Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Common and rare genetic variation intersects with ancestry to influence human skin and plasma carotenoid concentrations

Carotenoids are dietary bioactive compounds with health effects that are biomarkers of fruit and vegetable intake. Here, we examine genetic associations with plasma and skin carotenoid concentrations in two rigorously phenotyped human cohorts (n=317). Analysis of genome-wide SNPs revealed heritability to vary by genetic ancestry (h2=0.08-0.44) with ten SNPs at four loci reaching genome-wide significance (P<5E-08) in multivariate models, including at RAPGEF1 (rs3765544, P=8.86E-10, beta=0.75) with α-carotene, and near IGSF11 (rs80316816, P=6.25E-10, beta=0.74), with cryptoxanthin; these were replicated in the second cohort (n=110). Multiple SNPs near IGSF11 demonstrated genotype-dependent dietary effects on plasma cryptoxanthin. Deep sequencing of 35 candidate genes revealed associations between the PKD1L2-BCO1 locus and plasma β-carotene (Padj=0.04, beta=-1.3 to -0.3), and rare, ancestry-restricted, damaging variants in CETP (rs2303790) and APOA1 (rs756535387) in individuals with high skin carotenoids. Our findings implicate novel loci in carotenoid disposition and indicate the importance of including cohorts of diverse genetic ancestry.

Causal effects of omega-6 and LDL-C on androgenetic alopecia: A Mendelian randomization study

BACKGROUND

Increasing studies have reported a causal relationship between androgenetic alopecia (AGA) and lipid-related metabolites. However, the relationships between HDL-C, LDL-C, Omega-6, and Omega-3 with AGA remain unclear. Some research findings are even contradictory. Therefore, we designed this study to explore this issue.

METHODS

In this study, we selected seven exposure factors, screened SNPs with significant associations, removed linkage disequilibrium and weak instrumental variables, and conducted bidirectional MR analysis.

RESULTS

The study found that omega-6 and LDL-C, especially total cholesterol in medium LDL and total cholesterol in small LDL, are risk factors for the occurrence of androgenetic alopecia.

CONCLUSION

In summary, we found that various lipid-related metabolites have a causal relationship with the occurrence of androgenetic alopecia, providing new insights into the pathogenesis of androgenetic alopecia and offering references for clinical treatment of androgenetic alopecia.

Identification of shared genetic risks underlying metabolic syndrome and its related traits in the Korean population

Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS.

Variant level heritability estimates of type 2 diabetes in African Americans

Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.

A novel approach to risk exposure and epigenetics-the use of multidimensional context to gain insights into the early origins of cardiometabolic and neurocognitive health

BACKGROUND

Each mother-child dyad represents a unique combination of genetic and environmental factors. This constellation of variables impacts the expression of countless genes. Numerous studies have uncovered changes in DNA methylation (DNAm), a form of epigenetic regulation, in offspring related to maternal risk factors. How these changes work together to link maternal-child risks to childhood cardiometabolic and neurocognitive traits remains unknown. This question is a key research priority as such traits predispose to future non-communicable diseases (NCDs). We propose viewing risk and the genome through a multidimensional lens to identify common DNAm patterns shared among diverse risk profiles.

METHODS

We identified multifactorial Maternal Risk Profiles (MRPs) generated from population-based data (n = 15,454, Avon Longitudinal Study of Parents and Children (ALSPAC)). Using cord blood HumanMethylation450 BeadChip data, we identified genome-wide patterns of DNAm that co-vary with these MRPs. We tested the prospective relation of these DNAm patterns (n = 914) to future outcomes using decision tree analysis. We then tested the reproducibility of these patterns in (1) DNAm data at age 7 and 17 years within the same cohort (n = 973 and 974, respectively) and (2) cord DNAm in an independent cohort, the Generation R Study (n = 686).

RESULTS

We identified twenty MRP-related DNAm patterns at birth in ALSPAC. Four were prospectively related to cardiometabolic and/or neurocognitive childhood outcomes. These patterns were replicated in DNAm data from blood collected at later ages. Three of these patterns were externally validated in cord DNAm data in Generation R. Compared to previous literature, DNAm patterns exhibited novel spatial distribution across the genome that intersects with chromatin functional and tissue-specific signatures.

CONCLUSIONS

To our knowledge, we are the first to leverage multifactorial population-wide data to detect patterns of variability in DNAm. This context-based approach decreases biases stemming from overreliance on specific samples or variables. We discovered molecular patterns demonstrating prospective and replicable relations to complex traits. Moreover, results suggest that patterns harbour a genome-wide organisation specific to chromatin regulation and target tissues. These preliminary findings warrant further investigation to better reflect the reality of human context in molecular studies of NCDs.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

A single-cell massively parallel reporter assay detects cell-type-specific gene regulation

Massively parallel reporter gene assays are key tools in regulatory genomics but cannot be used to identify cell-type-specific regulatory elements without performing assays serially across different cell types. To address this problem, we developed a single-cell massively parallel reporter assay (scMPRA) to measure the activity of libraries of cis-regulatory sequences (CRSs) across multiple cell types simultaneously. We assayed a library of core promoters in a mixture of HEK293 and K562 cells and showed that scMPRA is a reproducible, highly parallel, single-cell reporter gene assay that detects cell-type-specific cis-regulatory activity. We then measured a library of promoter variants across multiple cell types in live mouse retinas and showed that subtle genetic variants can produce cell-type-specific effects on cis-regulatory activity. We anticipate that scMPRA will be widely applicable for studying the role of CRSs across diverse cell types.

Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.

Association of Genome-Wide Polygenic Risk Score for Body Mass Index With Cardiometabolic Health From Childhood Through Midlife

BACKGROUND

Genetic information may help to identify individuals in childhood who are at increased risk for cardiometabolic disease.

METHODS

We included 1201 BHS (Bogalusa Heart Study) participants (832 White participants and 369 Black participants) who were followed up to 42.3 years, starting at a mean age of 9.8 years. A validated genome-wide polygenic risk score (PRS) was tested for association with midlife body mass index (BMI), fasting plasma glucose, and systolic blood pressure using multiple linear regression models. Cox proportional hazards models tested associations of the PRS with incident obesity, diabetes, and hypertension. All analyses were conducted according to race and adjusted for baseline age, sex, ancestry, and BMI.

RESULTS

The constructed PRS was significantly and modestly correlated with midlife BMI in both White and Black participants, with correlation coefficients of 0.27 (P=1.94×10-8) and 0.16 (P=5.50×10-3), respectively. In White participants, per SD increase of PRS was associated with an average 1.29 kg/m2 higher BMI (P=4.44×10-9), 2.82 mg/dL higher fasting plasma glucose (P=1.17×10-3), and 1.09 mm Hg higher systolic blood pressure (P=3.57×10-2) at midlife. The PRS also conferred a 26% higher increased risk of obesity (P=3.50×10-6) in White participants. In addition, the variance in midlife BMI explained increased from 0.1973 to 0.2293 when PRS was added to the model including age, sex, principal components, and baseline BMI (P<0.0001). No associations were observed in Black participants.

CONCLUSIONS

Adiposity-related genetic information independently predicted cardiometabolic health in White BHS participants. Null associations observed in Black BHS participants highlight the urgent need for PRS development in multi-ancestry populations.

Transethnic meta-analysis of exome-wide variants identifies new loci associated with male-specific metabolic syndrome

BACKGROUND

Metabolic syndrome (MetS) is a group of very common human conditions promoting strong understand the impact of rare variants, beyond exome-wide association studies, to potentially discover causative variants, across different ethnic populations.

OBJECTIVE

We performed transethnic, exome-wide MetS association studies on MetS in men.

METHODS

We analyzed genotype data of 5302 European subjects (2658 cases and 2644 controls), in the discovery stage of the European METabolic Syndrome In Men study, generated from exome chips, and 2481 subjects (714 cases and 1767 controls), in the replication stage, across 6 independent cohorts of 5 ancestries (T2D-GENES consortium), using whole-exome sequencing. We therefore evaluated gene-level and variant-level associations, of rare variants for MetS, using logistic regression (LR) and multivariate analyses (MulA).

RESULTS

Gene-based association found the gene for the cholesteryl ester transfer protein (CETP) (from MulA, p value = 4.67 × 10-9; from LR, p value = 0.009) to well associate with MetS. At two missense variants, from 8 rare variants in CETP, Ala390Pro (rs5880) (from MulA, p value = 1.28 × 10-7; from LR, p value = 1.34 × 10-4) and Arg468Gln (rs1800777) (from MulA, p value = 2.40 × 10-5; from LR, p value = 1.49 × 10-3) significantly associated with MetS across five ancestries.

CONCLUSIONS

Our findings highlight novel rare variants of genes that confer MetS susceptibility, in Europeans, that are shared with diverse populations, emphasizing an opportunity to further understand the biological target or genes that underlie MetS, across populations.

Genetic and environmental correlational structure among metabolic syndrome endophenotypes

Metabolic syndrome (MetS) is diagnosed by the presence of high scores on three or more metabolic traits, including systolic and diastolic blood pressure (SBP, DBP), glucose and insulin levels, cholesterol and triglyceride (TG) levels, and central obesity. A diagnosis of MetS is associated with increased risk of cardiovascular disease and type 2 diabetes. The components of MetS have long been demonstrated to have substantial genetic components, but their genetic overlap is less well understood. The present paper takes a multi-prong approach to examining the extent of this genetic overlap. This includes the quantitative genetic and additive Bayesian network modeling of the large TwinsUK project and examination of the results of genome-wide association study (GWAS) of UK Biobank data through use of LD score regression and examination of the number of genes and pathways identified in the GWASes which overlap across MetS traits. Results demonstrate a modest genetic overlap, and the genetic correlations obtained from TwinsUK and UK Biobank are nearly identical. However, these correlations imply more genetic dissimilarity than similarity. Furthermore, examination of the extent of overlap in significant GWAS hits, both at the gene and pathway level, again demonstrates only modest but significant genetic overlap. This lends support to the idea that in clinical treatment of MetS, treating each of the components individually may be an important way to address MetS.

Transforming faces to mimic natural kin: A comparison of different paradigms

The ability to detect phenotypic similarity or kinship in third-parties' faces is not perfect, but better than chance. Still, some humans are better than others at this task. Yet researchers in kinship detection have difficulties in building up large and diverse datasets of high-quality pictures of related persons. The current experiments tested a novel method for circumventing this difficulty by using morphing techniques in order to generate a wide array of stimuli derived from a limited number of individual pictures. Six experiments tested various stimuli (standard protocol, mirrored face, other-sex face, other-ethnicity face, other-expression face and antiface). Our benchmarks are the similarity or kinship scores achieved by participants when faced with pictures of real siblings. We show that all stimuli, except the antiface, elicit detection scores similar to those elicited by real pictures of actual siblings. In addition, by exploring different experiment parameters (simultaneous or sequential task, kinship or similarity task) and some individual characteristics, these experiments provide a better understanding of kinship detection in third parties. The validation of our new method will allow widening the range of available stimuli to the research community, and even to develop new ecologically relevant experimental protocols that are hardly or not feasible with veridical images.

Hepatic Transcriptome Profiling Reveals Lack of Acsm3 Expression in Polydactylous Rats with High-Fat Diet-Induced Hypertriglyceridemia and Visceral Fat Accumulation

Metabolic syndrome (MetS) is an important cause of worldwide morbidity and mortality. Its complex pathogenesis includes, on the one hand, sedentary lifestyle and high caloric intake, and, on the other hand, there is a clear genetic predisposition. PD (Polydactylous rat) is an animal model of hypertriglyceridemia, insulin resistance, and obesity. To unravel the genetic and pathophysiologic background of this phenotype, we compared morphometric and metabolic parameters as well as liver transcriptomes among PD, spontaneously hypertensive rat, and Brown Norway (BN) strains fed a high-fat diet (HFD). After 4 weeks of HFD, PD rats displayed marked hypertriglyceridemia but without the expected hepatic steatosis. Moreover, the PD strain showed significant weight gain, including increased weight of retroperitoneal and epididymal fat pads, and impaired glucose tolerance. In the liver transcriptome, we found 5480 differentially expressed genes, which were enriched for pathways involved in fatty acid beta and omega oxidation, glucocorticoid metabolism, oxidative stress, complement activation, triacylglycerol and lipid droplets synthesis, focal adhesion, prostaglandin synthesis, interferon signaling, and tricarboxylic acid cycle pathways. Interestingly, the PD strain, contrary to SHR and BN rats, did not express the Acsm3 (acyl-CoA synthetase medium-chain family member 3) gene in the liver. Together, these results suggest disturbances in fatty acid utilization as a molecular mechanism predisposing PD rats to hypertriglyceridemia and fat accumulation.

Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

Pleiotropic genetic influence on birth weight and childhood obesity

Childhood obesity is a global public health problem. Understanding the molecular mechanisms that underlie early origins of childhood obesity can facilitate interventions. Consistent phenotypic and genetic correlations have been found between childhood obesity traits and birth weight (a proxy for in-utero growth), suggesting shared genetic influences (pleiotropy). We aimed to (1) investigate whether there is significant shared genetic influence between birth weight and childhood obesity traits, and (2) to identify genetic loci with shared effects. Using a statistical approach that integrates summary statistics and functional annotations for paired traits, we found strong evidence of pleiotropy (P < 3.53 × 10^–127) and enrichment of functional annotations (P < 1.62 × 10^–39) between birth weight and childhood body mass index (BMI)/obesity. The pleiotropic loci were enriched for regulatory features in skeletal muscle, adipose and brain tissues and in cell lines derived from blood lymphocytes. At 5% false discovery rate, 6 loci were associated with birth weight and childhood BMI and 13 loci were associated with birth weight and childhood obesity. Out of these 19 loci, one locus (EBF1) was novel to childhood obesity and one locus (LMBR1L) was novel to both birth weight and childhood BMI/obesity. These findings give evidence of substantial shared genetic effects in the regulation of both fetal growth and childhood obesity.

An Academic Clinician's Road Map to Hypertension Genomics: Recent Advances and Future Directions MMXX

At the dawn of the new decade, it is judicious to reflect on the boom of knowledge about polygenic risk for essential hypertension supplied by the wealth of genome-wide association studies. Hypertension continues to account for significant cardiovascular morbidity and mortality, with increasing prevalence anticipated. Here, we overview recent advances in the use of big data to understand polygenic hypertension, as well as opportunities for future innovation to translate this windfall of knowledge into clinical benefit.

Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies

Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.

GWA-based pleiotropic analysis identified potential SNPs and genes related to type 2 diabetes and obesity

Metabolic syndrome is a cluster of symptoms including excessive body fat and insulin resistance which may lead to obesity and type 2 diabetes (T2D). The physiological and pathological cross-talk between T2D and obesity is crucial and complex, meanwhile, the genetic connection between T2D and obesity is largely unknown. The purpose of this study is to identify pleiotropic SNPs and genes between these two associated conditions by applying genetic analysis incorporating pleiotropy and annotation (GPA) on two large genome-wide association studies (GWAS) data sets: a body mass index (BMI) data set containing 339,224 subjects and a T2D data set containing 110,452 subjects. In all, 5182 SNPs showed pleiotropy in both T2D and obesity. After further prioritization based on suggested local false discovery rates (FDR) by the GPA model, 2146 SNPs corresponding to 217 unique genes are significantly associated with both traits (FDR < 0.2), among which 187 are newly identified pleiotropic genes compare with original GWAS in individual traits. Subsequently, gene enrichment and pathway analyses highlighted several pleiotropic SNPs including rs849135 (FDR = 0.0002), rs2119812 (FDR = 0.0018), rs4506565 (FDR = 1.23E-08), rs1558902 (7.23E-10) and corresponding genes JAZF1, SYN2, TCF7L2, FTO which may play crucial rol5es in the etiology of both T2D and obesity. Additional evidences from expression data analysis of pleiotropic genes strongly supports that the pleiotropic genes including JAZF1 (p = 1.39E-05 and p = 2.13E-05), SYN2 (p = 5.49E-03 and p = 5.27E-04), CDKN2C (p = 1.99E-12 and p = 6.27E-11), RABGAP1 (p = 3.08E-03 and p = 7.46E-03), and UBE2E2 (p = 1.83E-04 and p = 8.22E-03) play crucial roles in both obesity and T2D pathogenesis. Pleiotropic analysis integrated with functional network identified several novel and causal SNPs and genes involved in both BMI and T2D which may be ignored in single GWAS.

Heritability and genome-wide association analyses of fasting plasma glucose in Chinese adult twins

Background

Currently, diabetes has become one of the leading causes of death worldwide. Fasting plasma glucose (FPG) levels that are higher than optimal, even if below the diagnostic threshold of diabetes, can also lead to increased morbidity and mortality. Here we intend to study the magnitude of the genetic influence on FPG variation by conducting structural equation modelling analysis and to further identify specific genetic variants potentially related to FPG levels by performing a genome-wide association study (GWAS) in Chinese twins.

Results

The final sample included 382 twin pairs: 139 dizygotic (DZ) pairs and 243 monozygotic (MZ) pairs. The DZ twin correlation for the FPG level (r_DZ = 0.20, 95% CI: 0.04–0.36) was much lower than half that of the MZ twin correlation (r_MZ = 0.68, 95% CI: 0.62–0.74). For the variation in FPG level, the AE model was the better fitting model, with additive genetic parameters (A) accounting for 67.66% (95% CI: 60.50–73.62%) and unique environmental or residual parameters (E) accounting for 32.34% (95% CI: 26.38–39.55%), respectively. In the GWAS, although no genetic variants reached the genome-wide significance level (P < 5 × 10^− 8), 28 SNPs exceeded the level of a suggestive association (P < 1 × 10^− 5). One promising genetic region (2q33.1) around rs10931893 (P = 1.53 × 10^− 7) was found. After imputing untyped SNPs, we found that rs60106404 (P = 2.38 × 10^− 8) located at SPATS2L reached the genome-wide significance level, and 216 SNPs exceeded the level of a suggestive association. We found 1007 genes nominally associated with the FPG level (P < 0.05), including SPATS2L, KCNK5, ADCY5, PCSK1, PTPRA, and SLC26A11. Moreover, C1orf74 (P = 0.014) and SLC26A11 (P = 0.021) were differentially expressed between patients with impaired fasting glucose and healthy controls. Some important enriched biological pathways, such as β-alanine metabolism, regulation of insulin secretion, glucagon signaling in metabolic regulation, IL-1 receptor pathway, signaling by platelet derived growth factor, cysteine and methionine metabolism pathway, were identified.

Conclusions

The FPG level is highly heritable in the Chinese population, and genetic variants are significantly involved in regulatory domains, functional genes and biological pathways that mediate FPG levels. This study provides important clues for further elucidating the molecular mechanism of glucose homeostasis and discovering new diagnostic biomarkers and therapeutic targets for diabetes.

Genomic Heritabilities and Correlations of 17 Traits Related to Obesity and Associated Conditions in the Japanese Population

Over the past few decades, obesity has become a public health issue of global concern. Even though disparities exist between human populations, e.g., the higher liver fat content of the Japanese despite a lower body mass index (BMI), studies on the genetics of obesity still largely focus on populations of European descent, leading to a dearth of genetic data on non-European populations. In this context, this study aimed to establish a broad picture of the genetic attributes of the Japanese population, by examining a representative sample of 18,889 individuals participating in the Tohoku Medical Megabank Project cohort. We applied linear mixed model methods to 17 traits related to obesity and associated diseases to estimate the heritabilities explained by common genetic variants and the genetic correlations between each pair of traits. These analyses allowed us to quantify the SNP heritability of health indicators such as BMI (0.248 ± 0.032) and HDL cholesterol (0.324 ± 0.031), and to provide one of the few estimates of the SNP heritability of cystatin C in unrelated individuals (0.260 ± 0.025). We discuss potential differences between the Japanese and people of European ancestry with respect to the genetic correlations between urinary biomarkers and adiposity traits, for which large estimates were obtained. For instance, the genetic correlations between urine potassium level and the values for weight, BMI, waist circumference, and waist-to-height ratio ranged from 0.290 to 0.559, much higher than the corresponding estimates in the UK Biobank.

Transgenic overexpression of glutathione S-transferase μ-type 1 reduces hypertension and oxidative stress in the stroke-prone spontaneously hypertensive rat

BACKGROUND

Combined congenic breeding and microarray gene expression profiling previously identified glutathione S-transferase μ-type 1 (Gstm1) as a positional and functional candidate gene for blood pressure (BP) regulation in the stroke-prone spontaneously hypertensive (SHRSP) rat. Renal Gstm1 expression in SHRSP rats is significantly reduced when compared with normotensive Wistar Kyoto (WKY) rats. As Gstm1 plays an important role in the secondary defence against oxidative stress, significantly lower expression levels may be functionally relevant in the development of hypertension. The aim of this study was to investigate the role of Gstm1 in BP regulation and oxidative stress by transgenic overexpression of the Gstm1 gene.

METHOD

Two independent Gstm1 transgenic SHRSP lines were generated by microinjecting SHRSP embryos with a linear construct controlled by the EF-1α promoter encoding WKY Gstm1 cDNA [SHRSP-Tg(Gstm1)1 and SHRSP-Tg(Gstm1)2].

RESULTS

Transgenic rats exhibit significantly reduced BP and pulse pressure when compared with SHRSP [systolic: SHRSP 205.2 ± 3.7 mmHg vs. SHRSP-Tg(Gstm1)1 175.5 ± 1.6 mmHg and SHRSP-Tg(Gstm1)2 172 ± 3.2 mmHg, P < 0.001; pulse pressure: SHRSP 58.4 ± 0.73 mmHg vs. SHRSP-Tg(Gstm1)1 52.7 ± 0.19 mmHg and SHRSP-Tg(Gstm1)2 40.7 ± 0.53 mmHg, P < 0.001]. Total renal and aortic Gstm1 expression in transgenic animals was significantly increased compared with SHRSP [renal relative quantification (RQ): SHRSP-Tg(Gstm1)1 1.95 vs. SHRSP 1.0, P < 0.01; aorta RQ: SHRSP-Tg(Gstm1)1 2.8 vs. SHRSP 1.0, P < 0.05]. Renal lipid peroxidation (malondialdehyde: protein) and oxidized : reduced glutathione ratio levels were significantly reduced in both transgenic lines when compared with SHRSP [malondialdehyde: SHRSP 0.04 ± 0.009 μmol/l vs. SHRSP-Tg(Gstm1)1 0.024 ± 0.002 μmol/l and SHRSP-Tg(Gstm1)2 0.021 ± 0.002 μmol/l; (oxidized : reduced glutathione ratio): SHRSP 5.19 ± 2.26 μmol/l vs. SHRSP-Tg(Gstm1)1 0.17 ± 0.11 μmol/l and SHRSP-Tg(Gstm1)2 0.47 ± 0.22 μmol/l]. Transgenic SHRSP rats containing the WKY Gstm1 gene demonstrate significantly lower BP, reduced oxidative stress and improved levels of renal Gstm1 expression.

CONCLUSION

These data support the hypothesis that reduced renal Gstm1 plays a role in the development of hypertension.

Genetics of Equine Endocrine and Metabolic Disease

A role for a genetic contribution to equine metabolic syndrome (EMS) and pars pituitary intermedia dysfunction (PPID) has been hypothesized. Heritability estimates of EMS biochemical measurements were consistent with moderately to highly heritable traits. Further, genome-wide association analyses have identified hundreds of regions of the genome contributing to EMS and candidate variants have been identified. The genetics of PPID has not yet been proven. Continued research for the specific genetic risk factors for both EMS and PPID is crucial for gaining a better understanding of the pathophysiology of both conditions and allowing development of genetic tests.

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.

Estimating narrow-sense heritability using family data from admixed populations

Estimating total narrow-sense heritability in admixed populations remains an open question. In this work, we used extensive simulations to evaluate existing linear mixed-model frameworks for estimating total narrow-sense heritability in two population-based cohorts from Greenland, and compared the results with data from unadmixed individuals from Denmark. When our analysis focused on Greenlandic sib pairs, and under the assumption that shared environment among siblings has a negligible effect, the model with two relationship matrices, one capturing identity by descent and one capturing identity by state, returned heritability estimates close to the true simulated value, while using each of the two matrices alone led to downward biases. When phenotypes correlated with ancestry, heritability estimates were inflated. Based on these observations, we propose a PCA-based adjustment that recovers the true simulated heritability. We use this knowledge to estimate the heritability of ten quantitative traits from the two Greenlandic cohorts, and report differences such as lower heritability for height in Greenlanders compared with Europeans. In conclusion, narrow-sense heritability in admixed populations is best estimated when using a mixture of genetic relationship matrices on individuals with at least one first-degree relative included in the sample.

Heritability of 596 lipid species and genetic correlation with cardiovascular traits in the Busselton Family Heart Study

CVD is the leading cause of death worldwide, and genetic investigations into the human lipidome may provide insight into CVD risk. The aim of this study was to estimate the heritability of circulating lipid species and their genetic correlation with CVD traits. Targeted lipidomic profiling was performed on 4,492 participants from the Busselton Family Heart Study to quantify the major fatty acids of 596 lipid species from 33 classes. We estimated narrow-sense heritabilities of lipid species/classes and their genetic correlations with eight CVD traits: BMI, HDL-C, LDL-C, triglycerides, total cholesterol, waist-hip ratio, systolic blood pressure, and diastolic blood pressure. We report heritabilities and genetic correlations of new lipid species/subclasses, including acylcarnitine (AC), ubiquinone, sulfatide, and oxidized cholesteryl esters. Over 99% of lipid species were significantly heritable (h²: 0.06-0.50) and all lipid classes were significantly heritable (h²: 0.14-0.50). The monohexosylceramide and AC classes had the highest median heritabilities (h² = 0.43). The largest genetic correlation was between clinical triglycerides and total diacylglycerol (r_g = 0.88). We observed novel positive genetic correlations between clinical triglycerides and phosphatidylglycerol species (r_g: 0.64-0.82), and HDL-C and alkenylphosphatidylcholine species (r_g: 0.45-0.74). Overall, 51% of the 4,768 lipid species-CVD trait genetic correlations were statistically significant after correction for multiple comparisons. This is the largest lipidomic study to address the heritability of lipids and their genetic correlation with CVD traits. Future work includes identifying putative causal genetic variants for lipid species and CVD using genome-wide SNP and whole-genome sequencing data.

The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees

The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P < 5E-08) as the four P values resulted from the tested methods for any SNP were all below or all above 5E-08. Nevertheless, larger discrepancies were detected between logistic regression and LMMs-based models at suggestive level of significance (i.e., of 5E-08 ≤ P < 5E-06). The SNP effects estimated by the logistic regression models were not statistically different from those estimated by GLMMs that implemented Wald's test. However, several SNP effects were significantly different from their counterparts in LMMs analyses. We suggest that fitting GLMMs with Wald's test on a pre-selected subset of SNPs obtained from logistic regression models can ensure the balance between the speed of analyses and the accuracy of parameters.

Contribution of rare coding mutations in CD36 to type 2 diabetes and cardio-metabolic complications

We sequenced coding regions of the cluster of differentiation 36 (CD36) gene in 184 French individuals of European ancestry presenting simultaneously with type 2 diabetes (T2D), arterial hypertension, dyslipidemia, and coronary heart disease. We identified rare missense mutations (p.Pro191Leu/rs143150225 and p.Ala252Val/rs147624636) in two heterozygous cases. The two CD36 mutation carriers had no family history of T2D and no clustering of cardio-metabolic complications. While the p.Pro191Leu mutation was found in 84 heterozygous carriers from five ethnic groups from the genome aggregation database (global frequency: 0.0297%, N = 141,321), only one European carrier of the p.Ala252Val mutation was identified (global frequency: 0.00040%, N = 125,523). The Pro191 and Ala252 amino acids were not conserved (74.8% and 68.9% across 131 animal species, respectively). In vitro experiments showed that the two CD36 mutant proteins are expressed and trafficked to the plasma membrane where they bind modified low-density-lipoprotein (LDL) cholesterol as normal. However, molecular modelling of the recent CD36 crystal structure showed that Pro191 was located at the exit/entrance gate of the lipid binding chamber and Ala252 was in line with the chamber. Overall, our data do not support a major contribution of CD36 rare coding mutations to T2D and its cardio-metabolic complications in the French population.

Heritability analysis of nontraditional glycemic biomarkers in the Atherosclerosis Risk in Communities Study

Nontraditional glycemic biomarkers, including fructosamine, glycated albumin, and 1,5-anhydroglucitol (1,5-AG) are potential alternatives or complement to traditional measures of hyperglycemia. Genetic variants are associated with these biomarkers, but the heritability, or extent to which genetics control their variation, is not known. We estimated pedigree-based, SNP-based, and bivariate heritabilities for traditional glycemic biomarkers (fasting glucose, HbA1c), and nontraditional biomarkers (fructosamine, glycated albumin, 1,5-AG) among white participants in the Atherosclerosis Risk in Communities (ARIC) Study (N = 400 first-degree relatives from sibships, N = 5,575 unrelated individuals). Pedigree-based heritabilities (representing heritability from the entire genome) for nontraditional biomarkers were substantial (0.44-0.55) and comparable to HbA1c (0.34); the fasting glucose estimate was nonsignificant. SNP-based heritabilities (representing heritability from common variants) were lower than pedigree-based heritabilities for all biomarkers. Bivariate heritabilities showed shared genetics between fructosamine and glycated albumin (0.46 pedigree-based, 1.00 SNP-based) and glycated albumin and 1,5-AG (0.50 pedigree-based, 0.47 SNP-based). Genetic factors contribute to a considerable proportion of the variance of fructosamine, glycated albumin, and 1,5-AG and a portion of this heritability likely comes from common variants.

Genome-Wide Association Study of Metabolic Syndrome Reveals Primary Genetic Variants at CETP Locus in Indians

Indians, a rapidly growing population, constitute vast genetic heterogeneity to that of Western population; however they have become a sedentary population in past decades due to rapid urbanization ensuing in the amplified prevalence of metabolic syndrome (MetS). We performed a genome-wide association study (GWAS) of MetS in 10,093 Indian individuals (6,617 MetS and 3,476 controls) of Indo-European origin, that belong to our previous biorepository of The Indian Diabetes Consortium (INDICO). The study was conducted in two stages-discovery phase (N = 2,158) and replication phase (N = 7,935). We discovered two variants within/near the CETP gene-rs1800775 and rs3816117-associated with MetS at genome-wide significance level during replication phase in Indians. Additional CETP loci rs7205804, rs1532624, rs3764261, rs247617, and rs173539 also cropped up as modest signals in Indians. Haplotype association analysis revealed GCCCAGC as the strongest haplotype within the CETP locus constituting all seven CETP signals. In combined analysis, we perceived a novel and functionally relevant sub-GWAS significant locus-rs16890462 in the vicinity of SFRP1 gene. Overlaying gene regulatory data from ENCODE database revealed that single nucleotide polymorphism (SNP) rs16890462 resides in repressive chromatin in human subcutaneous adipose tissue as characterized by the enrichment of H3K27me3 and CTCF marks (repressive gene marks) and diminished H3K36me3 marks (activation gene marks). The variant displayed active DNA methylation marks in adipose tissue, suggesting its likely regulatory activity. Further, the variant also disrupts a potential binding site of a key transcription factor, NRF2, which is known for involvement in obesity and metabolic syndrome.

Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases

Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead to inaccuracies in fine-mapping, with stochastic search more robust than stepwise. We develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. MFM analysis of six immune-mediated diseases reveals causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.

Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family-based samples

Numerous methods for estimating heritability have been proposed; however, unlike quantitative phenotypes, heritability estimation for dichotomous phenotypes is computationally and statistically complex, and the use of heritability is infrequent. In this study, we developed a statistical method to estimate heritability of dichotomous phenotypes using a liability threshold model in the context of ascertained family-based samples. This model assumes that dichotomous phenotypes are determined by unobserved latent variables that are normally distributed and can be applied to general pedigree data. The proposed methods were applied to simulated data and Korean type-2 diabetes family-based samples, and the accuracy of the estimates provided by the experimental methods was compared with that of the established methods.

Unpacking the heritability of body mass index and other ratios

OBJECTIVES

Ratios of weight to height, especially body mass index (BMI = kg/m² ), are often used in epidemiological and genetic studies of health, but the limitations of quantitative genetic analysis of ratios are not widely known. The heritability of these ratios can be closely approximated from a bivariate quantitative genetic model of weight and height which clarifies how BMI heritabilities change.

METHODS

I explored this bivariate approximation and alternative measures through simulated datasets fit with linear mixed models. Simulated data were based on published heritabilities and other statistics for BMI and related anthropometric dimensions from four human samples.

RESULTS

Inspection of the bivariate approximation and analysis of simulated data show the heritability of weight/height crucially depends on the phenotypic (r_P ) and genetic correlations (r_A ) between weight and height. Changes in these correlations can have dramatic effects on the heritability of BMI. For example, when r_P  ≪ r_A heritability of BMI is reduced to 35-50% of its value when the correlations are equal.

DISCUSSION

Increasing adiposity likely decreases the phenotypic correlations more than the genetic correlation resulting in reduced heritability of the ratio. This contrasts with the commonly reported stability or increase of BMI heritability and implies it may result from increased genetic variance in weight in obesogenic environments. The bivariate model offers other advantages over ratios, including estimating the conditional genetic variance or heritability of weight that is unassociated with height, which may prove useful in quantitative and molecular genetic studies.

Estimation of metabolic syndrome heritability in three large populations including full pedigree and genomic information

Metabolic syndrome is a complex human disorder characterized by a cluster of conditions (increased blood pressure, hyperglycemia, excessive body fat around the waist, and abnormal cholesterol or triglyceride levels). Any of these conditions increases the risk of serious disorders such as diabetes or cardiovascular disease. Currently, the degree of genetic regulation of this syndrome is under debate and partially unknown. The principal aim of this study was to estimate the genetic component and the common environmental effects in different populations using full pedigree and genomic information. We used three large populations (Gubbio, ARIC, and Ogliastra cohorts) to estimate the heritability of metabolic syndrome. Due to both pedigree and genotyped data, different approaches were applied to summarize relatedness conditions. Linear mixed models (LLM) using average information restricted maximum likelihood (AIREML) algorithm were applied to partition the variances and estimate heritability (h²) and common sib-household effect (c²). Globally, results obtained from pedigree information showed a significant heritability (h²: 0.286 and 0.271 in Gubbio and Ogliastra, respectively), whereas a lower, but still significant heritability was found using SNPs data ([Formula: see text]: 0.167 and 0.254 in ARIC and Ogliastra). The remaining heritability between h² and [Formula: see text] ranged between 0.031 and 0.237. Finally, the common environmental c² in Gubbio and Ogliastra were also significant accounting for about 11% of the phenotypic variance. Availability of different kinds of populations and data helped us to better understand what happened when heritability of metabolic syndrome is estimated and account for different possible confounding. Furthermore, the opportunity of comparing different results provided more precise and less biased estimation of heritability.

Partitioning the Pleiotropy Between Coronary Artery Disease and Body Mass Index Reveals the Importance of Low Frequency Variants and Central Nervous System-Specific Functional Elements

BACKGROUND

The objective of this study is to investigate the extent and nature of pleiotropy between coronary artery disease (CAD) and body mass index (BMI).

METHODS

We examined the contribution of genome-wide single-nucleotide polymorphisms (minor allele frequency ≥0.01) to co-occurrence of CAD and BMI in a sample of genetically unrelated 8041 subjects (genetic resemblance ≤0.025) of European ancestry using mixed-linear-models. We further partitioned the estimated pleiotropy according to biological features to gain insight into the nature of pleiotropy between CAD and BMI.

RESULTS

We found significant (P<0.0001) positive genetic correlation between CAD and BMI (r_g =0.60). The estimated pleiotropy explained 68% of phenotypic correlation, and it was not proportionally distributed across the chromosomes; notably, chromosome 10 contributed more; whereas, chromosomes 11 and 14 contributed less to pleiotropy than expected given their chromosomal length. We noted that a large proportion (63%; P=0.002) of the pleiotropy is attributed to single-nucleotide polymorphisms with low allele frequency (minor allele frequency <0.05). Of note, pleiotropy was enriched among central nervous system genes and genes of metabolic pathways. Further analyses revealed that these effects are more pronounced in the proopiomelanocortin pathway and genes involved in carbohydrate metabolism. After genome-wide association study meta-analysis, only single-nucleotide polymorphisms downstream of the MC4R gene were found concordantly associated with (P<5×10^-8) BMI and CAD with lead single-nucleotide polymorphism being rs663129 (combined P=2.7×10^-65). Finally, partitioning the pleiotropy according to functional elements pointed to the importance of superenhancers and notably brain-specific superenhancers.

CONCLUSIONS

Genome-wide pleiotropy substantially contributes to co-occurrence of CAD and obesity, and it is highly enriched among low frequency variants and central nervous system-specific functional elements.

Using DNA to predict educational trajectories in early adulthood

At the end of compulsory schooling, young adults decide on educational and occupational trajectories that impact their subsequent employability, health and even life expectancy. To understand the antecedents to these decisions, we follow a new approach that considers genetic contributions, which have largely been ignored before. Using genomewide polygenic scores (EA3) from the most recent genomewide association study of years of education in 1.1 million individuals, we tested for genetic influence on early adult decisions in a United Kingdom-representative sample of 5,839 at 18 years of age. EA3 significantly predicted educational trajectories in early adulthood (Nagelkerke R2 = 10%), χ2(4) = 571.77, p < .001, indicating that young adults partly adapt their aspirations to their genetic propensities-a concept known as gene-environment correlation. Compared to attending university, a 1 standard deviation increase in EA3 was associated on average with a 51% reduction in the odds of pursuing full-time employment (OR = .47; 95% CI [.43, .51]); an apprenticeship (OR = .49; 95% CI [.45, .54]); or not going in education, employment, or training (OR = .50; 95% CI [.41, .60]). EA3 associations were attenuated when controlling for previous academic achievement and family socioeconomic status. Overall this research illustrates how DNA-based predictions offer novel opportunities for studying the sociodevelopmental structures of life outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Replicability analysis in genome-wide association studies via Cartesian hidden Markov models

BACKGROUND

Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability analysis. In this paper, we propose a novel multiple testing procedure based on the Cartesian hidden Markov model (CHMM), called repLIS procedure, for replicability analysis across two studies, which can characterize the local dependence structure among adjacent SNPs via a four-state Markov chain.

RESULTS

Theoretical results show that the repLIS procedure can control the false discovery rate (FDR) at the nominal level α and is shown to be optimal in the sense that it has the smallest false non-discovery rate (FNR) among all α-level multiple testing procedures. We carry out simulation studies to compare our repLIS procedure with the existing methods, including the Benjamini-Hochberg (BH) procedure and the empirical Bayes approach, called repfdr. Finally, we apply our repLIS procedure and repfdr procedure in the replicability analyses of psychiatric disorders data sets collected by Psychiatric Genomics Consortium (PGC) and Wellcome Trust Case Control Consortium (WTCCC). Both the simulation studies and real data analysis show that the repLIS procedure is valid and achieves a higher efficiency compared with its competitors.

CONCLUSIONS

In replicability analysis, our repLIS procedure controls the FDR at the pre-specified level α and can achieve more efficiency by exploiting the dependency information among adjacent SNPs.

Gene-environment correlations in parental emotional warmth and intolerance: genome-wide analysis over two generations of the Young Finns Study

BACKGROUND

Genomic analysis of the child might offer new potential to illuminate human parenting. We examined whether offspring (G2) genome-wide genotype variation (SNPs) is associated with their mother's (G1) emotional warmth and intolerance, indicating a gene-environment correlation. If this association is stronger than between G2's genes and their emotional warmth and intolerance toward their own children, then this would indicate the presence of an evocative gene-environment correlation. To further understand how G1 mother's parenting has been evoked by genetically influenced characteristics of the child (G2), we examined whether child (G2) temperament partially accounted for the association between offspring genes and parental responses.

METHODS

Participants were from the Young Finns Study. G1 mothers (N = 2,349; mean age 39 years) self-reported the emotional warmth and intolerance toward G2 in 1980 when the participants were from 3 to 18 years old. G2 participants answered the same parenting scales in 2007/2012 (N = 1,378; mean age = 38 years in 2007; 59% female) when their children were on average 11 years old. Offspring temperament traits were self-reported in 1992 (G2 age range 15-30 years). Estimation of the phenotypic variance explained by the SNPs of G2 was done by genome-wide complex trait analysis with restricted maximum likelihood (GCTA-GREML).

RESULTS

Results showed that the SNPs of a child (G2) explained 22.6% of the phenotypic variance of maternal intolerance (G1; p-value = .039). G2 temperament trait negative emotionality explained only 2.4% points of this association. G2 genes did not explain G1 emotional warmth or G2's own emotional warmth and intolerance. However, further analyses of a combined measure of both G1 parenting scales found genetic effects. Parent or child gender did not moderate the observed associations.

CONCLUSIONS

Presented genome-wide evidence is pointing to the important role a child plays in affecting and shaping his/her family environment, though the underlying mechanisms remain unclear.

Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies

To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

Multivariate analysis of genomics data to identify potential pleiotropic genes for type 2 diabetes, obesity and dyslipidemia using Meta-CCA and gene-based approach

Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value < 0.05). Further, to refine the identified genes, we tested their relationship to the six correlated traits using VEGAS-2 (versatile gene-based association study-2). Only the genes significantly associated (Bonferroni corrected p value < 0.05) with more than one trait were kept. Finally, 25 genes (including two confirmed pleiotropic genes and eleven novel pleiotropic genes) were identified as potential pleiotropic genes. They were enriched in 5 pathways including the statin pathway and the PPAR (peroxisome proliferator-activated receptor) Alpha pathway. In summary, our study identified potential pleiotropic genes and pathways of type 2 diabetes, obesity and dyslipidemia, which may shed light on the common biological etiology and pathogenesis of these three disorders and provide promising insights for new therapies.

Regulation of Aldosterone Secretion

Secretion of the major mineralocorticoid aldosterone from the adrenal cortex is a tightly-regulated process enabling this hormone to regulate sodium homeostasis and thereby contribute to blood pressure control. The circulating level of aldosterone is the result of various regulatory mechanisms, the most significant being those controlled by the renin-angiotensin system and plasma potassium levels. The importance of maintaining tight control over aldosterone secretion is demonstrated by cases of dysregulation, which can result in severe hypertension and significantly increased cardiovascular risk. In this article we summarize current knowledge of the major regulatory mechanisms, focusing particularly on the systems operating within the adrenocortical zona glomerulosa cells; we also describe some of the other factors that influence aldosterone production to a lesser but still significant extent. Finally, we discuss the influence of common genetic polymorphisms on aldosterone secretion in large sections of the population and also the emerging role of microRNA as significant regulators of this system.

Trans-Ethnic Polygenic Analysis Supports Genetic Overlaps of Lumbar Disc Degeneration With Height, Body Mass Index, and Bone Mineral Density

Lumbar disc degeneration (LDD) is age-related break-down in the fibrocartilaginous joints between lumbar vertebrae. It is a major cause of low back pain and is conventionally assessed by magnetic resonance imaging (MRI). Like most other complex traits, LDD is likely polygenic and influenced by both genetic and environmental factors. However, genome-wide association studies (GWASs) of LDD have uncovered few susceptibility loci due to the limited sample size. Previous epidemiology studies of LDD also reported multiple heritable risk factors, including height, body mass index (BMI), bone mineral density (BMD), lipid levels, etc. Genetics can help elucidate causality between traits and suggest loci with pleiotropic effects. One such approach is polygenic score (PGS) which summarizes the effect of multiple variants by the summation of alleles weighted by estimated effects from GWAS. To investigate genetic overlaps of LDD and related heritable risk factors, we calculated the PGS of height, BMI, BMD and lipid levels in a Chinese population-based cohort with spine MRI examination and a Japanese case-control cohort of lumbar disc herniation (LDH) requiring surgery. Because most large-scale GWASs were done in European populations, PGS of corresponding traits were created using weights from European GWASs. We calibrated their prediction performance in independent Chinese samples, then tested associations with MRI-derived LDD scores and LDH affection status. The PGS of height, BMI, BMD and lipid levels were strongly associated with respective phenotypes in Chinese, but phenotype variances explained were lower than in Europeans which would reduce the power to detect genetic overlaps. Despite of this, the PGS of BMI and lumbar spine BMD were significantly associated with LDD scores; and the PGS of height was associated with the increased the liability of LDH. Furthermore, linkage disequilibrium score regression suggested that, osteoarthritis, another degenerative disorder that shares common features with LDD, also showed genetic correlations with height, BMI and BMD. The findings suggest a common key contribution of biomechanical stress to the pathogenesis of LDD and will direct the future search for pleiotropic genes.

An Epigenome-Wide Association Study of Obesity-Related Traits

We conducted an epigenome-wide association study on obesity-related traits. We used data from 2 prospective, population-based cohort studies: the Rotterdam Study (RS) (2006-2013) and the Atherosclerosis Risk in Communities (ARIC) Study (1990-1992). We used the RS (n = 1,450) as the discovery panel and the ARIC Study (n = 2,097) as the replication panel. Linear mixed-effect models were used to assess the cross-sectional associations between genome-wide DNA methylation in leukocytes and body mass index (BMI) and waist circumference (WC), adjusting for sex, age, smoking, leukocyte proportions, array number, and position on array. The latter 2 variables were modeled as random effects. Fourteen 5'-C-phosphate-G-3' (CpG) sites were associated with BMI and 26 CpG sites with WC in the RS after Bonferroni correction (P < 1.07 × 10-7), of which 12 and 13 CpGs were replicated in the ARIC Study, respectively. The most significant novel CpGs were located on the Musashi RNA binding protein 2 gene (MSI2; cg21139312) and the leucyl-tRNA synthetase 2, mitochondrial gene (LARS2; cg18030453) and were associated with both BMI and WC. CpGs at BRDT, PSMD1, IFI44L, MAP1A, and MAP3K5 were associated with BMI. CpGs at LGALS3BP, MAP2K3, DHCR24, CPSF4L, and TMEM49 were associated with WC. We report novel associations between methylation at MSI2 and LARS2 and obesity-related traits. These results provide further insight into mechanisms underlying obesity-related traits, which can enable identification of new biomarkers in obesity-related chronic diseases.

Breastfeeding and Risk of Metabolic Syndrome in Children and Adolescents: A Systematic Review

BACKGROUND

The beneficial effect of breastfeeding on individual components of the metabolic syndrome in children and adolescents has been reported, but it is unknown if there is an association between being breastfed and metabolic syndrome as a whole. Research aim: This systematic review was performed to assess quality and strength of evidence for the association between being breastfed and the development of metabolic syndrome in children and adolescents.

METHODS

Articles were obtained from searches using PubMed and Embase databases, as well as from secondary searches through reference lists. Study quality was assessed using a three-level quality rating system.

RESULTS

Of 11 studies reviewed, 7 found a protective association between breastfeeding and metabolic syndrome and 4 found no association. There was no clear dose-response relationship between duration of breastfeeding and metabolic syndrome risk and insufficient evidence to demonstrate an added effect of being exclusively breastfed. The overall quality of the articles was moderate. In general, lower quality articles found no significant association, whereas higher quality articles found a significant association.

CONCLUSION

Our review demonstrated a limited amount of high-quality research on the relationship between being breastfed and development of metabolic syndrome in children and adolescents. The evidence presented in this review suggests that being breastfed may be protective against metabolic syndrome, but further research with improvements in study design, such as improved measurement of breastfeeding and the use of prospectively collected data, will improve our understanding of this relationship.