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Abstract The role of genomic research and medicine in improving health continues to grow significantly, highlighting the need for increased equitable inclusion of diverse populations in genomics. Native Hawaiian and Pacific Islander (NHPI) communities are often missing from these efforts to ensure that the benefits of genomics are accessible to all individuals. In this article, we analyze the qualities of NHPI populations relevant to their inclusion in genomic research and investigate their current representation using data from the genome-wide association studies (GWAS) catalog. A discussion of the barriers NHPI experience regarding participating in research and recommendations to improve NHPI representation in genomic research are also included.

Abstract Elevated triglycerides (TG) are a risk factor for cardiometabolic disorders. There are limited data on lipidomics profiles associated with serum triglycerides concentrations, although these could advance our understanding of the mechanisms underlying these associations. We conducted a lipidomics study of 308 Nigerians with replication in 199 Kenyans. Regression models were used to assess the association of TG with 480 lipid metabolites. Association and mediation analyses were conducted to determine the relationship among TG, metabolites, and several cardiometabolic traits. Ninety-nine metabolites were significantly associated with TG, and 91% of these associations replicated. Overrepresentation analysis identified enrichment of diacylglycerols, monoacylglycerols, diacylglycerophosphoethanolamines, monoacylglycerophosphocholines, ceramide phosphocholines, and diacylglycerophosphocholines. TG-cardiometabolic trait associations were largely mediated by TG-associated metabolites. Associations with type 2 diabetes, waist circumference, body mass index, total cholesterol, and low-density lipoprotein cholesterol concentration were independently mediated by metabolites in multiple subpathways. This lipidomics study in sub-Saharan Africans demonstrated that TG is associated with several non-TG lipids classes, including phosphatidylethanolamines, phosphatidylcholines, lysophospholipids, and plasmalogens, some of which may mediate the effect of TG as a risk factor for cardiometabolic disorders. The study identifies metabolites that are more proximal to cardiometabolic traits, which may be useful for understanding the underlying biology as well as differences in TG-trait associations across ancestries.

Abstract Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10−9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10−16 and CPA3; p = 2.39 × 10−14) and wound healing (FN1; p = 7.63 × 10−9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes – increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response – that may play a critical role in asthma within the African Diaspora.

Abstract There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P

Abstract Background Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA). Methods A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch’s two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study. Results Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845–0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906–0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration. Conclusions We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

Abstract Background In vitro and in vivo studies have shown that certain cytokines and hormones may play a role in the development and progression of type 2 diabetes (T2D). However, studies on their role in T2D in humans are scarce. We evaluated associations between 11 circulating cytokines and hormones with T2D among a population of sub-Saharan Africans and tested for causal relationships using Mendelian randomization (MR) analyses. Methods We used logistic regression analysis adjusted for age, sex, body mass index, and recruitment country to regress levels of 11 cytokines and hormones (adipsin, leptin, visfatin, PAI-1, GIP, GLP-1, ghrelin, resistin, IL-6, IL-10, IL-1RA) on T2D among Ghanaians, Nigerians, and Kenyans from the Africa America Diabetes Mellitus study including 2276 individuals with T2D and 2790 non-T2D individuals. Similar linear regression models were fitted with homeostatic modelling assessments of insulin sensitivity (HOMA-S) and β-cell function (HOMA-B) as dependent variables among non-T2D individuals (n = 2790). We used 35 genetic variants previously associated with at least one of these 11 cytokines and hormones among non-T2D individuals as instrumental variables in univariable and multivariable MR analyses. Statistical significance was set at 0.0045 (0.05/11 cytokines and hormones). Results Circulating GIP and IL-1RA levels were associated with T2D. Nine of the 11 cytokines and hormones (exceptions GLP-1 and IL-6) were associated with HOMA-S, HOMA-B, or both among non-T2D individuals. Two-stage least squares MR analysis provided evidence for a causal effect of GIP and IL-RA on HOMA-S and HOMA-B in multivariable analyses (GIP ~ HOMA-S β = − 0.67, P-value = 1.88 × 10−6 and HOMA-B β = 0.59, P-value = 1.88 × 10−5; IL-1RA ~ HOMA-S β = − 0.51, P-value = 8.49 × 10−5 and HOMA-B β = 0.48, P-value = 5.71 × 10−4). IL-RA was partly mediated via BMI (30-34%), but GIP was not. Inverse variance weighted MR analysis provided evidence for a causal effect of adipsin on T2D (multivariable OR = 1.83, P-value = 9.79 × 10−6), though these associations were not consistent in all sensitivity analyses. Conclusions The findings of this comprehensive MR analysis indicate that circulating GIP and IL-1RA levels are causal for reduced insulin sensitivity and increased β-cell function. GIP’s effect being independent of BMI suggests that circulating levels of GIP could be a promising early biomarker for T2D risk. Our MR analyses do not provide conclusive evidence for a causal role of other circulating cytokines in T2D among sub-Saharan Africans.

Abstract European-ancestry populations are recognized as stratified but not as admixed, implying that residual confounding by locus-specific ancestry can affect studies of association, polygenic adaptation, and polygenic risk scores. We integrate individual-level genome-wide data from ~19,000 European-ancestry individuals across 79 European populations and five European American cohorts. We generate a new reference panel that captures ancestral diversity missed by both the 1000 Genomes and Human Genome Diversity Projects. Both Europeans and European Americans are admixed at the subcontinental level, with admixture dates differing among subgroups of European Americans. After adjustment for both genome-wide and locus-specific ancestry, associations between a highly differentiated variant in LCT (rs4988235) and height or LDL-cholesterol were confirmed to be false positives whereas the association between LCT and body mass index was genuine. We provide formal evidence of subcontinental admixture in individuals with European ancestry, which, if not properly accounted for, can produce spurious results in genetic epidemiology studies.

Summary: The vast majority of human populations and individuals have mixed ancestry. Consequently, adjustment for locus-specific ancestry is essential for genetic association studies. To empower association studies for all populations, it is necessary to integrate effects of locus-specific ancestry and genotype. We developed a joint test of ancestry and association that can be performed with summary statistics, is independent of study design, can take advantage of locus-specific ancestry effects to boost power in association testing, and can utilize association effects to fine map admixture peaks. We illustrate the test using the association between serum triglycerides and LPL. By combining data from African Americans, European Americans, and West Africans, we identify three conditionally independent variants with varying amounts of ancestrally differentiated allele frequencies. Using out-of-sample data, we demonstrate improved prediction achievable by accounting for multiple causal variants and locus-specific ancestry effects at a single locus.

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Abstract Adiponectin has been associated with cardiometabolic traits in observational studies across populations, yet it is unclear if these associations are causal. We performed Mendelian randomization (MR) analysis to assess the relationship between adiponectin and cardiometabolic traits in sub-Saharan Africans. We constructed a polygenic risk score (PRS) for adiponectin levels across 3354 unrelated sub-Saharan Africans. The PRS was used as the instrumental variable in two-stage least-squares MR analysis to assess its association with insulin resistance, HDL, LDL, total cholesterol, triglycerides, blood pressure, Type 2 Diabetes (T2D), and hypertension. The adiponectin PRS was causally related with LDL (β = 0.55, 95%CI 0.07–1.04, P-value = 0.024) but not the other traits. This association was observed in both overweight/obese and normal weight individuals, but only reached statistical significance among overweight/obese individuals (β = 0.55, 95%CI 0.01–1.08, P-value = 0.045). In normal weight individuals, the adiponectin PRS was associated with T2D (OR = 0.13, 95%CI 0.02–0.73, P-value = 0.021), and in men with HDL (β = 1.03, 95%CI 0.14–1.92, P-value = 0.023). The findings of this first MR study in sub-Saharan Africans support a causal relationship of adiponectin with LDL, with T2D in normal weight individuals only, and with HDL in men only. These observations add to the small but growing literature on adiponectin MR studies.

Summary: Background: West Africans and African Americans with substantial (∼80%) West African ancestry are characterized by low levels of triglycerides (TG) compared to East Africans and Europeans. The impact of these varying TG levels on other cardiometabolic risk factors is unclear. We compared the strength of association between TG with hypertension, blood pressure, BMI, waist circumference, type 2 diabetes (T2D), and fasting glucose across West African (WA), East African (EA), and European (EU) ancestry populations residing in three vastly different environmental settings: sub-Saharan Africa, United States, and Europe. Methods: We analysed data from four cross-sectional studies that included WA in sub-Saharan Africa (n = 7201), the U.S. (n = 4390), and Europe (n = 6436), EA in sub-Saharan Africa (n = 781), and EU in the U.S. (n = 8670) and Europe (n = 4541). Linear regression analyses were used to test the association between TG and cardiometabolic risk factors. Findings: Higher adjusted regression coefficients were observed in EU compared with WA ancestry for TG on hypertension (EU β [95% CI]: 0.179 [0.156, 0.203], WA β [95% CI]: 0.102 [0.086, 0.118]), BMI (EU β [95% CI]: 0.028 [0.027, 0.030], WA β [95% CI]: 0.015 [0.014, 0.016]), and waist circumference (EU β [95% CI]: 0.013 [0.013, 0.014], WA β [95% CI]: 0.009 [0.008, 0.009) (all ancestry × trait interaction P-values

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’s Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and ‘omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.

Abstract Background A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans. Methods We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans (n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses. Results GWAS identified 39 significant (P value

Abstract Serum bilirubin is associated with several clinical outcomes, including hypertension, type 2 diabetes (T2D), and drug metabolism. Here, we describe findings from our genome-wide association studies (GWAS) of serum (TBIL) using a generalized linear mixed model in West Africans (n = 1127), with adjustment for age, sex, body mass index, T2D, significant principal components of population structure, and cryptic relatedness. Genome-wide conditional analysis and CAVIARBF were used to fine map significant loci. The causal effect of TBIL on hypertension was assessed by Mendelian randomization (MR) using the GWAS findings as instrumental variables (IVs) in African Americans (n = 3,067). The SNP rs887829 (UGT1A1) was significantly associated with TBIL levels (effect allele (T) frequency = 0.49, β (SE) = 0.59 (0.04), p = 9.13 × 10−54). Genome-wide conditional analysis and regional fine mapping pointed to rs887829 as a possible causal variant with a posterior inclusion probability of 0.99. The T allele of rs887829 is associated with lower hepatic expression of UGT1A1. Using rs887829 as an IV, two-stage least-squares MR showed a causal effect of bilirubin on hypertension (β = −0.76, 95% CI [−1.52, −0.01], p = 0.0459). Our finding confirms that UGT1A1 influences bilirubin levels. Notably, lower TBIL is causally associated with the increased risk of hypertension.

Abstract Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with cross-sectional blood pressure (BP) traits; however, GWAS based on longitudinal BP have been underexplored. We performed ethnic-specific and trans-ethnic GWAS meta-analysis using longitudinal and cross-sectional BP data of 33,720 individuals from five cohorts in the US and one in Brazil. In addition to identifying several known loci, we identified thirteen novel loci with nine based on longitudinal and four on cross-sectional BP traits. Most of the novel loci were ethnic- or study-specific, with the majority identified in African Americans (AA). Four of these discoveries showed additional evidence of association in independent datasets, including an intergenic variant (rs4060030, p = 7.3 × 10–9) with reported regulatory function. We observed a high correlation between the meta-analysis results for baseline and longitudinal average BP (rho = 0.48). BP trajectory results were more correlated with those of average BP (rho = 0.35) than baseline BP(rho = 0.18). Heritability estimates trended higher for longitudinal traits than for cross-sectional traits, providing evidence for different genetic architectures. Furthermore, the longitudinal data identified up to 20% more BP known associations than did cross-sectional data. Our analyses of longitudinal BP data in diverse ethnic groups identified novel BP loci associated with BP trajectory, indicating a need for further longitudinal GWAS on BP and other age-related traits.

Impaired glucose tolerance is a major risk factor for type 2 diabetes (T2D) and several cardiometabolic disorders. To identify genetic loci underlying fasting glucose levels, we conducted an analysis of 9,232 individuals of European ancestry who at enrollment were either nondiabetic or had untreated type 2 diabetes. Multivariable linear mixed models were used to test for associations between fasting glucose and 7.9 million SNPs, with adjustment for age, body mass index (BMI), sex, significant principal components of the genotypes, and cryptic relatedness. Three previously discovered loci were genome-wide significant, with the lead SNPs being rs1260326, a missense variant in GCKR (p = 1.06×10−8); rs560887, an intronic variant in G6PC2 (p = 3.39×10−11); and rs13266634, a missense variant in SLC30A8 (p = 4.28×10−10). Fine mapping, genome-wide conditional analysis, and functional annotation indicated that the three loci were independently associated with fasting glucose. Each copy of an alternate allele at any of these three SNPs was associated with a reduction of 0.012 mmol/L in fasting glucose levels (p = 8.0×10−28), and this association was replicated in trans-ethnic analysis of 14,303 individuals (p = 2.2×10−16). The three SNPs were jointly associated with significantly reduced T2D risk, with an odds ratio (95% CI) of 0.93 (0.88, 0.98) per protective allele. Our findings implicate additive effects across pathophysiological pathways involved in type 2 diabetes, including glycolysis, gluconeogenesis, and insulin secretion. Since none of the individuals homozygous for the alternate alleles at all three loci has T2D, it might be possible to use a genetic predictor of fasting glucose levels to identify individuals at low vs. high risk of developing type 2 diabetes.

Type 2 diabetes (T2D) is prevalent in populations worldwide, however, mostly studied in European and mixed-ancestry populations. Here, the authors perform a genome-wide association study for T2D in over 5,000 sub-Saharan Africans and identify a locus, ZRANB3, that is specific for this population.

GWAS have identified more than 500 genetic loci associated with blood lipid levels. Here, the authors report a genome-wide analysis of interactions between genetic markers and physical activity, and find that physical activity modifies the effects of four genetic loci on HDL or LDL cholesterol.

Variations in lipid levels are attributed partly to genetic factors. Genome-wide association studies (GWASs) mainly performed in European, African American and Asian cohorts have identified variants associated with LDL-C, HDL-C, total cholesterol (TC) and triglycerides (TG), but few studies have been performed in sub-Saharan Africans. This study evaluated the effect of single nucleotide variants (SNVs) in eight candidate loci (ABCA1, LCAT, LPL, PON1, CETP, PCSK9, MVK, and MMAB) on lipid levels among 1855 Ghanaian adults. All lipid levels were measured directly using an automated analyser. DNA was extracted and genotyped using the H3Africa SNV array. Linear regression models were used to test the association between SNVs and log-transformed lipid levels, adjusting for sex, age and waist circumference. In addition Bonferroni correction was performed to account for multiple testing. Several variants of CETP, LCAT, PCSK9, and PON1 (MAF > 0.05) were associated with HDL-C, LDL-C and TC levels at p < 0.05. The lead variants for association with HDL-C were rs17231520 in CETP (β = 0.139, p < 0.0001) and rs1109166 in LCAT (β = −0.044, p = 0.028). Lower LDL-C levels were associated with an intronic variant in PCSK9 (rs11806638 [β = −0.055, p = 0.027]) and increased TC was associated with a variant in PON1 (rs854558 [β = 0.040, p = 0.020]). In silico functional analyses indicated that these variants likely influence gene function through their effect on gene transcription. We replicated a strong association between CETP variants and HDL-C and between PCSK9 variant and LDL-C in West Africans, with two potentially functional variants and identified three novel variants in linkage disequilibrium in PON1 which were associated with increasing TC levels in Ghanaians.

Abstract Background Sickle cell anemia (SCA) is a clinically heterogeneous, monogenic disorder. Medical care has less‐than‐optimal impact on clinical outcomes in SCA in Africa due to several factors, including patient accessibility, poor access to resources, and non‐availability of specific effective interventions for SCA. Methods Against this background, we investigated 192 African participants who underwent whole exome sequencing. Participants included 105 SCA patients spanning variable clinical expression: a “long survivor” group (age over 40 years), a “stroke” group (at least one episode of overt stroke), and a “random” group (patients younger than 40 years without overt cerebrovascular disease). Fifty‐eight ethnically matched homozygous hemoglobin A controls were also studied. Findings were validated in an independently recruited sample of 29 SCA patients. Statistical significance of the mutational burden of deleterious and loss‐of‐function variants per gene against a null model was estimated for each group, and gene‐set association tests were conducted to test differences between groups. Results In the “long survivor” group, deleterious/loss‐of‐function variants were enriched in genes including CLCN6 (a voltage‐dependent chloride channel for which rare deleterious variants have been associated with lower blood pressure) and OGHDL (important in arginine metabolism, which is a therapeutic target in SCA). In the “stroke” group, significant genes implicated were associated with increased activity of the blood coagulation cascade and increased complement activation, for example, SERPINC1, which encodes antithrombin. Oxidative stress and glutamate biosynthesis pathways were enriched in “long survivors” group. Published transcriptomic evidence provides functional support for the role of the identified pathways. Conclusions This study provides new gene sets that contribute to variability in clinical expression of SCA. Identified genes and pathways suggest new avenues for other interventions.

Gut dysbiosis has been associated with several disease outcomes including diabetes in human populations. Currently, there are no studies of the gut microbiome composition in relation to type 2 diabetes (T2D) in Africans. Here, we describe the profile of the gut microbiome in non-diabetic adults (controls) and investigate the association between gut microbiota and T2D in urban West Africans. Gut microbiota composition was determined in 291 Nigerians (98 cases, 193 controls) using fecal 16S V4 rRNA gene sequencing done on the Illumina MiSeq platform. Data analysis of operational taxonomic units (OTU) was conducted to describe microbiome composition and identify differences between T2D and controls. The most abundant phyla were Firmicutes, Actinobacteria, and Bacteroidetes. Clostridiaceae, and Peptostreptococcaceaea were significantly lower in cases than controls (p < 0.001). Feature selection analysis identified a panel of 18 OTUs enriched in cases that included Desulfovibrio piger, Prevotella, Peptostreptococcus, and Eubacterium. A panel of 17 OTUs that was enriched in the controls included Collinsella, Ruminococcus lactaris, Anaerostipes, and Clostridium. OTUs with strain-level annotation showing the largest fold-change included Cellulosilyticum ruminicola (log2FC = −3.1; p = 4.2 × 10−5), Clostridium paraputrificum (log2FC = −2.5; p = 0.005), and Clostridium butyricum (log2FC = −1.76; p = 0.01), all lower in cases. These findings are notable because supplementation with Clostridium butyricum and Desulfovibrio piger has been shown to improve hyperglycemia and reduce insulin resistance in murine models. This first investigation of gut microbiome and diabetes in urban Africans shows that T2D is associated with compositional changes in gut microbiota highlighting the possibility of developing strategies to improve glucose control by modifying bacterial composition in the gut.

Abstract Background Epigenome-wide association studies (EWAS) have identified DNA methylation loci involved in adiposity. However, EWAS on adiposity in sub-Saharan Africans are lacking despite the high burden of adiposity among African populations. We undertook an EWAS for anthropometric indices of adiposity among Ghanaians aiming to identify DNA methylation loci that are significantly associated. Methods The Illumina 450k DNA methylation array was used to profile DNA methylation in whole blood samples of 547 Ghanaians from the Research on Obesity and Diabetes among African Migrants (RODAM) study. Differentially methylated positions (DMPs) and differentially methylation regions (DMRs) were identified for BMI and obesity (BMI ≥ 30 kg/m2), as well as for waist circumference (WC) and abdominal obesity (WC ≥ 102 cm in men, ≥88 cm in women). All analyses were adjusted for age, sex, blood cell distribution estimates, technical covariates, recruitment site and population stratification. We also did a replication study of previously reported EWAS loci for anthropometric indices in other populations. Results We identified 18 DMPs for BMI and 23 for WC. For obesity and abdominal obesity, we identified three and one DMP, respectively. Fourteen DMPs overlapped between BMI and WC. DMP cg00574958 annotated to gene CPT1A was the only DMP associated with all outcomes analysed, attributing to 6.1 and 5.6% of variance in obesity and abdominal obesity, respectively. DMP cg07839457 (NLRC5) and cg20399616 (BCAT1) were significantly associated with BMI, obesity and with WC and had not been reported by previous EWAS on adiposity. Conclusions This first EWAS for adiposity in Africans identified three epigenome-wide significant loci (CPT1A, NLRC5 and BCAT1) for both general adiposity and abdominal adiposity. The findings are a first step in understanding the role of DNA methylation in adiposity among sub-Saharan Africans. Studies on other sub-Saharan African populations as well as translational studies are needed to determine the role of these DNA methylation variants in the high burden of adiposity among sub-Saharan Africans.

Abstract Genetic and archaeological studies have established a sub-Saharan African origin for anatomically modern humans with subsequent migrations out of Africa. Using the largest multi-locus data set known to date, we investigated genetic differentiation of early modern humans, human admixture and migration events, and relationships among ancestries and language groups. We compiled publicly available genome-wide genotype data on 5,966 individuals from 282 global samples, representing 30 primary language families. The best evidence supports 21 ancestries that delineate genetic structure of present-day human populations. Independent of self-identified ethno-linguistic labels, the vast majority (97.3%) of individuals have mixed ancestry, with evidence of multiple ancestries in 96.8% of samples and on all continents. The data indicate that continents, ethno-linguistic groups, races, ethnicities, and individuals all show substantial ancestral heterogeneity. We estimated correlation coefficients ranging from 0.522 to 0.962 between ancestries and language families or branches. Ancestry data support the grouping of Kwadi-Khoe, Kx’a, and Tuu languages, support the exclusion of Omotic languages from the Afroasiatic language family, and do not support the proposed Dené-Yeniseian language family as a genetically valid grouping. Ancestry data yield insight into a deeper past than linguistic data can, while linguistic data provide clarity to ancestry data.

Reduced glomerular filtration rate (eGFR) is a hallmark of chronic kidney disease. Here, Pattaro et al. conduct a meta-analysis to discover several new loci associated with variation in eGFR and find that genes associated with eGFR loci often encode proteins potentially related to kidney development.

Genomics is the study of the genetic material that constitutes the genomes of organisms. This genetic material can be sequenced and it provides a powerful tool for the study of human, plant and animal evolutionary history and diseases. Genomics research is becoming increasingly commonplace due to significant advances in and reducing costs of technologies such as sequencing. This has led to new challenges including increasing cost and complexity of data. There is, therefore, an increasing need for computing infrastructure and skills to manage, store, analyze and interpret the data. In addition, there is a significant cost associated with recruitment of participants and collection and processing of biological samples, particularly for large human genetics studies on specific diseases. As a result, researchers are often reluctant to share the data due to the effort and associated cost. In Africa, where researchers are most commonly at the study recruitment, determination of phenotypes and collection of biological samples end of the genomic research spectrum, rather than the generation of genomic data, data sharing without adequate safeguards for the interests of the primary data generators is a concern. There are substantial ethical considerations in the sharing of human genomics data. The broad consent for data sharing preferred by genomics researchers and funders does not necessarily align with the expectations of researchers, research participants, legal authorities and bioethicists. In Africa, this is complicated by concerns about comprehension of genomics research studies, quality of research ethics reviews and understanding of the implications of broad consent, secondary analyses of shared data, return of results and incidental findings. Additional challenges with genomics research in Africa include the inability to transfer, store, process and analyze large-scale genomics data on the continent, because this requires highly specialized skills and expensive computing infrastructure which are often unavailable. Recently initiatives such as H3Africa and H3ABioNet which aim to build capacity for large-scale genomics projects in Africa have emerged. Here we describe such initiatives, including the challenges faced in the generation, analysis and sharing of genomic data and how these challenges are being overcome.

We surveyed 26 quantitative traits and disease outcomes to understand the proportion of phenotypic variance explained by local ancestry in admixed African Americans. After inferring local ancestry as the number of African-ancestry chromosomes at hundreds of thousands of genotyped loci across all autosomes, we used a linear mixed effects model to estimate the variance explained by local ancestry in two large independent samples of unrelated African Americans. We found that local ancestry at major and polygenic effect genes can explain up to 20% and 8% of phenotypic variance, respectively. These findings provide evidence that most but not all additive genetic variance is explained by genetic markers undifferentiated by ancestry. These results also inform the proportion of health disparities due to genetic risk factors and the magnitude of error in association studies not controlling for local ancestry.

Low levels of high-density cholesterol (HDLc) accompany chronic kidney disease, but the association between HDLc and the estimated glomerular filtration rate (eGFR) in the general population is unclear. We investigated the HDLc-eGFR association in nondiabetic Han Chinese (HC, n=1100), West Africans (WA, n=1497), and African Americans (AA, n=1539). There were significant differences by ancestry: HDLc was positively associated with eGFR in HC (β=0.13, P