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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 integrated individual-level genome-wide data from~19,000 European-ancestry individuals across 79 European populations and five European American cohorts. We generated 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 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 inLCT(rs4988235) and height or LDL-cholesterol were confirmed to be false positives whereas the association betweenLCTand 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.

OBJECTIVE Previous reports of the annual incidence of type 1 diabetes (T1D) in China were conducted using retrospective hospital cases, which may not reflect the reality. This longitudinal study estimated T1D incidence in a Chinese population of 21.7 million from 2007 to 2017. RESEARCH DESIGN AND METHODS A population-based registry of T1D was performed by the Beijing Municipal Health Commission Information Center. Annual incidence and 95% CIs were calculated by age group and sex. The association of sex with T1D incidence and predicted new cases of T1D were assessed using Poisson regression models. Annual percentage change and average annual percentage of change were assessed using Joinpoint regression. RESULTS Overall, there were 6,875 individuals who developed T1D from 2007 to 2017 in this population. T1D incidence (/100,000 persons) (95% CI) significantly increased from 2.72 (2.51, 2.93) in 2007 to 3.60 (3.38, 3.78) in 2017 (P < 0.001). The T1D onset peak was in the 10–14-year-old age group. While no significant trend was found in the 0–14- and 15–29-year-old age groups, T1D incidence markedly increased from 1.87 to 3.52 in the ≥30-year-old age group (P < 0.05). The prevalence of diabetic ketoacidosis at diagnosis was highest in the 0–4-year-old age group. We predicted new cases of T1D will increase 1.57-fold over the next decade. CONCLUSIONS T1D incidence in this large Chinese population is higher than has been reported previously. From 2007 to 2017, although the incidence peak was in the 10–14-year age group, the T1D incidence increased sharply in adults but not in youth.

Background There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. GRS for cardiometabolic traits were evaluated in sub-Saharan Africans in comparison with African Americans and European Americans. Methods We evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n = 5200), African Americans (AA; n = 9139) and European Americans (EUR; n = 9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and the increase in discriminatory ability over traditional risk factors [age, sex and body mass index (BMI)], with adjustment for ancestry-derived principal components. Results Across all traits, GRS showed up to a 5-fold and 20-fold greater predictive utility in EUR relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percentage increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EUR, 26.6% to 65.8% among AA and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EUR relative to AA and up to 44-fold greater in EUR relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EUR. Conclusions This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiple ethnic populations in genomic studies to ensure equitable clinical translation of GRS.

Rates of type 2 diabetes (T2D) and hypertension are increasing rapidly in urbanizing sub-Saharan Africa (SSA). While lifestyle factors drive the increases in T2D and hypertension prevalence, evidence across populations shows that genetic variation, which is driven by evolutionary forces including a natural selection that shaped the human genome, also plays a role. Here we report the evidence for the effect of selection in African genomes on mechanisms underlying T2D and hypertension, including energy metabolism, adipose tissue biology, insulin action and salt retention. Selection effects found for variants in genes PPARA and TCF7L2 may have enabled Africans to respond to nutritional challenges by altering carbohydrate and lipid metabolism. Likewise, African-ancestry-specific characteristics of adipose tissue biology (low visceral adipose tissue [VAT], high intermuscular adipose tissue and a strong association between VAT and adiponectin) may have been selected for in response to nutritional and infectious disease challenges in the African environment. Evidence for selection effects on insulin action, including insulin resistance and secretion, has been found for several genes including MPHOSPH9, TMEM127, ZRANB3 and MC3R. These effects may have been historically adaptive in critical conditions, such as famine and inflammation. A strong correlation between hypertension susceptibility variants and latitude supports the hypothesis of selection for salt retention mechanisms in warm, humid climates. Nevertheless, adaptive genomics studies in African populations are scarce. More work is needed, particularly genomics studies covering the wide diversity of African populations in SSA and Africans in diaspora, as well as further functional assessment of established risk loci.

The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.

Significance HLA alleles have previously been implicated with scleroderma risk, but, in this study, using a European American ancestral cohort and a newly recruited large cohort of African Americans, we comprehensively define the HLA alleles and amino acid residues associated with scleroderma. Scleroderma is characterized by mutually exclusive and specific autoantibodies. We demonstrated ancestry-predominant HLA alleles that were much more strongly associated with autoantibody subsets of scleroderma than with the overall risk of SSc. We bioinformatically predicted immunodominant peptides of self-antigens and demonstrated homology of these peptides with viral protein sequences from Mimiviridae and Phycodnaviridae families. Our findings suggest the hypothesis that scleroderma-specific autoantibodies may arise through molecular mimicry, driven by the interaction of specific viral antigens with corresponding HLA α/β heterodimers., Systemic sclerosis (SSc) is a clinically heterogeneous autoimmune disease characterized by mutually exclusive autoantibodies directed against distinct nuclear antigens. We examined HLA associations in SSc and its autoantibody subsets in a large, newly recruited African American (AA) cohort and among European Americans (EA). In the AA population, the African ancestry-predominant HLA-DRB1*08:04 and HLA-DRB1*11:02 alleles were associated with overall SSc risk, and the HLA-DRB1*08:04 allele was strongly associated with the severe antifibrillarin (AFA) antibody subset of SSc (odds ratio = 7.4). These African ancestry-predominant alleles may help explain the increased frequency and severity of SSc among the AA population. In the EA population, the HLA-DPB1*13:01 and HLA-DRB1*07:01 alleles were more strongly associated with antitopoisomerase (ATA) and anticentromere antibody-positive subsets of SSc, respectively, than with overall SSc risk, emphasizing the importance of HLA in defining autoantibody subtypes. The association of the HLA-DPB1*13:01 allele with the ATA+ subset of SSc in both AA and EA patients demonstrated a transancestry effect. A direct correlation between SSc prevalence and HLA-DPB1*13:01 allele frequency in multiple populations was observed (r = 0.98, P = 3 × 10−6). Conditional analysis in the autoantibody subsets of SSc revealed several associated amino acid residues, mostly in the peptide-binding groove of the class II HLA molecules. Using HLA α/β allelic heterodimers, we bioinformatically predicted immunodominant peptides of topoisomerase 1, fibrillarin, and centromere protein A and discovered that they are homologous to viral protein sequences from the Mimiviridae and Phycodnaviridae families. Taken together, these data suggest a possible link between HLA alleles, autoantibodies, and environmental triggers in the pathogenesis of SSc.

Evolutionary processes, including mutation, migration and natural selection, have influenced the prevalence and distribution of various disorders in humans. However, despite a few well-known examples, such as the APOL1 variants — which have undergone positive genetic selection for their ability to confer resistance to Trypanosoma brucei infection but confer a higher risk of chronic kidney disease — little is known about the effects of evolutionary processes that have shaped genetic variation on kidney disease. An understanding of basic concepts in evolutionary genetics provides an opportunity to consider how findings from ancient and archaic genomes could inform our knowledge of evolution and provide insights into how population migration and genetic admixture have shaped the current distribution and landscape of human kidney-associated diseases. Differences in exposures to infectious agents, environmental toxins, dietary components and climate also have the potential to influence the evolutionary genetics of kidneys. Of note, selective pressure on loci associated with kidney disease is often from non-kidney diseases, and thus it is important to understand how the link between genome-wide selected loci and kidney disease occurs in relation to secondary nephropathies., Here, the authors provide an overview of the evolutionary processes that have implications for our understanding of kidney disease development and progression. They describe data derived from studies of ancient and archaic genomes and how population migration and genetic admixture have shaped the current landscape of human kidney-associated diseases, as well as the potential impact of environmental influences on evolutionary genetics and the adaptation of kidneys., Key points Kidney diseases that have an age of onset after the reproductive age (that is, most forms of chronic kidney disease) are unlikely to act as agents of natural selection for the elimination of disease-causing alleles because those alleles are successfully transmitted to successive generations.Several loci under natural selection owing to the protection they provide against infectious agents (for example, Trypanosoma brucei and Plasmodium falciparum) have an effect on the risk of kidney disease (APOL1-associated chronic kidney disease and sickle cell nephropathy, respectively).Loci that are under natural selection and that increase the risk of disorders that damage the kidney (such as diabetes mellitus and hypertension) may contribute to the risk of secondary nephropathies.Only a handful of genetic loci identified by genome-wide association studies as being associated with complex kidney diseases show evidence of natural selection in primates; however, many more of these loci show evidence of natural selection in human populations, suggesting that most selective events at kidney-associated loci occurred relatively recently in the evolution of the human species.

Gene-lifestyle interaction analyses have identified genetic variants whose effect on cardiovascular risk-raising traits is modified by alcohol consumption and smoking behavior. The biological mechanisms of these interactions remain largely unknown, but may involve epigenetic modification linked to perturbation of gene expression. Diverse, individual-level datasets including genotypes, methylation and gene expression conditional on lifestyle factors, are ideally suited to study this hypothesis, yet are often unavailable for large numbers of individuals. Summary-level data, such as effect sizes of genetic variants on a phenotype, present an opportunity for multi-omic study of the biological mechanisms underlying gene-lifestyle interactions. We propose a method that unifies disparate, publicly available summary datasets to build mechanistic hypotheses in models of smoking behavior and alcohol consumption with blood lipid levels and blood pressure measures. Of 897 observed genetic interactions, discovered through genome-wide analysis in diverse multi-ethnic cohorts, 48 were identified with lifestyle-related differentially methylated sites within close proximity and linked to target genes. Smoking behavior and blood lipids account for 37 and 28 of these signals respectively. Five genes also showed differential expression conditional on lifestyle factors within these loci with mechanisms supported in the literature. Our analysis demonstrates the utility of summary data in characterizing observed gene-lifestyle interactions and prioritizes genetic loci for experimental follow up related to blood lipids, blood pressure, and cigarette smoking. We show concordance between multiple trait-or exposure-related associations from diverse assays, driving hypothesis generation for better understanding gene-lifestyle interactions.

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.

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.

Publisher's version (útgefin grein)., Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles., This project was supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). This research was supported in part by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. Tuomas O. Kilpeläinen was supported by the Danish Council for Independent Research (DFF–6110-00183) and the Novo Nordisk Foundation (NNF18CC0034900, NNF17OC0026848 and NNF15CC0018486). Diana van Heemst was supported by the European Commission funded project HUMAN (Health-2013-INNOVATION-1-602757). Susan Redline was supported in part by NIH R35HL135818 and HL11338. Study-specific acknowledgements can be found in the Supplementary Notes 2 and 4. The data on coronary artery disease have been contributed by the Myocardial Infarction Genetics and CARDIoGRAM investigators, and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG.

Background: Context-specific evidence of the spectrum of type 2 diabetes (T2D) burden is essential for setting priorities and designing interventions to reduce associated morbidity and mortality. However, there are currently limited data on the burden of T2D complications and comorbidity in sub-Saharan Africa (SSA). Here, we aimed to estimate the burden and risk factors for T2D complications and comorbidity in a large sample of sub-Saharan Africans. Methods: Using a standardized protocol, we documented and estimated the burden of T2D complications and comorbidities in 2,784 diabetes participants enrolled from tertiary health centers from Nigeria, Ghana and Kenya. T2D complications and comorbidities evaluated by study physicians and clinic staffs included cardiometabolic, ocular, neurological and renal characteristics. Using the same protocol, we contextualized the burden of these complications and comorbidities by evaluating them in a sample of 3,209 individuals without diabetes enrolled from the catchment communities where the cases originated. Logistic regression models were used to identify risk factors and to adjust estimates of risk. Results: The mean age of T2D participants was 56 (SD 11) years with a median duration of diabetes of 5 (interquartile range 2-10) years. The notable complications/comorbidities observed in this sample of T2D participants included hypertension (71%; 95% CI 69-73), obesity 27% (25-29) and hyperlipidemia 34% (32-36). Diabetic retinopathy was found in 15% (13-17) and cataracts in 32% (30-35). About 13% (12-15) had impaired renal function as assessed by eGFR, representing 1 in 8 participants. Erectile dysfunction was a complication in 35% (32-38) of men. Age, duration of T2D and body mass index were significant risk factors for most complications/comorbidities. Population-attributable fraction ranged between 6 and 64 percent for most comorbidities. Conclusion: We observed a substantial burden of diabetes complications and comorbidity in this sample of T2D participants from SSA. Given the reported association of these complications with increased burden of T2D morbidity and mortality we call for public health strategies that prioritise early detection, the maintenance of healthy blood pressure, weight and lipid levels, as well as strengthen health care system capacities to provide treatment and care for neurological and ophthalmological complications of diabetes. Funding Statement: The study was supported in part by the Intramural Research Program of the National Institutes of Health in the Center for Research on Genomics and Global Health (CRGGH). The CRGGH is supported by the National Human Genome Research Institute (NHGRI), the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the Center for Information Technology, and the Office of the Director at the National Institutes of Health (1ZIAHG200362). Support for participant recruitment and initial genetic studies of the AADM study was provided by NIH grant No. 3T37TW00041-03S2 from the Office of Research on Minority Health. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The study received ethical approval from the Institutional Review Board at each study site, Howard University and the United States National Institutes of Health.

© The Author(s) 2019. A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-Alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2- degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10?6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10?8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models. National Heart, Lung, and Blood Institute; American Heart Association Grant

BackgroundSerum lipids are biomarkers of cardiometabolic disease risk, and understanding the genomic factors contributing to their distribution has been of considerable interest. Large genome-wide association studies (GWAS) have identified over 150 lipids loci; however, GWAS of Africans (AF) are rare. Given the genomic diversity among those of African ancestry, it is expected that a GWAS in Africans could identify novel lipids loci. While GWAS have been conducted in African Americans (AA), such studies are not proxies for studies in continental Africans due to the drastically different environmental context. Therefore, we conducted a GWAS of 4,317 Africans enrolled in the Africa America Diabetes Mellitus study.Methods and ResultsWe used linear mixed models of the inverse normal transformations of covariate-djusted residuals of high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), total cholesterol (CHOL), triglycerides (TG), and TG/HDLC, with adjustment for three principal components and the random effect of relatedness. Replication of loci associated at p−8 was attempted in 9,542 AA. Meta-analysis of AF and AA was also conducted. We also conducted analyses that excluded the relatively small number of East Africans. We evaluated known lipids loci in Africans using both exact replication and “local” replication, which accounts for interethnic differences in linkage disequilibrium.In our main analysis, we identified 23 novel associations in Africans. Of the 14 of these that were able to be tested in AA, two associations replicated (GPNMB-TG and ENPP1-TG). Two additional novel loci were discovered upon meta-analysis with AA (rs138282551-TG and TLL2-CHOL). Analyses considering only those with predominantly West African ancestry (Nigeria, Ghana, and AA) yielded new insights: ORC5-LDLC and chr20:60973327-CHOL.ConclusionsWhile functional work will be useful to confirm and understand the biological mechanisms underlying these associations, this study demonstrates the utility of conducting large-scale genomic analyses in Africans for discovering novel loci. The functional significance of some of these loci in relation to lipids remains to be elucidated, yet some have known connections to lipids pathways. For instance, rs147706369 (intronic, TLL2) alters a regulatory motif for sterol regulatory element-binding proteins (SREBPs), which are a family of transcription factors that control the expression of a range of enzymes involved in cholesterol, fatty acid, and triglyceride synthesis.

Non-communicable diseases, including cardiovascular diseases (CVDs), are increasing in African populations. High serum low density lipoprotein cholesterol (LDL-cholesterol) levels are a known risk factor for CVDs in European populations, but the link remains poorly understood among Africans. This study investigated the associations between serum LDL-cholesterol levels and selected variants in the low density lipoprotein receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9) and low density lipoprotein receptor adaptor protein 1 (LDLRAP1) genes in some selected African populations. Nineteen SNPs were selected from publicly available African whole genome sequence data based on functional prediction and allele frequency. SNPs were genotyped in 1000 participants from the AWI-Gen, study selected from the extremes of LDL-cholesterol level distribution (500 with LDL-cholesterol>3.5 mmol/L and 500 with LDL-cholesterol

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.

Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. Science invited the experts below to help explore how we got here and where we should (or ought not) be going.

Background - Resistin, a protein linked with inflammation and cardiometabolic diseases, is one of few proteins for which GWAS consistently report variants within and near the coding gene ( RETN ). Here, we took advantage of the reduced linkage disequilibrium in African populations to infer genetic causality for circulating resistin levels by performing GWAS, whole-exome analysis, fine-mapping, Mendelian randomization and transcriptomic data analyses. Methods - GWAS and fine-mapping analyses for resistin were performed in 5621 African ancestry individuals, including 3754 continental Africans (AF) and 1867 African Americans (AA). Causal variants identified were subsequently used as an instrumental variable in Mendelian randomization analyses for homeostatic modelling (HOMA) derived insulin resistance index, BMI and type 2 diabetes. Results - The lead variant (rs3219175, in the promoter region of RETN ) for the single locus detected was the same for AF ( P -value 5.0×10 -111 ) and for AA (9.5×10 -38 ), respectively explaining 12.1% and 8.5% of variance in circulating resistin. Fine-mapping analyses and functional annotation revealed this variant as likely causal affecting circulating resistin levels as a cis -eQTL increasing RETN expression. Additional variants regulating resistin levels were upstream of RETN with genes PCP2 , STXBP2 and XAB2 showing the strongest association using integrative analysis of GWAS with transcriptomic data. Mendelian randomization analyses did not provide evidence for resistin increasing insulin resistance, BMI or type 2 diabetes risk in African-ancestry populations. Conclusions - Taking advantage of the fine-mapping resolution power of African genomes, we identified a single variant (rs3219175) as the likely causal variant responsible for most of the variability in circulating resistin levels. In contrast to findings in some other ancestry populations, we showed that resistin does not seem to increase insulin resistance and related cardiometabolic traits in African-ancestry populations.

Given a lifetime risk of ~90% by the ninth decade of life, it is unknown if there are true controls for hypertension in epidemiological and genetic studies. Here, we use the Bayesian framework to compare logistic and time-to-event approaches to modeling hypertension. Using a proportional hazards model, we explored nonparametric and parametric models of the baseline hazard function, accounting for interval censoring. In the Howard University Family Study (HUFS), a population-based study of African Americans from Washington, D.C., the median age at hypertension was 48 years, baseline hazard rates increased with age until 55 years, and the probability of being free of hypertension at 85 years of age was 12.2%. In the nationwide NHANES study, the median age at hypertension was 42 years in African Americans, in contrast to 57 years in European Americans and 56 years in Mexican Americans. Baseline hazard rates increased with age until 58 years in African Americans, comparable to 60 years in European Americans and 58 years in Mexican Americans. The probability of being free of hypertension at 85 years of age was 8.4% in African Americans, in contrast to 21.4% in European Americans and 20.6% in Mexican Americans. In all four groups, baseline hazard rates decreased but did not reach zero, consistent with the nonexistence of controls. Model fits were comparable for a proportional hazards model based on gamma-distributed hazard rates under a correlated prior process and a logistic model adjusted for age and age2. Last, using an agnostic model screening approach of 38 potential covariates, we identified and replicated in all groups a model that included chloride, low-density lipoprotein cholesterol, uric acid, and weight. With age and age2, the variance explained by these covariates was 40.9% in African Americans, 34.8% in European Americans, and 28.3% in Mexican Americans. Taken together, modeling of the baseline hazard function of hypertension suggests that there are no true controls and that controls in logistic regression are cases with a late age of onset. These findings shed considerable insights into the design of genetic and epidemiological studies of hypertension with implications for ethnic health disparities.

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration (Pint < 5×10−8). Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively (Pint < 5×10−8). Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci (≤1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3 (Pint < 5×10−8). This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.