Donna K. Arnett

Key Points: An integrative risk framework with genomic, epigenomic, and clinical factors was developed to predict risk of new-onset kidney failure in a study. Addition of polygenic and methylation information to a clinical risk score modestly improved the prediction of kidney failure. Polygenic and methylation risk scores alone were not superior to a clinical risk algorithm. Background: Kidney failure affects approximately 800,000 adults in the United States, and African American individuals carry a disproportionate burden of disease. Robust algorithms for clinical risk prediction, such as the kidney failure risk equation (KFRE), have previously been validated, whereas the clinical utility of polygenic risk scores (PRS) and methylation risk scores (MRS) have not been as well characterized. Methods: In this study, we evaluated the capacity of PRS and MRS for CKD to predict multiple clinical end points, including incident kidney failure and decline in kidney function. Leveraging genomic, epigenomic, and clinical data from African American participants of the Hypertension Genetic Epidemiology Network (HyperGEN), Multi-Ethnic Study of Atherosclerosis (MESA), and Electronic Medical Records and Genomics Network, we evaluated the performance of these risk scores in comparison with and in conjunction with the widely validated KFRE. Results: In HyperGEN, a higher PRS was associated with a higher risk of kidney failure. Similarly, the MRS was associated with incident kidney failure in both HyperGEN and MESA. Neither PRS nor MRS were associated with the rate of decline in kidney function in Electronic Medical Records and Genomics or MESA. The KFRE outperformed both PRS and MRS as a single predictor. However, multiscore models suggested a synergistic effect of PRS and MRS to the predictive accuracy of the KFRE, with the greatest risk of kidney failure among individuals in high-risk strata of KFRE, PRS, and MRS. Conclusions: Results from this exploratory analysis suggest that, although PRS and MRS may not be superior to clinical algorithms, inclusion of -omics information may improve disease risk prediction. Future studies should seek to increase sample size to better evaluate these risk models and improve their capacity.

BACKGROUND: We investigated whether risk factors for intracerebral hemorrhage (ICH) among indigenous Africans (IA) would vary in prevalence and effect compared with self-reported African, Hispanic, and White Americans by comparing data from 2 independent population-based case-control studies conducted in West Africa and the United States. METHODS: We compared ICH risk factors common to the SIREN (Stroke Investigative Research and Educational Network: 1100 case-control pairs) and the ERICH (Ethnic/Racial Variation of Intracerebral Hemorrhage: 999 case-control pairs African American participants, 998 case-control pairs, Hispanic Americans, 1000 case-control pairs, White Americans) studies. Ethnicity/Race was self-reported. The effect measure of interest is the odds ratio (OR). To test for differences in the effects of the risk factors between the SIREN IA study population and each of the ERICH study populations, a test for heterogeneity was computed using the R program, metagen (version 4.9-6). RESULTS: ICH occurred at a younger age among IA (54.3±13.4 years), African Americans (58.0±12.7), and Hispanic Americans (58.9±14.3), compared with White Americans (69.1±13.9). The largest distinction was for hypertension, where IA exhibited a much larger risk of ICH than the American study population (OR, 67.02 [95% CI, 33.30–134.85]), African American (OR, 3.71 [95% CI, 2.53–5.44]); Hispanic (OR, 3.55 [95% CI, 2.54–4.92]), and White population (OR, 2.69 [95% CI, 1.95–3.69]). Current alcohol use exhibited increased risk in IA (OR, 2.24 [95% CI, 1.36–3.67]), but not in African Americans (OR, 0.63 [95% CI, 0.46–0.86]), Hispanic (OR, 0.87 [95% CI, 0.65–1.17]), and White Americans (OR, 0.51 [95% CI, 0.38–0.69]). CONCLUSIONS: Identical or comparable risk factors do not consistently result in the same disease risk across different cultures and regions. Therefore, to improve our understanding of the genetic determinants and biological pathways driving ICH risk, it is crucial to study multiple populations, including IA, while accounting for the influence of environmental and social factors.

Brain structural volumes are highly heritable and are linked to multiple neuropsychological outcomes, including Alzheimer’s disease (AD). Genome-wide association studies have successfully identified genetic variants associated with intracranial volume (ICV), total brain volume (TBV), hippocampal volume (HV), and lateral ventricular volume (LVV). However, these studies mostly focused on common genetic variants with minor allele frequencies (MAF) > 1%, and individuals included in most of these studies were of predominantly European ancestry. Here, we performed whole-genome sequence (WGS) association studies of MRI brain volumes in 7,674 individuals of diverse race and ethnicity from the Trans-Omics for Precision Medicine (TOPMed) program. We identified novel genetic loci on chromosomes 13 and 16 near LINC00598 and CACNG3 associated with HV and TBV, respectively (lead variants rs115674829, P -value = 1.7×10 ^-9 in pooled analysis and rs150440001, P -value = 6.6×10 ^-9 in black participants). Both lead variant minor A alleles are rarer in white participants (MAF = 0.14% and 0.03%) and in Hispanic participants (MAF = 1.5% and 0.17%) but more common in black participants (MAF = 13% and 1.5%). Rare variant aggregated analyses identified RIPK1, a gene encoding a kinase involved in neuroinflammation and promising target for AD treatment, suggestively associated with LVV ( P -value=5×10 ^-6 ). This study provides new insights into the genetic correlates of brain structural volumes and illustrates the importance of leveraging WGS data and cohorts of diverse race and ethnicity to better characterize the genetic architecture of complex polygenic traits.

Key Points: The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits. However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations. Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR. 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.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R ² = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified TCL1A , NRIP1 , and TERT locus variants as modulators of mCA clonal expansion rate.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( P < 5 × 10 ^-9 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

Background: Baseline stroke severity is probably partly responsible for poor stroke outcomes in sub‐Saharan Africa. However, there is a paucity of information on determinants of stroke severity among indigenous Africans. We sought to identify the factors associated with stroke severity among West Africans in the SIREN (Stroke Investigative Research and Educational Networks) study. Methods and Results: Stroke was diagnosed clinically and confirmed with brain neuroimaging. Severe stroke was defined as a Stroke Levity Scale score of ≤5. A multivariate logistic regression model was constructed to identify factors associated with stroke severity at 95% CI and a nominal cutoff of 5% type 1 error. A total of 3660 stroke cases were included. Overall, 50.7%% had severe stroke, including 47.6% of all ischemic strokes and 56.1% of intracerebral hemorrhage. Factors independently associated with severe stroke were meat consumption (adjusted odds ratio [aOR], 1.97 [95% CI, 1.43–2.73]), low vegetable consumption (aOR, 2.45 [95% CI, 1.93–3.12]), and lesion volume, with an aOR of 1.67 (95% CI, 1.03–2.72) for lesion volume of 10 to 30 cm ³ and aOR of 3.88 (95% CI, 1.93–7.81) for lesion volume >30 cm ³ . Severe ischemic stroke was independently associated with total anterior circulation infarction (aOR, 3.1 [95% CI, 1.5–6.9]), posterior circulation infarction (aOR, 2.2 [95% CI, 1.1–4.2]), and partial anterior circulation infarction (aOR, 2.0 [95% CI, 1.2–3.3]) compared with lacunar stroke. Increasing age (aOR, 2.6 [95% CI, 1.3–5.2]) and lesion volume >30 cm ³ (aOR, 6.2 [95% CI, 2.0–19.3]) were independently associated with severe intracerebral hemorrhage. Conclusions: Severe stroke is common among indigenous West Africans, where modifiable dietary factors are independently associated with it. These factors could be targeted to reduce the burden of severe stroke.