Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations

Interaction between genetic risk score and dietary fat intake on lipid-related traits in Brazilian young adults

The occurrence of dyslipidaemia, which is an established risk factor for cardiovascular diseases, has been attributed to multiple factors including genetic and environmental factors. We used a genetic risk score (GRS) to assess the interactions between genetic variants and dietary factors on lipid-related traits in a cross-sectional study of 190 Brazilians (mean age: 21 ± 2 years). Dietary intake was assessed by a trained nutritionist using three 24-h dietary recalls. The high GRS was significantly associated with increased concentration of TAG (beta = 0·10 mg/dl, 95 % CI 0·05-0·16; P < 0·001), LDL-cholesterol (beta = 0·07 mg/dl, 95 % CI 0·04, 0·11; P < 0·0001), total cholesterol (beta = 0·05 mg/dl, 95 % CI: 0·03, 0·07; P < 0·0001) and the ratio of TAG to HDL-cholesterol (beta = 0·09 mg/dl, 95 % CI: 0·03, 0·15; P = 0·002). Significant interactions were found between the high GRS and total fat intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03) and between the high GRS and SFA intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03). A high intake of total fat (>31·5 % of energy) and SFA (>8·6 % of energy) was associated with higher TAG:HDL-cholesterol ratio in individuals with the high GRS (beta = 0·14, 95 % CI: 0·06, 0·23; P < 0·001 for total fat intake; beta = 0·13, 95 % CI: 0·05, 0·22; P = 0·003 for SFA intake). Our study provides evidence that the genetic risk of high TAG:HDL-cholesterol ratio might be modulated by dietary fat intake in Brazilians, and these individuals might benefit from limiting their intake of total fat and SFA.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

Association Between rs2278426 Polymorphism of the ANGPTL8 Gene and Polycystic Ovary Syndrome

Purpose

To study the relationship between the single nucleotide polymorphism (SNP) rs2278426 in the angiopoietin-like protein 8 gene (ANGPTL8) and polycystic ovary syndrome (PCOS).

Patients and methods

A total of 122 patients with PCOS and 108 controls were recruited for comparison of glucose, lipid, insulin, sex hormone, and ANGPTL8 levels. Polymerase chain reaction (PCR) and gene sequencing were performed for comparison of the frequency of the CC, CT, and TT rs2278426 genotypes and the rs2278426 allele distributions between the PCOS and control groups and between the obese and non-obese subgroups of the PCOS and control groups.

Results

The frequency of the T allele was significantly higher in the PCOS group than that in the controls (P = 0.037). In the dominant genetic model, the proportion of the CT+TT genotype in the PCOS group was significantly higher than that in the controls (P = 0.047). Subgroup analysis demonstrated that the T allele proportion was significantly higher in obese PCOS group than obese control group (P = 0.027). PCOS with the CT+TT genotype had significantly higher body mass index (BMI; P = 0.001), triglyceride (TG; P = 0.005), homeostasis model assessment of insulin resistance (HOMA-IR; P = 0.035), testosterone (P = 0.041), and ANGPTL8 (P = 0.037) levels and significantly lower high-density lipoprotein (HDL) levels (P = 0.025) than PCOS with the CC genotype. Obese PCOS group with the CT+TT genotype had significantly higher TG (P = 0.015), luteinizing hormone (LH; P = 0.030), fasting insulin (FINS; P = 0.039), HOMA-IR (P = 0.018), and ANGPTL8 (P = 0.049) levels than obese PCOS group with the CC genotype.

Conclusion

Polymorphisms of rs2278426 may induce glycolipid metabolic disorders by affecting ANGPTL8 levels and functions in Han Chinese females with obesity from the Shandong region, increasing the risk of PCOS in this population.

Prevalence and risk factors of diabetic peripheral neuropathy: A population-based cross-sectional study in China

AIMS

To assess the prevalence of diabetic peripheral neuropathy (DPN) and its risk factors in the type 2 diabetes mellitus (T2DM) population.

METHODS

This cross-sectional study enroled patients with T2DM between July and December 2017 from 24 provinces in China. Diabetic peripheral neuropathy and its severity were assessed by the Toronto clinical scoring system, neuropathy symptoms score (NSS) and neuropathy disability score. The prevalence of DPN and its risk factors were analysed.

RESULTS

A total of 14,908 patients with T2DM were enroled. The prevalence of DPN was 67.6%. Among 10,084 patients with DPN, 4808 (47.7%), 3325 (33.0%), and 1951 (19.3%) had mild, moderate, and severe DPN, respectively. The prevalence of DPN in females was higher than in males (69.0% vs. 66.6%, P = 0.002). The prevalence of DPN increased with age and course of diabetes and decreased with body mass index (BMI) and education level (all P for trend <0.05). The comorbidities and complications in patients with DPN were higher than in those without DPN, including hypertension, myocardial infarction, diabetic retinopathy, and diabetic nephropathy (all P < 0.001). Age, hypertension, duration of diabetes, diabetic retinopathy, diabetic nephropathy, glycated haemoglobin, high-density lipoprotein cholesterol, and lower estimated glomerular filtration rate were positively associated with DPN, while BMI, education level, fasting C-peptide, and uric acid were negatively associated with DPN.

CONCLUSIONS

Among patients with T2DM in China, the prevalence of DPN is high, especially in the elderly, low-income, and undereducated patients.

Strategies for the Genomic Analysis of Admixed Populations

Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.

Prevalence and clinical correlates of abnormal lipid metabolism in first-episode and drug-naïve patients with major depressive disorder: A large-scale cross-sectional study

OBJECTIVE

Studies have shown an association between abnormal lipid profiles and MDD, but there are few studies on the clinical correlates of lipid abnormalities in patients with major depressive disorder (MDD). The purpose of this study was to investigate the prevalence of abnormal lipid metabolism and its correlates in Chinese first-episode and drug-naïve MDD patients, which has not yet been reported.

METHODS

A total of 1718 outpatients with first-episode and drug-naïve MDD were included. Demographic data were collected by a standardized questionnaire and blood lipid levels were measured, including total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL-C), high density lipoprotein (HDL-C). The Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Positive and Negative Syndrome Scale (PANSS) positive subscale, and Clinical Global Impression of Severity Scale (CGI-S) were assessed for each patient.

RESULTS

The prevalence of abnormal lipid metabolism was 72.73% (1301/1718), and the rates of high TC, high TG, high LDL-C and low HDL-C were 51.05% (877/1718), 61.18% (1051/1718), 30.09% (517/1718), 23.40% (402/1718), respectively. Logistic regression showed the risk factors for abnormal lipid metabolism were severe anxiety, HAMD score, CGI-S score, BMI and systolic blood pressure (SBP). Multiple linear regression analysis showed that age at onset, SBP, HAMD score, HAMA score, PANSS positive subscale score, and CGI-S were independently associated with TC levels. BMI, HAMD score, PANSS positive subscale score and CGI-S score were independently associated with TG levels. SBP, HAMD score, PANSS positive subscale score and CGI-S score were independently associated with LDL-C levels. Age of onset, SBP and CGI-S score were independently associated with HDL-C levels.

CONCLUSIONS

The prevalence of abnormal lipid metabolism in first-episode and drug-naïve MDD patients is quite high. The severity of psychiatric symptoms may be closely associated with the presence of abnormal lipid metabolism in patients with MDD.

Similarity and diversity of genetic architecture for complex traits between East Asian and European populations

BACKGROUND

Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently.

RESULTS

By leveraging summary statistics of 37 traits from East Asian (N_max=254,373) or European (N_max=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρ_g) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with [Formula: see text] ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs.

CONCLUSIONS

Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.

Epigenome-wide association study of plasma lipids in West Africans: the RODAM study

BACKGROUND

DNA-methylation has been associated with plasma lipid concentration in populations of diverse ethnic backgrounds, but epigenome-wide association studies (EWAS) in West-Africans are lacking. The aim of this study was to identify DNA-methylation loci associated with plasma lipids in Ghanaians.

METHODS

We conducted an EWAS using Illumina 450k DNA-methylation array profiles of extracted DNA from 663 Ghanaian participants. Differentially methylated positions (DMPs) were examined for association with plasma total cholesterol (TC), LDL-cholesterol, HDL-cholesterol, and triglycerides concentrations using linear regression models adjusted for age, sex, body mass index, diabetes mellitus, and technical covariates. Findings were replicated in independent cohorts of different ethnicities.

FINDINGS

We identified one significantly associated DMP with triglycerides (cg19693031 annotated to TXNIP, regression coefficient beta -0.26, false discovery rate adjusted p-value 0.001), which replicated in-silico in South African Batswana, African American, and European populations. From the top five DMPs with the lowest nominal p-values, two additional DMPs for triglycerides (CPT1A, ABCG1), two DMPs for LDL-cholesterol (EPSTI1, cg13781819), and one for TC (TXNIP) replicated. With the exception of EPSTI1, these loci are involved in lipid transport/metabolism or are known GWAS-associated loci. The top 5 DMPs per lipid trait explained 9.5% in the variance of TC, 8.3% in LDL-cholesterol, 6.1% in HDL-cholesterol, and 11.0% in triglycerides.

INTERPRETATION

The top DMPs identified in this study are in loci that play a role in lipid metabolism across populations, including West-Africans. Future studies including larger sample size, longitudinal study design and translational research is needed to increase our understanding on the epigenetic regulation of lipid metabolism among West-African populations.

FUNDING

European Commission under the Framework Programme (grant number: 278901).

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Background

Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.

Methods

By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement.

Results

Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.

Conclusion

Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03637-8.

Impact of Lipid Genetic Risk Score and Saturated Fatty Acid Intake on Central Obesity in an Asian Indian Population

Abnormalities in lipid metabolism have been linked to the development of obesity. We used a nutrigenetic approach to establish a link between lipids and obesity in Asian Indians, who are known to have a high prevalence of central obesity and dyslipidaemia. A sample of 497 Asian Indian individuals (260 with type 2 diabetes and 237 with normal glucose tolerance) (mean age: 44 ± 10 years) were randomly chosen from the Chennai Urban Rural Epidemiological Study (CURES). Dietary intake was assessed using a previously validated questionnaire. A genetic risk score (GRS) was constructed based on cholesteryl ester transfer protein (CETP) and lipoprotein lipase (LPL) genetic variants. There was a significant interaction between GRS and saturated fatty acid (SFA) intake on waist circumference (WC) (Pinteraction = 0.006). Individuals with a low SFA intake (≤23.2 g/day), despite carrying ≥2 risk alleles, had a smaller WC compared to individuals carrying <2 risk alleles (Beta = −0.01 cm; p = 0.03). For those individuals carrying ≥2 risk alleles, a high SFA intake (>23.2 g/day) was significantly associated with a larger WC than a low SFA intake (≤23.2 g/day) (Beta = 0.02 cm, p = 0.02). There were no significant interactions between GRS and other dietary factors on any of the measured outcomes. We conclude that a diet low in SFA might help reduce the genetic risk of central obesity confirmed by CETP and LPL genetic variants. Conversely, a high SFA diet increases the genetic risk of central obesity in Asian Indians.

Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits

Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10-8). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10-8). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data.

Genetic Variants and Haplotypes of TOMM40, APOE, and APOC1 are Related to the Age of Onset of Late-onset Alzheimer Disease in a Colombian Population

BACKGROUND

The Apolipoprotein E (APOE) gene is the main risk factor for late-onset Alzheimer disease (LOAD). Genetic variants and haplotypes in regions near the APOE locus may be associated with LOAD in the Colombian population.

OBJECTIVE

We evaluated frequencies and risk of genetic variants and haplotypes in APOE, TOMM40, and APOC1 promoters, also in putative regulatory enhancer elements (TOMM40 IVS2-4 and TOMM40 IVS6), and in cis-regulatory elements (ME1 and BCR).

MATERIALS AND METHODS

Our case-control association study was carried out in 50 patients with LOAD and 50 controls. We determined frequencies and odd ratios for genetic variants and haplotypes.

RESULTS

We found a significant association between LOAD and genetic variants at the TOMM40 promoter, at TOMM40 IVS2-4 and TOMM40 IVS6 regulatory enhancer elements, and at the APOC1 promoter. Particularly, variants of Poly-T and APOC1 promoter could anticipate the age of onset of LOAD in our population. We identified three risk haplotypes in TOMM40 (ACGGAG, ACGGGG, and ATAGGC) related to LOAD's age of onset. We also found other risk or protection haplotypes at the TOMM40 and APOE promoters, at TOMM40 IVS2-4, TOMM40 IVS6 regulatory enhancer elements, and at ME1.

CONCLUSION

Genetic variants and haplotypes near the APOE locus are related to LOAD risk and accelerated onset of LOAD in the Colombian population.

Genetic European Ancestry and Incident Diabetes in Black Individuals: Insights From the SPRINT Trial

BACKGROUND

Black individuals have high incident diabetes risk, despite having paradoxically lower triglyceride and higher HDL (high-density lipoprotein) cholesterol levels. The basis of this is poorly understood. We evaluated the participants of SPRINT (Systolic Blood Pressure Intervention Trial) to assess the association of estimated European genetic ancestry with the risk of incident diabetes in self-identified Black individuals.

METHODS

Self-identified non-Hispanic Black SPRINT participants free of diabetes at baseline were included. Black participants were stratified into tertiles (T1-T3) of European ancestry proportions estimated using 106 biallelic ancestry informative genetic markers. The multivariable-adjusted association of European ancestry proportion with indices of baseline metabolic syndrome (ie, fasting plasma glucose, triglycerides, HDL cholesterol, body mass index, and blood pressure) was assessed. Multivariable-adjusted Cox regression determined the risk of incident diabetes (fasting plasma glucose ≥126 mg/dL or self-reported diabetes treatment) across tertiles of European ancestry proportion.

RESULTS

Among 2466 Black SPRINT participants, a higher European ancestry proportion was independently associated with higher baseline triglyceride and lower HDL cholesterol levels (P<0.001 for both). European ancestry proportion was not associated with baseline fasting plasma glucose, body mass index, and blood pressure (P>0.05). Compared with the first tertile, those in the second (hazard ratio, 0.64 [95% CI, 0.45-0.90]) and third tertiles (hazard ratio, 0.61 [95% CI, 0.44-0.89]) of the European ancestry proportion had a lower risk of incident diabetes. A 5% point higher European ancestry was associated with a 29% lower risk of incident diabetes (hazard ratio, 0.71 [95% CI, 0.55-0.93]). There was no evidence of a differential association between the European ancestry proportion tertiles and incident diabetes between those randomized to intensive versus standard blood pressure treatment.

CONCLUSIONS

The higher risk of incident diabetes in Black individuals may have genetic determinants in addition to adverse social factors. Further research may help understand the interplay between biological and social determinants of cardiometabolic health in Black individuals. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01206062.

A Nutrigenetic Update on CETP Gene–Diet Interactions on Lipid-Related Outcomes

Purpose of Review

An abnormal lipid profile is considered a main risk factor for cardiovascular diseases and evidence suggests that single nucleotide polymorphisms (SNPs) in the cholesteryl ester transfer protein (CETP) gene contribute to variations in lipid levels in response to dietary intake. The objective of this review was to identify and discuss nutrigenetic studies assessing the interactions between CETP SNPs and dietary factors on blood lipids.

Recent Findings

Relevant articles were obtained through a literature search of PubMed and Google Scholar through to July 2021. An article was included if it examined an interaction between CETP SNPs and dietary factors on blood lipids. From 49 eligible nutrigenetic studies, 27 studies reported significant interactions between 8 CETP SNPs and 17 dietary factors on blood lipids in 18 ethnicities. The discrepancies in the study findings could be attributed to genetic heterogeneity, and differences in sample size, study design, lifestyle and measurement of dietary intake. The most extensively studied ethnicities were those of Caucasian populations and majority of the studies reported an interaction with dietary fat intake. The rs708272 (TaqIB) was the most widely studied CETP SNP, where ‘B1’ allele was associated with higher CETP activity, resulting in lower high-density lipoprotein cholesterol and higher serum triglycerides under the influence of high dietary fat intake.

Summary

Overall, the findings suggest that CETP SNPs might alter blood lipid profiles by modifying responses to diet, but further large studies in multiple ethnic groups are warranted to identify individuals at risk of adverse lipid response to diet.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11883-022-00987-y.

Indirect Genetic Effects of ADIPOQ Variants on Lipid Levels in a Sibling Study of a Rural Chinese Population

Variations in lipid levels are the result of combinations of genetic and environmental factors. We aim to investigate the indirect effect between siblings of the three polymorphisms of ADIPOQ on serum lipid levels in rural Chinese populations. A total of 2571 sibling pairs were enrolled as study participants. A generalized estimating equation was used to accommodate a family-based design. We used stratified analysis to detect sex combination differences in the indirect genetic effect. We found a significant association between the number of altered risk alleles of rs182052 and ego lipid levels of TG (β = 0.177, P = 0.003), TC (β = 0.140, P = 0.004) and LDL-C (β = 0.098, P = 0.014). Ego and altered genotypes of rs182052 demonstrated a joint effect on ego lipid levels of TC (β = 0.212, P = 0.019), HDL-C (β = 0.099, P = 0.002) and LDL-C (β = 0.177, P = 0.013) in recessive inheritance mode. In opposite-sex siblings, the altered GG genotype of rs182052 increased the ego lipid levels. Thus, our findings demonstrate that ADIPOQ has an indirect genetic effect on lipid levels in sibling pairs, and there are sex-combination differences in the indirect genetic effect in siblings.

Bench Research Informed by GWAS Results

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

African genetic diversity and adaptation inform a precision medicine agenda

The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.

Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Genome-Wide Identification of Rare and Common Variants Driving Triglyceride Levels in a Nevada Population

Clinical conditions correlated with elevated triglyceride levels are well-known: coronary heart disease, hypertension, and diabetes. Underlying genetic and phenotypic mechanisms are not fully understood, partially due to lack of coordinated genotypic-phenotypic data. Here we use a subset of the Healthy Nevada Project, a population of 9,183 sequenced participants with longitudinal electronic health records to examine consequences of altered triglyceride levels. Specifically, Healthy Nevada Project participants sequenced by the Helix Exome+ platform were cross-referenced to their electronic medical records to identify: (1) rare and common single-variant genome-wide associations; (2) gene-based associations using a Sequence Kernel Association Test; (3) phenome-wide associations with triglyceride levels; and (4) pleiotropic variants linked to triglyceride levels. The study identified 549 significant single-variant associations (p < 8.75 × 10-9), many in chromosome 11's triglyceride hotspot: ZPR1, BUD13, APOC3, APOA5. A well-known protective loss-of-function variant in APOC3 (R19X) was associated with a 51% decrease in triglyceride levels in the cohort. Sixteen gene-based triglyceride associations were identified; six of these genes surprisingly did not include a single variant with significant associations. Results at the variant and gene level were validated with the UK Biobank. The combination of a single-variant genome-wide association, a gene-based association method, and phenome wide-association studies identified rare and common variants, genes, and phenotypes associated with elevated triglyceride levels, some of which may have been overlooked with standard approaches.

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.

Genetic and Non-genetic Determinants of Cardiovascular Disease in South Asians

South Asians (SAs), people from the Indian subcontinent (e.g., India, Pakistan, Bangladesh, Sri Lanka, and Nepal) have a higher prevalence of cardiovascular disease (CVD) and suffer from a greater risk of CVD-associated mortality compared to other global populations. These problems are compounded by the alterations in lifestyles due to urbanization and changing cultural, social, economic, and political environments. Current methods of CV risk prediction are based on white populations that under-estimate the CVD risk in SAs. Prospective studies are required to obtain actual CVD morbidity/mortality rates so that comparisons between predicted CVD risk can be made with actual events. Overwhelming data support a strong influence of genetic factors. Genome-Wide Association Studies (GWAS) serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence CVD is still unclear. It is difficult to predict the potential implication of these findings in clinical settings. This review provides a systematic assessment of the risk factors, genetics, and environmental causes of CV health disparity in SAs, and highlights progress made in clinical and genomics discoveries in the rapidly evolving field, which has the potential to show clinical relevance in the near future.

Exploring the Potential Genetic Heterogeneity in the Incidence of Hoof Disorders in Austrian Fleckvieh and Braunvieh Cattle

Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh were as follows: CBLB on BTA1, which causes arthritis in rats; CAV2 on BTA4, which affects skeletal muscles in mice; PTHLH on BTA5, which causes disease phenotypes related to the skeleton in humans, mice, and zebrafish; and SORCS2 on BTA6, which causes decreased susceptibility to injury in mice. Some of the significant SNPs (BTA1, BTA4, BTA5, BTA13, and BTA16) revealed allelic heterogeneity-i.e., different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA1, BTA5, BTA13, and BTA16) correlated to inter-breed differences in linkage disequilibrium (LD) structure and may thus represent false-positive heterogeneity. However, positions on BTA6 (SORCS2), BTA14, and BTA24 mark Braunvieh-specific regions. We hypothesize that the observed genetic heterogeneity of hoof disorders is a by-product of different selection goals defined for the analyzed breeds-toward dairy production in Braunvieh and toward beef production in Fleckvieh. Based on the current dataset, it is not possible to unequivocally confirm or exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The main reason for the problem is that the potential heterogeneity was explored through SNP-phenotype associations and not through causal mutations, due to a limited SNP density offered by the SNP-chip. The rationale against genetic heterogeneity comprises a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, different selection goals defined for the analyzed breeds accompanied by no systematic, genome-wide differences in LD structure between the breeds favor the heterogeneity hypothesis at some smaller genomic regions.

Contribution of Known Genetic Risk Variants to Dyslipidemias and Type 2 Diabetes in Mexico: A Population-Based Nationwide Study

Dyslipidemias are common risk factors for the development of chronic disorders including type 2 diabetes (T2D). Over 100 associated loci have been identified but few reports have evaluated the population attributable fraction captured by them in population-based nationwide surveys. Therefore, we determined the population contribution of a set of known genetic risk variants to the development of dyslipidemias and T2D in Mexico. This study included 1665 participants from a Mexican National Health Survey carried out in the year 2000. It is a probabilistic complex sample survey of households, which comprises representative data at a national level. 103 previously reported SNPs associated with different dyslipidemias or T2D were genotyped and used to compute polygenic risk scores. We found that the previously known variants associated with dyslipidemias explain at most 7% of the total risk variance of lipid levels. In contrast, the known genetic risk component for T2D explained a negligible amount of variance (0.1%). Notably, variants derived from the Native-American ancestry have the strongest effect and contribute with a high proportion of the variance. These results support the need for additional studies aimed to identify specific genetic risk variants for Mexican population.

Genome-wide association study of metabolic syndrome in Korean populations

Metabolic syndrome (MetS) which is caused by obesity and insulin resistance, is well known for its predictive capability for the risk of type 2 diabetes mellitus and cardiovascular disease. The development of MetS is associated with multiple genetic factors, environmental factors and lifestyle. We performed a genome-wide association study to identify single-nucleotide polymorphism (SNP) related to MetS in large Korean population based samples of 1,362 subjects with MetS and 6,061 controls using the Axiom^® Korean Biobank Array 1.0. We replicated the data in another sample including 502 subjects with MetS and 1,751 controls. After adjusting for age and sex, rs662799 located in the APOA5 gene were significantly associated with MetS. 15 SNPs in GCKR, C2orf16, APOA5, ZPR1, and BUD13 were associated with high triglyceride (TG). 14 SNPs in APOA5, ALDH1A2, LIPC, HERPUD1, and CETP, and 2 SNPs in MTNR1B were associated with low high density lipoprotein cholesterol (HDL-C) and high fasting blood glucose respectively. Among these SNPs, 6 TG SNPs: rs1260326, rs1260333, rs1919127, rs964184, rs2075295 and rs1558861 and 11 HDL-C SNPs: rs4775041, rs10468017, rs1800588, rs72786786, rs173539, rs247616, rs247617, rs3764261, rs4783961, rs708272, and rs7499892 were first discovered in Koreans. Additional research is needed to confirm these 17 novel SNPs in Korean population.

Progressive effects of single-nucleotide polymorphisms on 16 phenotypic traits based on longitudinal data

Background

There are many research studies have estimated the heritability of phenotypic traits, but few have considered longitudinal changes in several phenotypic traits together.

Objective

To evaluate the progressive effect of single nucleotide polymorphisms (SNPs) on prominent health-related phenotypic traits by determining SNP-based heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{snp}^{2}$$\end{document}hsnp2) using longitudinal data.

Methods

Sixteen phenotypic traits associated with major health indices were observed biennially for 6843 individuals with 10-year follow-up in a Korean community-based cohort. Average SNP heritability and longitudinal changes in the total period were estimated using a two-stage model. Average and periodic differences for each subject were considered responses to estimate SNP heritability. Furthermore, a genome-wide association study (GWAS) was performed for significant SNPs.

Results

Each SNP heritability for the phenotypic mean of all sixteen traits through 6 periods (baseline and five follow-ups) were significant. Gradually, the forced vital capacity in one second (FEV1) reflected the only significant SNP heritability among longitudinal changes at a false discovery rate (FDR)-adjusted 0.05 significance level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{snp}^{2} = 0.171$$\end{document}hsnp2=0.171, FDR = 0.0012). On estimating chromosomal heritability, chromosome 2 displayed the highest heritability upon periodic changes in FEV1. SNPs including rs2272402 and rs7209788 displayed a genome-wide significant association with longitudinal changes in FEV1 (P = 1.22 × 10⁻⁸ for rs2272402 and P = 3.36 × 10⁻⁷ for rs7209788). De novo variants including rs4922117 (near LPL, P = 2.13 × 10⁻¹⁵) of log-transformed high-density lipoprotein (HDL) ratios and rs2335418 (near HMGCR, P = 3.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10⁻⁹) of low-density lipoprotein were detected on GWAS.

Conclusion

Significant genetic effects on longitudinal changes in FEV1 among the middle-aged general population and chromosome 2 account for most of the genetic variance.

Electronic supplementary material

The online version of this article (10.1007/s13258-019-00902-x) contains supplementary material, which is available to authorized users.

Correlation Between the APOB rs1042034 SNP and Blood Lipid Characteristics of 2 Ethnic Groups in China

The apolipoprotein (Apo) B gene (APOB) is a susceptible gene for dyslipidemia. The purpose of this investigation was to explore the relationship between the APOB rs1042034 single-nucleotide polymorphism (SNP) and serum lipid levels in the Maonan and Han populations. A total of 598 Maonan participants and 609 Han participants were genotyped by polymerase chain reaction and restriction fragment length polymorphism, and the genotypes were also verified by sequencing. There were no differences in genotype and allele frequencies between the 2 ethnic groups or between males and females. The levels of triglyceride (TG) in Maonans were higher and high-density lipoprotein cholesterol was lower in the A allele carriers than the A allele noncarriers; the A allele carriers in Hans had higher TG levels and lower ApoA1/ApoB ratio than the A allele noncarriers (P < .05 for all). Subgroup analysis showed that the A allele carriers in Maonan females had higher TG levels and the A allele carriers in Han females had higher TG levels and lower ApoA1/ApoB ratio than the A allele noncarriers (P < .05 for all). In our study populations, there may be ethnicity- and/or sex-specific associations between the APOB rs1042034 SNP and serum lipid levels.

Clinical utility of the polygenic LDL-C SNP score in familial hypercholesterolemia

Mutations in any of three genes (LDLR, APOB and PCSK9) are known to cause autosomal dominant FH, but a mutation can be found in only ∼40% of patients with a clinical diagnosis of FH. In the remainder, a polygenic aetiology may be the cause of the phenotype, due to the co-inheritance of common LDL-C raising variants. In 2013, we reported the development of a 12-SNP LDL-C "SNP-Score" based on common variants identified as LDL-C raising from genome wide association consortium studies, and have confirmed the validity of this score in samples of no-mutation FH adults and children from more than six countries with European-Caucasian populations. In more than 80% of those with a clinical diagnosis of FH but with no detectable mutation in LDLR/APOB/PCSK9, the polygenic explanation is the most likely for their hypercholesterolaemia. Those with a low score (in the bottom two deciles) may have a mutation in a novel gene, and further research including whole exome or whole genome sequencing is warranted. Only in families where the index case has a monogenic cause should cascade testing be carried out, using DNA tests for an unambiguous identification of affected relatives. The clinical utility of the polygenic explanation is that it supports a more conservative (less aggressive) treatment care pathway for those with no mutation. The ability to distinguish those with a clinical diagnosis of FH who have a monogenic or a polygenic cause of their hypercholesterolaemia is a paradigm example of the use of genomic information to inform Precision Medicine using lipid lowering agents with different efficacy and costs.

Transethnic meta-analysis of metabolic syndrome in a multiethnic study

Genome-wide association studies (GWAS) have been used to establish thousands of genetic associations across numerous phenotypes. To improve the power of GWAS and generalize associations across ethnic groups, transethnic meta-analysis methods are used to combine the results of several GWAS from diverse ancestries. The goal of this study is to identify genetic associations for eight quantitative metabolic syndrome (MetS) traits through a meta-analysis across four ethnic groups. Traits were measured in the GENetics of Noninsulin dependent Diabetes Mellitus (GENNID) Study which consists of African-American (families = 73, individuals = 288), European-American (families = 79, individuals = 519), Japanese-American (families = 17, individuals = 132), and Mexican-American (families = 113, individuals = 610) samples. Genome-wide association results from these four ethnic groups were combined using four meta-analysis methods: fixed effects, random effects, TransMeta, and MR-MEGA. We provide an empirical comparison of the four meta-analysis methods from the GENNID results, discuss which types of loci (characterized by allelic heterogeneity) appear to be better detected by each of the four meta-analysis methods in the GENNID Study, and validate our results using previous genetic discoveries. We specifically compare the two transethnic methods, TransMeta and MR-MEGA, and discuss how each transethnic method's framework relates to the types of loci best detected by each method.

Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.

CD 36: Focus on Epigenetic and Post-Transcriptional Regulation

CD36 is a transmembrane protein involved in fatty acid translocation, scavenging for oxidized fatty acids acting as a receptor for adhesion molecules. It is expressed on macrophages, as well as other types of cells, such as endothelial and adipose cells. CD36 participates in muscle lipid uptake, adipose energy storage, and gut fat absorption. Recently, several preclinical and clinical studies demonstrated that upregulation of CD36 is a prerequisite for tumor metastasis. Cancer metastasis-related research emerged much later and has been less investigated, though it is equally or even more important. CD36 protein expression can be modified by epigenetic changes and post-transcriptional interference from non-coding RNAs. Some data indicate modulation of CD36 expression in specific cell types by epigenetic changes via DNA methylation patterns or histone tails, or through miRNA interference, but this is largely unexplored. The few papers addressing this topic refer mostly to lipid metabolism-related pathologies, whereas in cancer research, data are even more scarce. The aim of this review was to summarize major epigenetic and post-transcriptional mechanisms that impact CD36 expression in relation to various pathologies while highlighting the areas in need of further exploration.

Relevance of Multi-Omics Studies in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of "omics data" have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of "omics" data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.

CETP genotype and concentrations of HDL and lipoprotein subclasses in African-American men

Aim: To assess the association between the CETP Taq1B and I405V polymorphisms with levels of lipoprotein subclasses in African-American (AA) men with and without Type 2 diabetes (T2DM). Patients & methods: AA men, over 30 years of age, with (n = 54) or without T2DM (n = 50), and not receiving lipid-lowering agents, underwent advanced lipid analysis and genotyping. Results & conclusion: In the total patient population Taq1B B2-allele carriers had significantly higher levels of large HDL subclasses (HDL-2b [p = 0.017] and HDL-L [p = 0.019]), lower levels of small-HDL subclasses (HDL-3a [p = 0.004] and HDL-3b [p = 0.031]), and lower levels of LDL subclasses (LDL-IVa [p = 0.012] and LDL-IIIb [p = 0.009]). The only significant genotype-diabetes interaction occurred with the HDL-2a subclass (p = 0.015). No statistically significant associations were seen with I405V genotype. Our observations of lower levels of small-HDL and higher levels of large-HDL suggest that a potentially important HDL subclass-CETP relationship exists.

Apolipoprotein E-C1-C4-C2 gene cluster region and inter-individual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups

The apolipoprotein E-C1-C4-C2 gene cluster at 19q13.32 encodes four amphipathic apolipoproteins. The influence of APOE common polymorphisms on plasma lipid/lipoprotein profile, especially on LDL-related traits, is well recognized; however, little is known about the role of other genes/variants in this gene cluster. In this study, we evaluated the role of common and uncommon/rare genetic variation in this gene region on inter-individual variation in plasma lipoprotein levels in non-Hispanic Whites (NHWs) and African blacks (ABs). In the variant discovery step, the APOE, APOC1, APOC4, APOC2 genes were sequenced along with their flanking and hepatic control regions (HCR1 and HCR2) in 190 subjects with extreme HDL-C/TG levels. The next step involved the genotyping of 623 NHWs and 788 ABs for the identified uncommon/rare variants and common tagSNPs along with additional relevant SNPs selected from public resources, followed by association analyses with lipid traits. A total of 230 sequence variants, including 15 indels were identified, of which 65 were novel. A total of 70 QC-passed variants in NHWs and 108 QC-passed variants in ABs were included in the final association analyses. Single-site association analysis of SNPs with MAF>1% revealed 20 variants in NHWs and 24 variants in ABs showing evidence of association with at least one lipid trait, including several variants exhibiting independent associations from the established APOE polymorphism even after multiple-testing correction. Overall, our study has confirmed known associations and also identified novel associations in this genomic region with various lipid traits. Our data also support the contribution of both common and uncommon/rare variation in this gene region in affecting plasma lipid profile in the general population.

Identification of 22 novel loci associated with urinary biomarkers of albumin, sodium, and potassium excretion

Urine biomarkers reflecting kidney function and handling of dietary sodium and potassium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease, and diabetes mellitus. Knowledge about the genetic determinants of these biomarkers may shed light on pathophysiological mechanisms underlying the development of these diseases. We performed genome-wide association studies of urinary albumin: creatinine ratio (UACR), urinary potassium: creatinine ratio (UK/UCr), urinary sodium: creatinine ratio (UNa/UCr) and urinary sodium: potassium ratio (UNa/UK) in up to 218,450 (discovery) and 109,166 (replication) unrelated individuals of European ancestry from the UK Biobank. Further, we explored genetic correlations, tissue-specific gene expression, and possible genes implicated in the regulation of these biomarkers. After replication, we identified 19 genome-wide significant independent loci associated with UACR, 6 each with UK/UCr and UNa/UCr, and 4 with UNa/UK. In addition to 22 novel associations, we confirmed several established associations, including between the CUBN locus and microalbuminuria. We detected high pairwise genetic correlation across the urinary biomarkers, and between their levels and several physiological measurements. We highlight GIPR, a potential diabetes drug target, as possibly implicated in the genetic control of urinary potassium excretion, and NRBP1, a locus associated with gout, as plausibly involved in sodium and albumin excretion. Overall, we identified 22 novel genome-wide significant associations with urinary biomarkers and confirmed several previously established associations, providing new insights into the genetic basis of these traits and their connection to chronic diseases.

Genome-wide Significance Thresholds for Admixture Mapping Studies

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women's Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10-5 and 4.5 × 10-6 for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

Generalizability and applicability of results obtained from populations of European descent regarding the effect direction and size of HDL-C level-associated genetic variants to the Hungarian general and Roma populations

OBJECTIVES

Large-scale association studies that mainly involve European populations identified many genetic loci related to high-density lipoprotein cholesterol (HDL-C) levels, one of the most important indicators of the risk for cardiovascular diseases. The question with intense speculation of whether the effect estimates obtained from European populations for different HDL-C level-related SNPs are applicable to the Roma ethnicity, the largest minority group in Europe with a South Asian origin, was addressed in the present study.

DESIGN

The associations between 21 SNPs (in the genes LIPC(G), CETP, GALNT2, HMGCP, ABCA1, KCTD10 and WWOX) and HDL-C levels were examined separately in adults of the Hungarian general (N = 1542) and Roma (N = 646) populations by linear regression. Individual effects (direction and size) of single SNPs on HDL-C levels were computed and compared between the study groups and with data published in the literature.

RESULTS

Significant associations between SNPs and HDL-C levels were more frequently found in general subjects than in Roma subjects (11 SNPs in general vs. 4 SNPs in Roma). The CETP gene variants rs1532624, rs708272 and rs7499892 consistently showed significant associations with HDL-C levels across the study groups (p ˂ 0.05), indicating a possible causal variant(s) in this region. Although nominally significant differences in effect size were found for three SNPs (rs693 in gene APOB, rs9989419 in gene CETP, and rs2548861 in gene WWOX) by comparing the general and Roma populations, most of these SNPs did not have a significant effect on HDL-C levels. The β coefficients for SNPs in the Roma population were found to be identical both in direction and magnitude to the effect obtained previously in large-scale studies on European populations.

CONCLUSIONS

The effect of the vast majority of the SNPs on HDL-C levels could be replicated in the Hungarian general and Roma populations, which indicates that the effect size measurements obtained from the literature can be used for risk estimation for both populations.

Methods to evaluate rare variants gene-age interaction for triglycerides

Triglycerides are an important measure of heart health. Although more than 90 genes have been found to be associated to lipids, they only explain 12 to 15% of the variance in lipid levels. Evidence suggests that age may interact with the genetic effect on lipid levels. Existing methods to detect the main effect of rare variants cannot be readily applied for testing the gene environment interaction effect of rare variants, as those methods either have unstable results or inflated Type I error rates when the main effect exists. To overcome these difficulties, we developed two statistical methods: testing of optimally weighted combination of single-nucleotide polymorphism (SNP) environment interaction (TOW-SE) and a variable weight TOW-SE (VW-TOW-SE) to test the gene environment interaction effect of rare variants by grouping SNPs into biologically meaningful SNP-sets (SNPs in a gene or pathway) to improve power and interpretability. The proposed methods can be applied to either continuous or binary environmental variables, and to either continuous or binary outcomes. Simulation studies show that Type I error rates of the proposed methods are under control. Comparing the two methods with the existing interaction sequence kernel association test (iSKAT), the VW-TOW-SE is the most powerful test and the TOW-SE is the second most powerful test when gene environment interaction effect exists for both rare and common variants. The three tests were applied to the GAW20 simulated data, among the five regions in which the main effect of common SNPs was simulated and the gene–age interaction effect was not included. As expected, none of the tests indicated positive results.

Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.

Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes

Even though there has been great success in identifying lipid-associated single-nucleotide polymorphisms (SNPs), the mechanisms through which the SNPs act on each trait are poorly understood. The emergence of large, complex biological data sets in well-characterized cohort studies offers an opportunity to investigate the genetic effects on trait variability as a way of informing the causal genes and biochemical pathways that are involved in lipoprotein metabolism. However, methods for simultaneously analyzing multiple omics, environmental exposures, and longitudinally measured, correlated phenotypes are lacking. The purpose of our study was to demonstrate the utility of the structural equation modeling (SEM) approach to inform our understanding of the pathways by which genetic variants lead to disease risk. With the SEM method, we examine multiple pathways directly and indirectly through previously identified triglyceride (TG)-associated SNPs, methylation, and high-density lipoprotein (HDL), including sex, age, and smoking behavior, while adding in biologically plausible direct and indirect pathways. We observed significant SNP effects (P < 0.05 and directionally consistent) on TGs at visit 4 (TG4) for five loci, including rs645040 (DOCK7), rs964184 (ZPR1/ZNF259), rs4765127 (ZNF664), rs1121980 (FTO), and rs10401969 (SUGP1). Across these loci, we identify three with strong evidence of an indirect genetic effect on TG4 through HDL, one with evidence of pleiotropic effect on HDL and TG4, and one variant that acts on TG4 indirectly through a nearby methylation site. Such information can be used to prioritize candidate genes in regions of interest, inform mechanisms of action of methylation effects, and highlight possible genes with pleiotropic effects.

IGF1 Gene Is Associated With Triglyceride Levels In Subjects With Family History Of Hypertension From The SAPPHIRe And TWB Projects

Chromosome 12q23-q24 has been linked to triglyceride (TG) levels by previous linkage studies, and it contains the Insulin-like growth factor 1 (IGF1) gene. We investigated the association between IGF1 and TG levels using two independent samples collected in Taiwan. First, based on 954 siblings in 397 families from the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe), we found that rs978458 was associated with TG levels (β = -0.049, p = 0.0043) under a recessive genetic model. Specifically, subjects carrying the homozygous genotype of the minor allele had lower TG levels, compared with other subjects. Then, a series of stratification analyses in a large sample of 13,193 unrelated subjects from the Taiwan biobank (TWB) project showed that this association appeared in subjects with a family history (FH) of hypertension (β = -0.045, p = 0.0000034), but not in subjects without such an FH. A re-examination of the SAPPHIRe sample confirmed that this association appeared in subjects with an FH of hypertension (β = -0.068, p = 0.0025), but not in subjects without an FH. The successful replication in two independent samples indicated that IGF1 is associated with TG levels in subjects with an FH of hypertension in Taiwan.

Association between the PPP1R3B polymorphisms and serum lipid traits, the risk of coronary artery disease and ischemic stroke in a southern Chinese Han population

Background

Little is known about the association of the protein phosphatase 1 regulatory subunit 3B gene (PPP1R3B) single nucleotide polymorphisms (SNPs) and serum lipid levels, the risk of coronary artery disease (CAD) and ischemic stroke (IS) in the Chinese populations. This study detected such association in a Southern Chinese Han population.

Methods

Genotypes of 4 novel PPP1R3B SNPs (rs12785, rs330910, rs330915 and rs9949) in 1704 Han Chinese (CAD, 556; IS, 531 and control, 617) were determined by the Snapshot technology.

Results

The rs12785A and rs9949A allele frequency was higher in both CAD/IS patients than in controls. The rs330910T and rs330915T allele frequency was also higher in CAD patients than in controls. The rs330910T allele carriers in controls had lower serum low-density lipoprotein cholesterol (LDL-C) levels than the rs330910T allele non-carriers (P < 0.0014). The rs12785A, rs9949A and rs330910T allele carriers were associated with an increased risk of CAD (P = 0.008–0.004). There was strong linkage disequilibrium among the 4 SNPs in the controls and CAD/IS patients. The T-A-A-G haplotype was associated with a decreased risk of CAD and IS, whereas the A-A-T-A haplotype was associated with an increased risk for IS. Haplotype-environment interactions on the risk of CAD and IS were also observed.

Conclusions

Several PPP1R3B polymorphisms were associated with serum LDL-C levels, the risk of CAD and IS in the Southern Chinese Han population. But these findings still need to be confirmed in the other populations with larger sample sizes.

Electronic supplementary material

The online version of this article (10.1186/s12986-018-0266-y) contains supplementary material, which is available to authorized users.

New Common and Rare Variants Influencing Metabolic Syndrome and Its Individual Components in a Korean Population

To identify novel loci for susceptibility to MetS, we conducted genome-wide association and exome wide association studies consisting of a discovery stage cohort (KARE, 1946 cases and 6427 controls), and a replication stage cohort (HEXA, 430 cases and 3,264 controls). For finding genetic variants for MetS, with its components, we performed multivariate analysis for common and rare associations, using a standard logistic regression analysis for MetS. From the discovery and replication GWA studies, we confirmed 21 genome-wide signals significantly associated with MetS. Of these 21, four were previously unreported to associate with any MetS components: rs765547 near LPL; rs3782889 in MYL2; and rs11065756 and rs10849915 in CCDC63. Using exome chip variants, gene-based analysis of rare variants revealed three genes, CETP, SH2B1, and ZFP2, in the discovery stage, among which only CETP was confirmed in the replication stage. Finally, CETP D442G (rs2303790) associated, as a less common variant, with decreased risk of MetS. In conclusion, we discovered a total of five new MetS-associated loci, and their overlap with other disease-related components, suggest roles in the various etiologies of MetS, and its possible preventive strategies.

A large electronic-health-record-based genome-wide study of serum lipids

A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.

Insights from population-based analyses of plasma lipids across the allele frequency spectrum

Plasma lipid levels are heritable quantitative risk factors and therapeutic targets for cardiovascular disease. Plasma lipids have been a model for translating genetic observations across the allele frequency spectrum to unique biological and therapeutic insights. Most large studies to date predominately comprised of individuals of European ancestry. This review focuses on contemporary evidence from 2016 to 2017 looking at the effect of genetic variants on plasma lipid levels across the allele frequency spectrum with incrementally larger sample sizes and the contribution of non-European ancestry studies to the genetic etiology of plasma lipid levels. To date, over 250 loci have been associated with plasma lipid levels and several of these loci have additional evidence of association with rare coding variants providing evidence for causal genes at the locus.

Prediction of cholesterol ratios within a Korean population

Cholesterol ratios (total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-c) and triglyceride (TG)/HDL-c) have been suggested as better indicators to predict various clinical features such as insulin resistance and heart disease. Therefore, we aimed to build a single nucleotide polymorphism (SNP) set to predict constitutional lipid metabolism. The genotype data of 7795 samples were obtained from the Korea Association Resource. Among the total of 7795 samples, 7016 subjects were used to perform 10-fold cross-validation. We selected the SNPs that showed significance constantly throughout all 10 cross-validation sets; another 779 samples were used as the final validation set. After performing the 10-fold cross-validation, the six SNPs (rs4420638 (APOC1), rs12421652 (BUD13), rs17411126 (LPL), rs6589566 (ZPR1), rs16940212 (LOC101928635) and rs10852765 (ABCA8)) were finally selected for predicting cholesterol ratios. The weighted genetic risk scores (wGRS) were calculated based on the regression slopes of the six selected SNPs. Our results showed upward trends of wGRS for both the TC/HDL-c and TG/HDL-c ratios within the 10-fold cross-validation. Similarly, the wGRS of the six SNPs also showed upward trends in analyses using the SNP selection set and final validation set. The selected six SNPs can be used to explain both the TC/HDL-c and TG/HDL-c ratios. Our results may be useful for the prospective predictions of cholesterol-related diseases.

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data

We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the "hidden heritability" of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10^-7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10^-8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10^-7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.