Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

BDNF Gene as a Precision Skill of Obesity Management

The scarcity of the results obtained for the treatment of obesity leads us to consider new strategies, contemplating all the factors involved in the development of the disease. One of the key molecules for controlling body weight and energy homeostasis is the brain-derived neurotrophic factor (BDNF). This work summarizes the mechanisms in which BDNF gene regulates this multifactorial disease. In addition, we discuss the role of other BDNF polymorphisms as genetic determinants of obesity. In this context, a total of 14 SNPs near or inside BDNF/BDNF-AS related to BMI were identified in various GWASs. Finally, we assess gene-diet interaction as a novel tool to prevent obesity and formulate solid and personalized nutritional management. Our research group has performed the first study on the association of BDNF-AS rs925946 polymorphism and calcium intake as potential modulators of the nutritional status. Although these results should be confirmed in future studies, they open the path for new prevention opportunities.

Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10^-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity.

Genetics of Obesity in East Asians

Obesity has become a public health problem worldwide. Compared with Europe, people in Asia tend to suffer from type 2 diabetes with a lower body mass index (BMI). Genome-wide association studies (GWASs) have identified over 750 loci associated with obesity. Although the majority of GWAS results were conducted in individuals of European ancestry, a recent GWAS in individuals of Asian ancestry has made a significant contribution to the identification of obesity susceptibility loci. Indeed, owing to the multifactorial character of obesity with a strong environmental component, the revealed loci may have distinct contributions in different ancestral genetic backgrounds and in different environments as presented through diet and exercise among other factors. Uncovering novel, yet unrevealed genes in non-European ancestries may further contribute to explaining the missing heritability for BMI. In this review, we aimed to summarize recent advances in obesity genetics in individuals of Asian ancestry. We therefore compared proposed mechanisms underlying susceptibility loci for obesity associated with individuals of European and Asian ancestries and discussed whether known genetic variants might explain ethnic differences in obesity risk. We further acknowledged that GWAS implemented in individuals of Asian ancestries have not only validated the potential role of previously specified obesity susceptibility loci but also exposed novel ones, which have been missed in the initial genetic studies in individuals of European ancestries. Thus, multi-ethnic studies have a great potential not only to contribute to a better understanding of the complex etiology of human obesity but also potentially of ethnic differences in the prevalence of obesity, which may ultimately pave new avenues in more targeted and personalized obesity treatments.

An in silico approach to identify and prioritize miRNAs target sites polymorphisms in colorectal cancer and obesity

Colorectal cancer (CRC) and obesity are linked clinical entities with a series of complex processes being engaged in their development. MicroRNAs (miRNAs) participate in these processes through regulating CRC and obesity‐related genes. This study aimed to develop an in silico approach to systematically identify and prioritize miRNAs target sites polymorphisms in obesity and CRC. Data from genome‐wide association studies (GWASs) were used to retrieve CRC and obesity‐associated variants. The polymorphisms that were resided in experimentally verified or computationally predicted miRNA target sites were retrieved and prioritized using a range of bioinformatics analyses. We found 6284 CRC and 38931 obesity unique variants. For CRC 33 haplotypes variants in 134 interactions were in miRNA targetome, while for obesity we found more than 935 unique interactions. Functionally prioritized SNPs revealed that, SNPs in 153 obesity and 50 CRC unique interactions were have disruptive effects on miRNA:mRNA integration by changing on target RNA secondary structure. Structural accessibility of target sites were decreased in 418 and 103 unique interactions and increased in 516 and 79 interactions, for obesity and CRC, respectively. The miRNA:mRNA hybrid stability was increased in 127 and 17 unique interactions and decreased in 33 and 24 interactions for the effect of obesity and CRC SNPs, respectively. In this study, seven SNPs with 15 interactions and three SNPs with four interactions were prioritized for obesity and CRC, respectively. These SNPs could be used for future studies for finding potential biomarkers for diagnoses, prognosis, or treatment of CRC and obesity.

Translating GWAS-identified loci for cardiac rhythm and rate using an in vivo image- and CRISPR/Cas9-based approach

A meta-analysis of genome-wide association studies (GWAS) identified eight loci that are associated with heart rate variability (HRV), but candidate genes in these loci remain uncharacterized. We developed an image- and CRISPR/Cas9-based pipeline to systematically characterize candidate genes for HRV in live zebrafish embryos. Nine zebrafish orthologues of six human candidate genes were targeted simultaneously in eggs from fish that transgenically express GFP on smooth muscle cells (Tg[acta2:GFP]), to visualize the beating heart. An automated analysis of repeated 30 s recordings of beating atria in 381 live, intact zebrafish embryos at 2 and 5 days post-fertilization highlighted genes that influence HRV (hcn4 and si:dkey-65j6.2 [KIAA1755]); heart rate (rgs6 and hcn4); and the risk of sinoatrial pauses and arrests (hcn4). Exposure to 10 or 25 µM ivabradine—an open channel blocker of HCNs—for 24 h resulted in a dose-dependent higher HRV and lower heart rate at 5 days post-fertilization. Hence, our screen confirmed the role of established genes for heart rate and rhythm (RGS6 and HCN4); showed that ivabradine reduces heart rate and increases HRV in zebrafish embryos, as it does in humans; and highlighted a novel gene that plays a role in HRV (KIAA1755).

Significance of Single-Nucleotide Variants in Long Intergenic Non-protein Coding RNAs

Single-nucleotide variants (SNVs) are the most common genetic variants and universally present in the human genome. Genome-wide association studies (GWASs) have identified a great number of disease or trait-associated variants, many of which are located in non-coding regions. Long intergenic non-protein coding RNAs (lincRNAs) are the major subtype of long non-coding RNAs; lincRNAs play crucial roles in various disorders and cellular models via multiple mechanisms. With rapid growth in the number of the identified lincRNAs and genetic variants, there is great demand for an investigation of SNVs in lincRNAs. Hence, in this article, we mainly summarize the significant role of SNVs within human lincRNA regions. Some pivotal variants may serve as risk factors for the development of various disorders, especially cancer. They may also act as important regulatory signatures involved in the modulation of lincRNAs in a tissue- or disorder-specific manner. An increasing number of researches indicate that lincRNA variants would potentially provide additional options for genetic testing and disease risk assessment in the personalized medicine era.

Non-random sampling leads to biased estimates of transcriptome association

Integration of independent data resources across -omics platforms offers transformative opportunity for novel clinical and biological discoveries. However, application of emerging analytic methods in the context of selection bias represents a noteworthy and pervasive challenge. We hypothesize that combining differentially selected samples for integrated transcriptome analysis will lead to bias in the estimated association between predicted expression and the trait. Our results are based on in silico investigations and a case example focused on body mass index across four well-described cohorts apparently derived from markedly different populations. Our findings suggest that integrative analysis can lead to substantial relative bias in the estimate of association between predicted expression and the trait. The average estimate of association ranged from 51.3% less than to 96.7% greater than the true value for the biased sampling scenarios considered, while the average error was - 2.7% for the unbiased scenario. The corresponding 95% confidence interval coverage rate ranged from 46.4% to 69.5% under biased sampling, and was equal to 75% for the unbiased scenario. Inverse probability weighting with observed and estimated weights is applied as one corrective measure and appears to reduce the bias and improve coverage. These results highlight a critical need to address selection bias in integrative analysis and to use caution in interpreting findings in the presence of different sampling mechanisms between groups.

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data

MOTIVATION

Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.

RESULTS

We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis.

AVAILABILITY AND IMPLEMENTATION

We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

Sex Differences in Genomic Drivers of Adipose Distribution and Related Cardiometabolic Disorders: Opportunities for Precision Medicine

This review focuses on the human genetics, epidemiology, and molecular pathophysiology of sex differences in central obesity, adipose distribution, and related cardiometabolic disorders. Distribution of fat is important for cardiometabolic health, with peripheral fat depots having a protective effect and central visceral fat depots conferring a detrimental effect on health. There are important sex differences in fat distribution that are masked when studying body mass index as a measure of obesity. From epidemiological, murine, and in vitro studies, several mechanisms have been proposed to explain the sex differences in adipose distribution, including sex hormonal effects, cell-intrinsic properties, and the microenvironment in fat depots. More recently, human genetics have revealed hundreds of loci for central obesity providing disruptive opportunities for mechanistic discoveries and clinical translation. A striking feature is that over one-third of these loci have reproducible but poorly understood sexual dimorphic associations with central obesity, most having stronger effects in women. Understanding the genetic and molecular mechanisms of adipose distribution and its sexual dimorphism in humans provides a unique opportunity to promote the use of precision medicine for early identification of at-risk individuals, and the development of novel therapeutic strategies for central obesity and related cardiometabolic disorders.

Genomics of Reproductive Traits and Cardiometabolic Disease Risk in African American Women

BACKGROUND

Age at menarche and age at natural menopause occur significantly earlier in African American women than in other ethnic groups. African American women also have twice the prevalence of cardiometabolic disorders related to the timing of these reproductive traits.

OBJECTIVES

The objectives of this integrative review were to (a) summarize the genome-wide association studies of reproductive traits in African American women, (b) identify genes that overlap with reproductive traits and cardiometabolic risk factors in African American women, and (c) propose biological mechanisms explaining the link between reproductive traits and cardiometabolic risk factors.

METHODS

PubMed was searched for genome-wide association studies of genes associated with reproductive traits in African American women. After extracting and summarizing the primary genes, we examined whether any of the associations with reproductive traits had also been identified with cardiometabolic risk factors in African American women.

RESULTS

Seven studies met the inclusion criteria. Associations with both reproductive and cardiometabolic traits were reported in or near the following genes: FTO, SEC16B, TMEM18, APOE, PHACTR1, KCNQ1, LDLR, PIK3R1, and RORA. Biological pathways implicated include body weight regulation, vascular homeostasis, and lipid metabolism.

DISCUSSION

A better understanding of the genetic basis of reproductive traits in African American women may provide insight into the biological mechanisms linking variation in these traits with increased risk for cardiometabolic disorders in this population.

The association of the common fat mass and obesity associated gene polymorphisms with type 2 diabetes in obese Iraqi population

BACKGROUND & OBJECTIVES

This study investigates the association of two potential Fat mass and obesity associated gene (FTO) gene polymorphisms (rs9939609 and rs918031) as potential predictors of type 2 diabetes (T2D) in obese Iraqi population and their metabolic effects on hyperglycemia and insulin sensitivity.

MATERIALS & METHODS

The study included 400 participants with obesity & T2D, with a matching 400 obese non-diabetic cohort. Venous blood samples were collected for DNA extraction. Using specific primers and restriction enzymes, genotyping was performed to identify the various alleles for each gene. The genotype and allele frequencies determined by multinomial logistic regression analysis for FTO single nucleotide polymorphisms (rs9939609) among all the study groups.

RESULTS

There is a two-fold increase in the risk of T2D within the homozygous genotype (TT) group (OR = 2.43, CI 95% 3.57-11.2, P ≤ 0.001) as compared to the wild type (TA). In addition, there was a significantly higher level of the minor allele genotype (T) in T2D patients when compared to the control group, (P ≤ 0.001).

CONCLUSION

We conclude that the FTO rs9939609 genotype significantly affect the development of insulin resistance, therefore the future occurrence of T2D, in obese individuals.

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

Genetics and epigenetics in obesity

Obesity is among the most threatening health burdens worldwide and its prevalence has markedly increased over the last decades. Obesity maybe considered a heritable trait. Identifications of rare cases of monogenic obesity unveiled that hypothalamic circuits and the brain-adipose axis play an important role in the regulation of energy homeostasis, appetite, hunger and satiety. For example, mutations in the leptin gene cause obesity through almost unsuppressed overeating. Common (multifactorial) obesity, most likely resulting from a concerted interplay of genetic, epigenetic and environmental factors, is clearly linked to genetic predisposition by multiple risk variants, which, however only account for a minor part of the general BMI variability. Although GWAS opened new avenues in elucidating the complex genetics behind common obesity, understanding the biological mechanisms relative to the specific risk contributing to obesity remains poorly understood. Non-genetic factors such as eating behavior or physical activity strongly modulate the individual risk for developing obesity. These factors may interact with genetic predisposition for obesity through epigenetic mechanisms. Thus, here, we review the current knowledge about monogenic and common (multifactorial) obesity highlighting the important recent advances in our knowledge on how epigenetic regulation is involved in the etiology of obesity.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs

Background

Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs.

Results

Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat.

Conclusion

Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5386-2) contains supplementary material, which is available to authorized users.

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

A study of Kibbutzim in Israel reveals risk factors for cardiometabolic traits and subtle population structure

Genetic studies in isolated populations often increase power for identifying loci associated with complex diseases and traits. We present here the Kibbutzim Family Study (KFS), aimed at investigating the genetic basis of cardiometabolic traits in extended Israeli families characterized by long-term social stability and a homogeneous environment. Extensive information on cardiometabolic traits, as well as genome-wide genotypes, were collected on 901 individuals. We observed that most KFS participants were of Ashkenazi Jewish (AJ) genetic origin, confirmed a recent severe bottleneck in the AJ recent history, and detected a subtle within-AJ population structure. Focusing on genetic variants relatively common in the KFS but very rare in Europeans, we observed that AJ-enriched variants appear in cancer-related pathways more than expected by chance. We conducted an association study of the AJ-enriched variants against 16 cardiometabolic traits, and found seven loci (24 variants) to be significantly associated. The strongest association, which we also replicated in an independent study, was between a variant upstream of MSRA (frequency ≈1% in the KFS and nearly absent in Europeans) and weight (P = 3.6∙10-8). In conclusion, the KFS is a valuable resource for the study of the population genetics of Israel as well as the genetics of cardiometabolic traits.

WITHDRAWN: Single nucleotide polymorphism, a putative driver for the role of long intergeneric non-coding RNA

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Genetics of Obesity in Diverse Populations

PURPOSE OF REVIEW

The prevalence of obesity continues to rise, fueling a global public health crisis characterized by dramatic increases in type 2 diabetes, cardiovascular disease, and many cancers. In the USA, several minority populations, who bear much of the obesity burden (47% in African Americans and Hispanic/Latinos, compared to 38% in European descent groups), are particularly at risk of downstream chronic disease. Compounding these disparities, most genome-wide association studies (GWAS)-including those of obesity-have largely been conducted in populations of European or East Asian ancestry. In fact, analysis of the GWAS Catalog found that while the proportion of participants of non-European or non-Asian descent had risen from 4% in 2009 to 19% in 2016, African-ancestry participants are still just 3% of GWAS, Hispanic/Latinos are < 0.5%, and other ancestries are < 0.3% or not represented at all. This review summarizes recent developments in obesity genomics in US minority populations, with the goal of reducing obesity health disparities and improving public health programs and access to precision medicine.

RECENT FINDINGS

GWAS of populations with the highest burden of obesity are essential to narrow candidate variants for functional follow-up, to identify additional ancestry-specific variants that contribute to individual genetic susceptibility, and to advance both public health and precision medicine approaches to obesity. Given the global public health burden posed by obesity and downstream chronic conditions which disproportionately affect non-European populations, GWAS of obesity-related traits in diverse populations is essential to (1) locate causal variants in GWAS-identified regions through fine mapping, (2) identify variants which influence obesity across ancestries through generalization, and (3) discover novel ancestry-specific variants which may be low frequency in European populations but common in other groups. Recent efforts to expand obesity genomic studies to understudied and underserved populations, including AAAGC, PAGE, and HISLA, are working to reduce obesity health disparities, improve public health, and bring the promise of precision medicine to all.

Genome-wide variants of Eurasian facial shape differentiation and a prospective model of DNA based face prediction

It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association studies (GWAS) were conducted on a discovery panel of Uyghurs. Six significant loci were identified, four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals and tested in hypothetic forensic scenarios.

A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci

Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10^-8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene-age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice.

Comprehensive review and annotation of susceptibility SNPs associated with obesity-related traits

We aimed to summarize the results of genetic association studies for obesity and provide a comprehensive annotation of all susceptibility single nucleotide polymorphisms (SNPs). A total of 72 studies were summarized, resulting in 90,361 susceptibility SNPs (738 index SNPs and 89,623 linkage disequilibrium SNPs). Over 90% of the susceptibility SNPs are located in non-coding regions, and it is challenging to understand their functional significance. Therefore, we annotated these SNPs by using various functional databases. We identified 24,623 functional SNPs, including 4 nonsense SNPs, 479 missense SNPs, 399 untranslated region SNPs which might affect microRNA binding, 262 promoter and 5,492 enhancer SNPs which might affect transcription factor binding, 7 splicing sites, 76 SNPs which might affect gene methylation levels, 1,839 SNPs under natural selection and 17,351 SNPs which might modify histone binding. Expression quantitative trait loci analyses for functional SNPs identified 98 target genes, including 69 protein coding genes, 27 long non-coding RNAs and 3 processed transcripts. The percentage of protein coding genes that could be correlated with obesity-related pathways directly or through gene-gene interaction is 75.36 (52/69). Our results may serve as an encyclopaedia of obesity susceptibility SNPs and offer guide for functional experiments.

The genetics of adiposity

Genome-wide discovery efforts have identified more than 500 genetic loci associated with adiposity traits. The vast majority of these loci were found through large-scale meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), and in European ancestry populations. However, alternative approaches, focusing on non-European ancestry populations, more refined adiposity measures, and low-frequency (minor allele frequency (MAF)<5%) coding variants, identified additional novel loci that had not been identified before. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with fat distribution point to pathways involved in adipocyte biology. Pinpointing the causal gene within each locus remains challenging, but is a critical step towards translation of genome-wide association study (GWAS) loci into new biology. Ultimately, new genes may provide pharmacological targets for the development of weight loss drugs.

Genetic Ancestry for Sleep Research: Leveraging Health Inequalities to Identify Causal Genetic Variants

Recent evidence has highlighted the health inequalities in sleep behaviors and sleep disorders that adversely affect outcomes in select populations, including African-American and Hispanic-American subjects. Race-related sleep health inequalities are ascribed to differences in multilevel and interlinked health determinants, such as sociodemographic factors, health behaviors, and biology. African-American and Hispanic-American subjects are admixed populations whose genetic inheritance combines two or more ancestral populations originating from different continents. Racial inequalities in admixed populations can be parsed into relevant groups of mediating factors (environmental vs genetic) with the use of measures of genetic ancestry, including the proportion of an individual's genetic makeup that comes from each of the major ancestral continental populations. This review describes sleep health inequalities in African-American and Hispanic-American subjects and considers the potential utility of ancestry studies to exploit these differences to gain insight into the genetic underpinnings of these phenotypes. The inclusion of genetic approaches in future studies of admixed populations will allow greater understanding of the potential biological basis of race-related sleep health inequalities.

Opposite Genetic Effects of CMIP Polymorphisms on the Risk of Type 2 Diabetes and Obesity: A Family-Based Study in China

C-Maf Inducing Protein (CMIP) gene polymorphisms were reported to be associated with type 2 diabetes mellitus (T2DM). Whether the association between CMIP and T2DM is mediated via obesity-related phenotypes is still unclear. We analyzed the association of CMIP rs2925979 with T2DM and a comprehensive set of obesity-related phenotypes in 1576 families ascertained from a Chinese population. These families included a total of 3444 siblings (1582 with T2DM, 963 with prediabetes, and 899 with a normal glucose level). Using multi-level mixed effects regression models, we found that each copy of CMIP rs2925979_T allele was associated with a 29% higher risk of T2DM in females (p = 9.30 × 10^-4), while it was not significantly associated with T2DM in males (p = 0.705). Meanwhile, rs2925979_T allele was associated with lower levels of body mass index (BMI), waist circumference (WC), hip circumference (HC), percentage of body fat (PBF), PBF of arms, PBF of legs, and PBF of trunk in nondiabetes females (all p < 0.05). The opposite associations of rs2925979_T allele with T2DM and obesity-related phenotypes suggest that CMIP may exert independent pleiotropic effects on T2DM and obesity-related phenotypes in females.

Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications

Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although there is reason to hope that these discoveries will eventually lead to new preventive and therapeutic agents for obesity, this will take time because such developments require detailed mechanistic understanding of how an SNP influences phenotype (and this information is largely unavailable). Fortunately, absence of functional information has not prevented GWAS findings from providing insights into the biology of obesity. Genes near loci regulating total body mass are enriched for expression in the CNS, whereas genes for fat distribution are enriched in adipose tissue itself. Gene by environment and lifestyle interaction analyses have revealed that our increasingly obesogenic environment might be amplifying genetic risk for obesity, yet those at highest risk could mitigate this risk by increasing physical activity and possibly by avoiding specific dietary components. GWAS findings have also been used in mendelian randomisation analyses probing the causal association between obesity and its many putative complications. In supporting a causal association of obesity with diabetes, coronary heart disease, specific cancers, and other conditions, these analyses have clinical relevance in identifying which outcomes could be preventable through weight loss interventions.

Genetic Fine-Mapping and Identification of Candidate Genes and Variants for Adiposity Traits in Outbred Rats

OBJECTIVE

Obesity is a major risk factor for multiple diseases and is in part heritable, yet the majority of causative genetic variants that drive excessive adiposity remain unknown. Here, outbred heterogeneous stock (HS) rats were used in controlled environmental conditions to fine-map novel genetic modifiers of adiposity.

METHODS

Body weight and visceral fat pad weights were measured in male HS rats that were also genotyped genome-wide. Quantitative trait loci (QTL) were identified by genome-wide association of imputed single-nucleotide polymorphism (SNP) genotypes using a linear mixed effect model that accounts for unequal relatedness between the HS rats. Candidate genes were assessed by protein modeling and mediation analysis of expression for coding and noncoding variants, respectively.

RESULTS

HS rats exhibited large variation in adiposity traits, which were highly heritable and correlated with metabolic health. Fine-mapping of fat pad weight and body weight revealed three QTL and prioritized five candidate genes. Fat pad weight was associated with missense SNPs in Adcy3 and Prlhr and altered expression of Krtcap3 and Slc30a3, whereas Grid2 was identified as a candidate within the body weight locus.

CONCLUSIONS

These data demonstrate the power of HS rats for identification of known and novel heritable mediators of obesity traits.

The genetic underpinnings of body fat distribution

Obesity, defined as a body mass index (BMI) ≥ 30 kg/m², has reached epidemic proportions; people who are overweight (BMI > 25 kg/m²) or obese now comprise more than 25% of the world's population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered: The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity-related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary: Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro, will be crucial in translating genome-wide association signals to the causal mechanisms driving disease.

The genomic landscape of African populations in health and disease

A deeper appreciation of the complex architecture of African genomes is critical to the global effort to understand human history, biology and differential distribution of disease by geography and ancestry. Here, we report on how the growing engagement of African populations in genome science is providing new insights into the forces that shaped human genomes before and after the Out-of-Africa migrations. As a result of this human evolutionary history, African ancestry populations have the greatest genomic diversity in the world, and this diversity has important ramifications for genomic research. In the case of pharmacogenomics, for instance, variants of consequence are not limited to those identified in other populations, and diversity within African ancestry populations precludes summarizing risk across different African ethnic groups. Exposure of Africans to fatal pathogens, such as Plasmodium falciparum, Lassa Virus and Trypanosoma brucei rhodesiense, has resulted in elevated frequencies of alleles conferring survival advantages for infectious diseases, but that are maladaptive in modern-day environments. Illustrating with cardiometabolic traits, we show that while genomic research in African ancestry populations is still in early stages, there are already many examples of novel and African ancestry-specific disease loci that have been discovered. Furthermore, the shorter haplotypes in African genomes have facilitated fine-mapping of loci discovered in other human ancestry populations. Given the insights already gained from the interrogation of African genomes, it is imperative to continue and increase our efforts to describe genomic risk in and across African ancestry populations.