD controls, using a two-locus interaction approach. Logistic regression was used to examine the effect of APOL1 risk status (using recessive and additive models), single nucleotide polymorphism (SNP), and APOL1*SNP interaction on incident ESKD, adjusting for age, sex, and ancestry. APOL1 *SNP interactions that met the threshold of 1.0 × 10-5 were replicated in the Genetics of Hypertension Associated Treatment (GenHAT) study (626 ESKD cases and 6,165 controls). In a sensitivity analysis, models were additionally adjusted for diabetes status. We conducted additional replication in the BioVU study.

Results

Two APOL1 risk alleles prevalence (recessive model) was similar in the REGARDS and GenHAT studies. Only one APOL1-SNP interaction, for rs7067944 on chromosome 10, ~10 KB from the PCAT5 gene met the genome-wide statistical threshold (P interaction = 3.4 × 10-8), but this interaction was not replicated in the GenHAT study. Among other relevant top findings (with P interaction < 1.0 × 10-5), a variant (rs2181251) near SMOC2 on chromosome six interacted with APOL1 risk status (additive) on ESKD outcomes (REGARDS study, P interaction =5.3 × 10-6) but the association was not replicated (GenHAT study, P interaction = 0.07, BioVU study, P interaction = 0.53). The association with the locus near SMOC2 persisted further in stratified analyses. Among those who inherited ≥1 alternate allele of rs2181251, APOL1 was associated with an increased risk of incident ESKD (OR [95%CI] = 2.27[1.53, 3.37]) but APOL1 was not associated with ESKD in the absence of the alternate allele (OR [95%CI] = 1.34[0.96, 1.85]) in the REGARDS study. The associations were consistent after adjusting for diabetes.

Conclusion

In a large genome-wide association study of AAs, a locus SMOC2 exhibited a significant interaction with the APOL1 locus. SMOC2 contributes to the progression of fibrosis after kidney injury and the interaction with APOL1 variants may contribute to an explanation for why only some APOLI high-risk individuals develop ESKD.

APOL1 Risk Variants, Acute Kidney Injury, and Death in Participants With African Ancestry Hospitalized With COVID-19 From the Million Veteran Program

IMPORTANCE

Coronavirus disease 2019 (COVID-19) confers significant risk of acute kidney injury (AKI). Patients with COVID-19 with AKI have high mortality rates.

OBJECTIVE

Individuals with African ancestry with 2 copies of apolipoprotein L1 (APOL1) variants G1 or G2 (high-risk group) have significantly increased rates of kidney disease. We tested the hypothesis that the APOL1 high-risk group is associated with a higher-risk of COVID-19-associated AKI and death.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included 990 participants with African ancestry enrolled in the Million Veteran Program who were hospitalized with COVID-19 between March 2020 and January 2021 with available genetic information.

EXPOSURES

The primary exposure was having 2 APOL1 risk variants (RV) (APOL1 high-risk group), compared with having 1 or 0 risk variants (APOL1 low-risk group).

MAIN OUTCOMES AND MEASURES

The primary outcome was AKI. The secondary outcomes were stages of AKI severity and death. Multivariable logistic regression analyses adjusted for preexisting comorbidities, medications, and inpatient AKI risk factors; 10 principal components of ancestry were performed to study these associations. We performed a subgroup analysis in individuals with normal kidney function prior to hospitalization (estimated glomerular filtration rate ≥60 mL/min/1.73 m2).

RESULTS

Of the 990 participants with African ancestry, 905 (91.4%) were male with a median (IQR) age of 68 (60-73) years. Overall, 392 (39.6%) patients developed AKI, 141 (14%) developed stages 2 or 3 AKI, 28 (3%) required dialysis, and 122 (12.3%) died. One hundred twenty-five (12.6%) of the participants were in the APOL1 high-risk group. Patients categorized as APOL1 high-risk group had significantly higher odds of AKI (adjusted odds ratio [OR], 1.95; 95% CI, 1.27-3.02; P = .002), higher AKI severity stages (OR, 2.03; 95% CI, 1.37-2.99; P < .001), and death (OR, 2.15; 95% CI, 1.22-3.72; P = .007). The association with AKI persisted in the subgroup with normal kidney function (OR, 1.93; 95% CI, 1.15-3.26; P = .01). Data analysis was conducted between February 2021 and April 2021.

CONCLUSIONS AND RELEVANCE

In this cohort study of veterans with African ancestry hospitalized with COVID-19 infection, APOL1 kidney risk variants were associated with higher odds of AKI, AKI severity, and death, even among individuals with prior normal kidney function.

A 7-gene signature predicts the prognosis of patients with bladder cancer

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C‐index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693–0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

Toward a fine-scale population health monitoring system

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.