Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study

Regulation of nc886 (vtRNA2-1) RNAs is associated with cardiometabolic risk factors and diseases

Non-coding 886 (nc886, vtRNA2-1) is a polymorphically imprinted gene. The methylation status of this locus has been shown to be associated with periconceptional conditions, and both the methylation status and the levels of nc886 RNAs have been shown to associate with later-life health traits. We have previously shown that nc886 RNA levels are associated not only with the methylation status of the locus, but also with a genetic polymorphism upstream from the locus. In this study, we describe the genetic and epigenetic regulators that predict lifelong nc886 RNA levels, as well as their association with cardiometabolic disease (CMD) risk factors and events. We utilised six population cohorts and one CMD cohort comprising 9058 individuals in total. The association of nc886 RNA levels, as predicted by epigenetic and genetic regulators, with CMD phenotypes was analysed using regression models, with a meta-analysis of the results. The meta-analysis showed that individuals with upregulated nc886 RNA levels have higher diastolic blood pressure (β = 0.07, p = 0.008), lower HDL levels (β =  - 0.07, p = 0.006) and an increased incidence of type 2 diabetes (OR = 1.260, p = 0.013). Moreover, CMD patients with upregulated nc886 RNA levels have an increased incidence of stroke (OR = 1.581, p = 0.006) and death (OR = 1.290, p = 0.046). In conclusion, we show that individuals who are predicted to present elevated nc886 RNA levels have poorer cardiovascular health and are at an elevated risk of complications in secondary prevention. This unique mechanism yields metabolic variation in human populations, constituting a CMD risk factor that cannot be modified through lifestyle choices.

A framework for conducting GWAS using repeated measures data with an application to childhood BMI

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

New insights into the genetic loci related to egg weight and age at first egg traits in broiler breeder

Egg weight (EW) and age at first egg (AFE) are economically important traits in breeder chicken production. The genetic basis of these traits, however, is far from understood, especially for broiler breeders. In this study, genetic parameter estimation, genome-wide association analysis, meta-analysis, and selective sweep analysis were carried out to identify genetic loci associated with EW and AFE in 6,842 broiler breeders. The study found that the heritability of EW ranged from 0.42 to 0.44, while the heritability of AFE was estimated at 0.33 in the maternal line. Meta-analysis and selective sweep analysis identified two colocalized regions on GGA4 that significantly influenced EW at 32 wk (EW32W) and at 43 wk (EW43W) with both paternal and maternal lines. The genes AR, YIPF6, and STARD8 were located within the significant region (GGA4: 366.86-575.50 kb), potentially affecting EW through the regulation of follicle development, cell proliferation, and lipid transfer etc. The promising genes LCORL and NCAPG were positioned within the significant region (GGA4:75.35-75.42 Mb), potentially influencing EW through pleiotropic effects on growth and development. Additionally, 3 significant regions were associated with AFE on chromosomes GGA7, GGA19, and GGA27. All of these factors affected the AFE by influencing ovarian development. In our study, the genomic information from both paternal and maternal lines was used to identify genetic regions associated with EW and AFE. Two genomic regions and eight genes were identified as the most likely candidates affecting EW and AFE. These findings contribute to a better understanding of the genetic basis of egg production traits in broiler breeders and provide new insights into future technology development.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

SORCS2 activity in pancreatic α-cells safeguards insulin granule formation and release from glucose-stressed β-cells

Sorting receptor SORCS2 is a stress-response factor protecting neurons from acute insults, such as during epilepsy. SORCS2 is also expressed in the pancreas, yet its action in this tissue remains unknown. Combining metabolic studies in SORCS2-deficient mice with ex vivo functional analyses and single-cell transcriptomics of pancreatic tissues, we identified a role for SORCS2 in protective stress response in pancreatic islets, essential to sustain insulin release. We show that SORCS2 is predominantly expressed in islet alpha cells. Loss of expression coincides with inability of these cells to produce osteopontin, a secreted factor that facilitates insulin release from stressed beta cells. In line with diminished osteopontin levels, beta cells in SORCS2-deficient islets show gene expression patterns indicative of aggravated cell stress, and exhibit defects in insulin granule maturation and a blunted glucose response. These findings corroborate a function for SORCS2 in protective stress response that extends to metabolism.

Roles of peripheral lipoproteins and cholesteryl ester transfer protein in the vascular contributions to cognitive impairment and dementia

This narrative review focuses on the role of cholesteryl ester transfer protein (CETP) and peripheral lipoproteins in the vascular contributions to cognitive impairment and dementia (VCID). Humans have a peripheral lipoprotein profile where low-density lipoproteins (LDL) represent the dominant lipoprotein fraction and high-density lipoproteins (HDL) represent a minor lipoprotein fraction. Elevated LDL-cholesterol (LDL-C) levels are well-established to cause cardiovascular disease and several LDL-C-lowering therapies are clinically available to manage this vascular risk factor. The efficacy of LDL-C-lowering therapies to reduce risk of all-cause dementia and AD is now important to address as recent studies demonstrate a role for LDL in Alzheimer's Disease (AD) as well as in all-cause dementia. The LDL:HDL ratio in humans is set mainly by CETP activity, which exchanges cholesteryl esters for triglycerides across lipoprotein fractions to raise LDL and lower HDL as CETP activity increases. Genetic and pharmacological studies support the hypothesis that CETP inhibition reduces cardiovascular risk by lowering LDL, which, by extension, may also lower VCID. Unlike humans, wild-type mice do not express catalytically active CETP and have HDL as their major lipoprotein fraction. As HDL has potent beneficial effects on endothelial cells, the naturally high HDL levels in mice protect them from vascular disorders, likely including VCID. Genetic restoration of CETP expression in mice to generate a more human-like lipid profile may increase the relevance of murine models for VCID studies. The therapeutic potential of existing and emerging LDL-lowering therapies for VCID will be discussed. Figure Legend. Cholesteryl Ester Transfer Protein in Alzheimer's Disease. CETP is mainly produced by the liver, and exchanges cholesteryl esters for triglycerides across lipoprotein fractions to raise circulating LDL and lower HDL as CETP activity increases. Low CETP activity is associated with better cardiovascular health, due to decreased LDL and increased HDL, which may also improve brain health. Although most peripheral lipoproteins cannot enter the brain parenchyma due to the BBB, it is increasingly appreciated that direct access to the vascular endothelium may enable peripheral lipoproteins to have indirect effects on brain health. Thus, lipoproteins may affect the cerebrovasculature from both sides of the BBB. Recent studies show an association between elevated plasma LDL, a well-known cardiovascular risk factor, and a higher risk of AD, and considerable evidence suggests that high HDL levels are associated with reduced CAA and lower neuroinflammation. Considering the potential detrimental role of LDL in AD and the importance of HDL's beneficial effects on endothelial cells, high CETP activity may lead to compromised BBB integrity, increased CAA deposits and greater neuroinflammation. Abbreviations: CETP - cholesteryl transfer ester protein; LDL - low-density lipoproteins; HDL - high-density lipoproteins; BBB - blood-brain barrier; CAA - cerebral amyloid angiopathy, SMC - smooth muscle cells, PVM - perivascular macrophages, RBC - red blood cells.

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.

Reducing the Threshold of Primary Prevention of Cardiovascular Disease to 10% Over 10 Years: The Implications of Altered Intensity "Statin" Therapy Guidance

Cardiovascular disease (CVD) is a significant noncommunicable disease associated with high long-term mortality. In addition to more effective secondary therapies, the primary prevention of CVD has developed markedly in the past several years. This study aims to investigate the evidence and impact of reducing the threshold for primary CVD risk management to 10% over 10 years with "statin" therapy. To conduct research a systematic review utilizing 5 electronic database searches was completed for studies, analyzing the clinical effect of reducing the threshold of CVD risk to 10% over 10 years for primary prevention with statin therapy. The study included six (6) trials. Statin therapy was allocated to 31,018 participants. The mean age was 61 years and the mean follow-up was 4.6 years. The mean relative reduction in total cholesterol was 19% (from an average of), low-density lipoprotein cholesterol was 28.3% (from mmol/L to mmol/L) and triglycerides were 14.8% (from mmol/L to mmol/L). High-density lipoprotein cholesterol was observed to increase by a mean of 3.3% (from mmol/L to mmol/L). When examining all-cause mortality, statin therapy was associated with a 12% relative risk reduction compared with control, where overall rates were reduced from 1.4% to 1. % There is a 30% risk reduction in general major coronary events (from to %). There is a 19% risk reduction in general major cerebrovascular events with the statin group. While there is undoubtedly statistical evidence that supports the observation of the effectiveness of statin therapy for primary prevention, there is a risk that many hundreds of patients need to be treated to avoid a single adverse clinical outcome.

Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review

Introduction

The prevalence of cardiometabolic diseases has increased in Latin American and the Caribbean populations (LACP). To identify gene-lifestyle interactions that modify the risk of cardiometabolic diseases in LACP, a systematic search using 11 search engines was conducted up to May 2022.

Methods

Eligible studies were observational and interventional studies in either English, Spanish, or Portuguese. A total of 26,171 publications were screened for title and abstract; of these, 101 potential studies were evaluated for eligibility, and 74 articles were included in this study following full-text screening and risk of bias assessment. The Appraisal tool for Cross-Sectional Studies (AXIS) and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) assessment tool were used to assess the methodological quality and risk of bias of the included studies.

Results

We identified 122 significant interactions between genetic and lifestyle factors on cardiometabolic traits and the vast majority of studies come from Brazil (29), Mexico (15) and Costa Rica (12) with FTO, APOE, and TCF7L2 being the most studied genes. The results of the gene-lifestyle interactions suggest effects which are population-, gender-, and ethnic-specific. Most of the gene-lifestyle interactions were conducted once, necessitating replication to reinforce these results.

Discussion

The findings of this review indicate that 27 out of 33 LACP have not conducted gene-lifestyle interaction studies and only five studies have been undertaken in low-socioeconomic settings. Most of the studies were cross-sectional, indicating a need for longitudinal/prospective studies. Future gene-lifestyle interaction studies will need to replicate primary research of already studied genetic variants to enable comparison, and to explore the interactions between genetic and other lifestyle factors such as those conditioned by socioeconomic factors and the built environment. The protocol has been registered on PROSPERO, number CRD42022308488.

Systematic review registration

https://clinicaltrials.gov, identifier CRD420223 08488.

The APOE-TOMM40 Humanized Mouse Model: Characterization of Age, Sex, and PolyT Variant Effects on Gene Expression

BACKGROUND

The human chromosome 19q13.32 is a gene rich region and has been associated with multiple phenotypes, including late onset Alzheimer's disease (LOAD) and other age-related conditions.

OBJECTIVE

Here we developed the first humanized mouse model that contains the entire TOMM40 and APOE genes with all intronic and intergenic sequences including the upstream and downstream regions. Thus, the mouse model carries the human TOMM40 and APOE genes and their intact regulatory sequences.

METHODS

We generated the APOE-TOMM40 humanized mouse model in which the entire mouse region was replaced with the human (h)APOE-TOMM40 loci including their upstream and downstream flanking regulatory sequences using recombineering technologies. We then measured the expression of the human TOMM40 and APOE genes in the mice brain, liver, and spleen tissues using TaqMan based mRNA expression assays.

RESULTS

We investigated the effects of the '523' polyT genotype (S/S or VL/VL), sex, and age on the human TOMM40- and APOE-mRNAs expression levels using our new humanized mouse model. The analysis revealed tissue specific and shared effects of the '523' polyT genotype, sex, and age on the regulation of the human TOMM40 and APOE genes. Noteworthy, the regulatory effect of the '523' polyT genotype was observed for all studied organs.

CONCLUSION

The model offers new opportunities for basic science, translational, and preclinical drug discovery studies focused on the APOE genomic region in relation to LOAD and other conditions in adulthood.

The Interindividual Variability of Phytofluene Bioavailability is Associated with a Combination of Single Nucleotide Polymorphisms

SCOPE

Phytofluene is a colorless carotenoid with potential health benefits that displays a higher bioavailability compared to carotenoids such as lutein, β-carotene or lycopene. Several studies suggest its bioavailability displays an elevated interindividual variability. The aim of this work is to investigate whether a combination of SNPs is associated with this variability.

METHODS AND RESULTS

Thirty-seven healthy adult males consume a test meal that provides phytofluene from a tomato puree. Phytofluene concentrations are measured at fast and in chylomicrons at regular time intervals after meal intake. Identification of the combination of SNPs that best explained the interindividual variability of the phytofluene response is assessed by partial least squares regression. There is a large interindividual variability in the phytofluene response, with CV = 88%. Phytofluene bioavailability is positively correlated with fasting plasma phytofluene concentration (r = 0.57; p = 2 × 10^-4 ). A robust partial least squares regression model comprising 14 SNPs near or within 11 genes (ABCA1-rs2487059, rs2515629, and rs4149316, APOC1-rs445925, CD36-rs3211881, ELOVL5-rs6941533, FABP1-rs10185660, FADS3-rs1000778, ISX-rs130461, and rs17748559, LIPC-rs17240713, LPL-rs7005359, LYPLAL1-rs1351472, SETD7-rs11936429) explains 51% (adjusted R² ) of the interindividual variability in phytofluene bioavailability.

CONCLUSIONS

This study reports a combination of SNPs that is associated with a significant part of the interindividual variability of phytofluene bioavailability in a healthy male adult population.

Life-Course Associations between Blood Pressure-Related Polygenic Risk Scores and Hypertension in the Bogalusa Heart Study

Genetic information may help to identify individuals at increased risk for hypertension in early life, prior to the manifestation of elevated blood pressure (BP) values. We examined 369 Black and 832 White Bogalusa Heart Study (BHS) participants recruited in childhood and followed for approximately 37 years. The multi-ancestry genome-wide polygenic risk scores (PRSs) for systolic BP (SBP), diastolic BP (DBP), and hypertension were tested for an association with incident hypertension and stage 2 hypertension using Cox proportional hazards models. Race-stratified analyses were adjusted for baseline age, age2, sex, body mass index, genetic principal components, and BP. In Black participants, each standard deviation increase in SBP and DBP PRS conferred a 38% (p = 0.009) and 22% (p = 0.02) increased risk of hypertension and a 74% (p < 0.001) and 50% (p < 0.001) increased risk of stage 2 hypertension, respectively, while no association was observed with the hypertension PRSs. In Whites, each standard deviation increase in SBP, DBP, and hypertension PRS conferred a 24% (p < 0.05), 29% (p = 0.01), and 25% (p < 0.001) increased risk of hypertension, and a 27% (p = 0.08), 29% (0.01), and 42% (p < 0.001) increased risk of stage 2 hypertension, respectively. The addition of BP PRSs to the covariable-only models generally improved the C-statistics (p < 0.05). Multi-ancestry BP PRSs demonstrate the utility of genomic information in the early life prediction of hypertension.

The relationship between temperament, polygenic score for intelligence and cognition: A population-based study of middle-aged adults

We investigated whether temperament modifies an association between polygenic intelligence potential and cognitive test performance in midlife. The participants (n = 1647, born between 1962 and 1977) were derived from the Young Finns Study. Temperament was assessed with Temperament and Character Inventory over a 15-year follow-up (1997, 2001, 2007, 2012). Polygenic intelligence potential was assessed with a polygenic score for intelligence. Cognitive performance (visual memory, reaction time, sustained attention, spatial working memory) was assessed with CANTAB in midlife. The PGSI was significantly associated with the overall cognitive performance and performance in visual memory, sustained attention and working memory tests but not reaction time test. Temperament did not correlate with polygenic score for intelligence and did not modify an association between the polygenic score and cognitive performance, either. High persistence was associated with higher visual memory (B = 0.092; FDR-adj. p = 0.007) and low harm avoidance with higher overall cognitive performance, specifically better reaction time (B = -0.102; FDR-adj; p = 0.007). The subscales of harm avoidance had different associations with cognitive performance: higher "anticipatory worry," higher "fatigability," and lower "shyness with strangers" were associated with lower cognitive performance, while the role of "fear of uncertainty" was subtest-related. In conclusion, temperament does not help or hinder one from realizing their genetic potential for intelligence. The overall modest relationships between temperament and cognitive performance advise caution if utilizing temperament-related information e.g. in working-life recruitments. Cognitive abilities may be influenced by temperament variables, such as the drive for achievement and anxiety about test performance, but they involve distinct systems of learning and memory.

Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model

In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits.

Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

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.

SORLA is required for insulin-induced expansion of the adipocyte precursor pool in visceral fat

Visceral adipose tissue shows remarkable plasticity, constantly replacing mature adipocytes from an inherent pool of adipocyte precursors. The number of precursors is set in the juvenile organism and remains constant in adult life. Which signals drive precursor pool expansion in juveniles and why they operate in visceral but not in subcutaneous white adipose tissue (WAT) are unclear. Using mouse models, we identified the insulin-sensitizing receptor SORLA as a molecular factor explaining the distinct proliferative capacity of visceral WAT. High levels of SORLA activity in precursors of juvenile visceral WAT prime these cells for nutritional stimuli provided through insulin, promoting mitotic expansion of the visceral precursor cell pool in overfed juvenile mice. SORLA activity is low in subcutaneous precursors, blunting their response to insulin and preventing diet-induced proliferation of this cell type. Our findings provide a molecular explanation for the unique proliferative properties of juvenile visceral WAT, and for the genetic association of SORLA with visceral obesity in humans.

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

Genetic differential susceptibility to the parent-child relationship quality and the life span development of compassion

The development of compassion for others might be influenced by the social experiences made during childhood and has a genetic component. No research has yet investigated whether the parent-child relationship quality interacts with genetic variation in the oxytocin and dopamine systems in predicting compassion over the life span. In the prospective Young Finns Study (N = 2099, 43.9% men), we examined the interaction between mother-reported emotional warmth and intolerance toward their child assessed in 1980 (age of participants, 3-18 years) and two established genetic risk scores for oxytocin levels and dopamine signaling activity. Dispositional compassion for others was measured with the Temperament and Character Inventory 1997, 2001, and 2012 (age of participants, 20-50 years). We found a gene-environment interaction (p = .031) that remained marginally significant after adjustment for multiple testing. In line with the differential susceptibility hypothesis, only participants who carry alleles associated with low dopamine signaling activity had higher levels of compassion when growing up with emotionally warm parents, whereas they had lower levels of compassion when their parents were emotionally cold. Children's genetic variability in the dopamine system might result in plasticity to early environmental influences that have a long-lasting effect on the development of compassion. However, our findings need replication.

Functional Polymorphisms in Oxytocin and Dopamine Pathway Genes and the Development of Dispositional Compassion Over Time: The Young Finns Study

Background: We define compassion as an enduring disposition that centers upon empathetic concern for another person's suffering and the motivation to act to alleviate it. The contribution of specific candidate genes to the development of dispositional compassion for others is currently unknown. We examine candidate genes in the oxytocin and dopamine signaling pathways.

Methods: In a 32-year follow-up of the Young Finns Study (N = 2,130, 44.0% men), we examined with multiple indicators latent growth curve modeling the molecular genetic underpinnings of dispositional compassion for others across the life span. We selected five single nucleotide polymorphisms (SNPs) whose functions are known in humans: rs2268498 (OXTR), rs3796863 (CD38) (related to lower oxytocin levels), rs1800497 (ANKK1/DRD2), rs4680 (COMT), and rs1611115 (DBH) (related to higher dopamine levels). Compassion was measured with Cloninger's Temperament and Character Inventory on three repeated observations spanning 15 years (1997–2012). Differences between gender were tested.

Results: We did not find an effect of the five SNPs in oxytocin and dopamine pathway genes on the initial levels of dispositional compassion for others. Individuals who carry one or two copies of the T-allele of DBH rs1611115, however, tend to increase faster in compassion over time than those homozygotes for the C-allele, b = 0.063 (SE = 0.027; p = 0.018). This effect was largely driven by male participants, 0.206 (SE = 0.046; p < 0.001), and was not significant in female participants when analyzed separately.

Conclusions: Men who are known to have, on average, lower compassion than women seem to reduce this difference over time if they carry the T-allele of DBH rs1611115. The direction of the association indicates that dopamine signaling activity rather than overall dopamine levels might drive the development of compassion.

Human apolipoprotein C1 transgenesis reduces atherogenesis in hypercholesterolemic rabbits

BACKGROUND AND AIMS

Apolipoprotein (apo) C1 is a 6.6 kDa protein associated with HDL and VLDL. ApoC1 alters triglyceride clearance, and it also favors cholesterol accumulation in HDL, especially by inhibiting CETP in human plasma. Apart from studies in mice, which lack CETP, the impact of apoC1 on atherosclerosis in animal models expressing CETP, like in humans, is not known. This study aimed at determining the net effect of human apoC1 on atherosclerosis in rabbits, a species with naturally high CETP activity but with endogenous apoC1 without CETP inhibitory potential.

METHODS

Rabbits expressing a human apoC1 transgene (HuApoC1Tg) were generated and displayed significant amounts of human apoC1 in plasma.

RESULTS

After cholesterol feeding, atherosclerosis lesions were significantly less extensive (-22%, p < 0.05) and HDL displayed a reduced ability to serve as CETP substrates (-25%, p < 0.05) in HuApoC1Tg rabbits than in WT littermates. It was associated with rises in plasma HDL cholesterol level and PON-1 activity, and a decrease in the plasma level of the lipid oxidation markers 12(S)-HODE and 8(S)HETE. In chow-fed animals, the level of HDL-cholesterol was also significantly higher in HuApoC1Tg than in WT animals (0.83 ± 0.11 versus 0.73 ± 0.11 mmol/L, respectively, p < 0.05), and it was associated with significantly lower CETP activity (cholesteryl ester transfer rate, -10%, p < 0.05; specific CETP activity, -14%, p < 0.05).

CONCLUSIONS

Constitutive expression of fully functional human apoC1 in transgenic rabbit attenuates atherosclerosis. It was found to relate, at least in part, to the inhibition of plasma CETP activity and to alterations in plasma HDL.

Carotid Intima-Media Thickness: Novel Loci, Sex-Specific Effects, and Genetic Correlations With Obesity and Glucometabolic Traits in UK Biobank

OBJECTIVE

Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM software to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease.

CONCLUSIONS

These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression.

Uncovering the complex genetics of human character

Human personality is 30-60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic-phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Uncovering the complex genetics of human temperament

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models

Motivation

Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together.

Results

We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially non-informative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP in terms of both high association mapping power and accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project.

Availability and implementation

iMAP is freely available at http://www.xzlab.org/software.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

High-density lipoprotein lipid peroxidation as a molecular signature of the risk for developing cardiovascular disease: Results from MASHAD cohort

High-density lipoprotein (HDL) function rather than level may better predict cardiovascular disease (CVD). However, the contribution of the impaired antioxidant function of HDL that is associated with increased HDL lipid peroxidation (HDLox) to the development of clinical CVD remains unclear. We have investigated the association between serum HDLox with incident CVD outcomes in Mashhad cohort. Three-hundred and thirty individuals who had a median follow-up period of 7 years were recruited as part of the cohort. The primary end point was cardiovascular event, including myocardial infarction, stable angina, unstable angina, or coronary revascularization. In both univariate/multivariate analyses adjusted for traditional CVD risk factors, HDLox was an independent risk factor for CVD (odds ratio, 1.62; 95% confidence interval, 1.41-1.86; p < 0.001). For every increase in HDLox by 0.1 unit, there was an increase in CVD risk by 1.62-fold. In an adjusted analysis, there was a >2.5-fold increase in cardiovascular risk in individuals with HDLox higher than cutoff point of 1.06 compared to those with lower scores, suggesting HDLox > 1.06 is related to the impaired HDL oxidant function and in turn exposed to elevated risk of CVD outcomes (hazard ratio, 2.72; 95% CI, 1.88-3.94). Higher HDLox is a surrogate measure of reduced HDL antioxidant function that positively associated with cardiovascular events in a population-based cohort.

Physical Exercise Affects Adipose Tissue Profile and Prevents Arterial Thrombosis in BDNF Val66Met Mice

Adipose tissue accumulation is an independent and modifiable risk factor for cardiovascular disease (CVD). The recent CVD European Guidelines strongly recommend regular physical exercise (PE) as a management strategy for prevention and treatment of CVD associated with metabolic disorders and obesity. Although mutations as well as common genetic variants, including the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, are associated with increased body weight, eating and neuropsychiatric disorders, and myocardial infarction, the effect of this polymorphism on adipose tissue accumulation and regulation as well as its relation to obesity/thrombosis remains to be elucidated. Here, we showed that white adipose tissue (WAT) of humanized knock-in BDNFVal66Met (BDNF^Met/Met) mice is characterized by an altered morphology and an enhanced inflammatory profile compared to wild-type BDNF^Val/Val. Four weeks of voluntary PE restored the adipocyte size distribution, counteracted the inflammatory profile of adipose tissue, and prevented the prothrombotic phenotype displayed, per se, by BDNF^Met/Met mice. C3H10T1/2 cells treated with the Pro-BDNFMet peptide well recapitulated the gene alterations observed in BDNF^Met/Met WAT mice. In conclusion, these data indicate the strong impact of lifestyle, in particular of the beneficial effect of PE, on the management of arterial thrombosis and inflammation associated with obesity in relation to the specific BDNF Val66Met mutation.

CETP (Cholesteryl Ester Transfer Protein) Concentration: A Genome-Wide Association Study Followed by Mendelian Randomization on Coronary Artery Disease

BACKGROUND

We aimed to identify independent genetic determinants of circulating CETP (cholesteryl ester transfer protein) to assess causal effects of variation in CETP concentration on circulating lipid concentrations and cardiovascular disease risk.

METHODS

A genome-wide association discovery and replication study on serum CETP concentration were embedded in the NEO study (Netherlands Epidemiology of Obesity). Based on the independent identified variants, Mendelian randomization was conducted on serum lipids (NEO study) and coronary artery disease (CAD; CARDIoGRAMplusC4D consortium).

RESULTS

In the discovery analysis (n=4248), we identified 3 independent variants (P<5×10^-8) that determine CETP concentration. These single-nucleotide polymorphisms were mapped to CETP and replicated in a separate subpopulation (n=1458). Per-allele increase (SE) in serum CETP was 0.32 (0.02) µg/mL for rs247616-C, 0.35 (0.02) µg/mL for rs12720922-A, and 0.12 (0.02) µg/mL for rs1968905-G. Combined, these 3 variants explained 16.4% of the total variation in CETP concentration. One microgram per milliliter increase in genetically determined CETP concentration strongly decreased high-density lipoprotein cholesterol (-0.23 mmol/L; 95% confidence interval, -0.26 to -0.20), moderately increased low-density lipoprotein cholesterol (0.08 mmol/L; 95% confidence interval, 0.00-0.16), and was associated with an odds ratio of 1.08 (95% confidence interval, 0.94-1.23) for CAD risk.

CONCLUSIONS

This is the first genome-wide association study identifying independent variants that largely determine CETP concentration. Although high-density lipoprotein cholesterol is not a causal risk factor for CAD, it has been unequivocally demonstrated that low-density lipoprotein cholesterol lowering is proportionally associated with a lower CAD risk. Therefore, the results of our study are fully consistent with the notion that CETP concentration is causally associated with CAD through low-density lipoprotein cholesterol.

Generality of genomic findings on blood pressure traits and its usefulness in precision medicine in diverse populations: A systematic review

Remarkable findings from genome-wide association studies (GWAS) on blood pressure (BP) traits have made new insights for developing precision medicine toward more effective screening measures. However, generality of GWAS findings in diverse populations is hampered by some technical limitations. There is no comprehensive study to evaluate source(s) of the non-generality of GWAS results on BP traits, so to fill the gap, this systematic review study was carried out. Using MeSH terms, 1545 records were detected through searching in five databases and 49 relevant full-text articles were included in our review. Overall, 749 unique variants were reported, of those, majority of variants have been detected in Europeans and were associated to systolic and diastolic BP traits. Frequency of genetic variants with same position was low in European and non-European populations (n = 38). However, more than 200 (>25%) single nucleotide polymorphisms were found on same loci or linkage disequilibrium blocks (r² ≥ 80%). Investigating for locus position and linkage disequilibrium of infrequent unique variants showed modest to high reproducibility of findings in Europeans that in some extent was generalizable in other populations. Beyond theoretical limitations, our study addressed other possible sources of non-generality of GWAS findings for BP traits in the same and different origins.

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

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.

A PheWAS study of a large observational epidemiological cohort of African Americans from the REGARDS study

BACKGROUND

Cardiovascular disease, diabetes, and kidney disease are among the leading causes of death and disability worldwide. However, knowledge of genetic determinants of those diseases in African Americans remains limited.

RESULTS

In our study, associations between 4956 GWAS catalog reported SNPs and 67 traits were examined among 7726 African Americans from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which is focused on identifying factors that increase stroke risk. The prevalent and incident phenotypes studied included inflammation, kidney traits, cardiovascular traits and cognition. Our results validated 29 known associations, of which eight associations were reported for the first time in African Americans.

CONCLUSION

Our cross-racial validation of GWAS findings provide additional evidence for the important roles of these loci in the disease process and may help identify genes especially important for future functional validation.

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.

Oxytocin receptor gene (OXTR) variant rs1042778 moderates the influence of family environment on changes in perceived social support over time

BACKGROUND

Lack of social support is an established risk factor across health outcomes, making it important to examine its family environmental and genetic determinants.

METHODS

In a 27-year follow-up of the Young Finns Study (N = 2341), we examined with a latent growth curve model whether genes involved in the oxytocin signaling pathway-namely, oxytocin receptor gene (OXTR) variants rs1042778, rs2254298, and rs53576-moderate the effect of early-life social experiences on perceived social support across the life span. Mothers reported the emotional warmth and acceptance towards their children at baseline when the participants were from 3 to 18 years old (1980). Perceived family support and support from friends and peripheral sources were assessed in five follow-ups 18 years apart (1989-2007).

RESULTS

Maternal emotional warmth and acceptance predicted the initial level of perceived social support across subscales, while the rate of change in family support was affected by the family environment only if participants carried the T-allele of OXTR rs1042778. This gene-environment interaction was not found for the rate of change in support from friends and peripheral sources and we also did not find associations between latent growth in perceived social support and OXTR variants rs53576 and rs2254298.

LIMITATIONS

Selective attrition in perceived social support, maternal emotional warmth and acceptance, gender, and SES. Family environment was assessed by a non-standardized measure.

CONCLUSIONS

OXTR rs1042778 polymorphism seems to contribute to changes in perceived family support in that way that some individuals (T-allele carriers) 'recover', to some extent, from the effects of early-life social experiences, whereas others (G/G genotype carriers) do not.

Hypertension genomics and cardiovascular prevention

Hypertension continues to be a major risk factor for global mortality, and recent genome-wide association studies (GWAS) have expanded in size, leading to the identification of further genetic loci influencing blood pressure. In light of the new knowledge from the largest cardiovascular GWAS to date, we review the potential impact of genomics on discovering potential drug targets, risk stratification with genetic risk scores, drug selection with pharmacogenetics, and exploring insights provided by gene-environment interactions.

Hidden heterogeneity in Alzheimer's disease: Insights from genetic association studies and other analyses

Despite evident success in clarifying many important features of Alzheimer's disease (AD) the efficient methods of its prevention and treatment are not yet available. The reasons are likely to be the fact that AD is a multifactorial and heterogeneous health disorder with multiple alternative pathways of disease development and progression. The availability of genetic data on individuals participated in longitudinal studies of aging health and longevity, as well as on participants of cross-sectional case-control studies allow for investigating genetic and non-genetic connections with AD and to link the results of these analyses with research findings obtained in clinical, experimental, and molecular biological studies of this health disorder. The objective of this paper is to perform GWAS of AD in several study populations and investigate possible roles of detected genetic factors in developing AD hallmarks and in other health disorders. The data collected in the Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), Health and Retirement Study (HRS) and Late Onset Alzheimer's Disease Family Study (LOADFS) were used in these analyses. The logistic regression and Cox's regression were used as statistical models in GWAS. The results of analyses confirmed strong associations of genetic variants from well-known genes APOE, TOMM40, PVRL2 (NECTIN2), and APOC1 with AD. Possible roles of these genes in pathological mechanisms resulting in development of hallmarks of AD are described. Many genes whose connection with AD was detected in other studies showed nominally significant associations with this health disorder in our study. The evidence on genetic connections between AD and vulnerability to infection, as well as between AD and other health disorders, such as cancer and type 2 diabetes, were investigated. The progress in uncovering hidden heterogeneity in AD would be substantially facilitated if common mechanisms involved in development of AD, its hallmarks, and AD related chronic conditions were investigated in their mutual connection.

Association of apolipoprotein E gene polymorphisms with blood lipids and their interaction with dietary factors

BACKGROUND

Several candidate genes have been identified in relation to lipid metabolism, and among these, lipoprotein lipase (LPL) and apolipoprotein E (APOE) gene polymorphisms are major sources of genetically determined variation in lipid concentrations. This study investigated the association of two single nucleotide polymorphisms (SNPs) at LPL, seven tagging SNPs at the APOE gene, and a common APOE haplotype (two SNPs) with blood lipids, and examined the interaction of these SNPs with dietary factors.

METHODS

The population studied for this investigation included 660 individuals from the Prevention of Cancer by Intervention with Selenium (PRECISE) study who supplied baseline data. The findings of the PRECISE study were further replicated using 1238 individuals from the Caerphilly Prospective cohort (CaPS). Dietary intake was assessed using a validated food-frequency questionnaire (FFQ) in PRECISE and a validated semi-quantitative FFQ in the CaPS. Interaction analyses were performed by including the interaction term in the linear regression model adjusted for age, body mass index, sex and country.

RESULTS

There was no association between dietary factors and blood lipids after Bonferroni correction and adjustment for confounding factors in either cohort. In the PRECISE study, after correction for multiple testing, there was a statistically significant association of the APOE haplotype (rs7412 and rs429358; E2, E3, and E4) and APOE tagSNP rs445925 with total cholesterol (P = 4 × 10^- 4 and P = 0.003, respectively). Carriers of the E2 allele had lower total cholesterol concentration (5.54 ± 0.97 mmol/L) than those with the E3 (5.98 ± 1.05 mmol/L) (P = 0.001) and E4 (6.09 ± 1.06 mmol/L) (P = 2 × 10^- 4) alleles. The association of APOE haplotype (E2, E3, and E4) and APOE SNP rs445925 with total cholesterol (P = 2 × 10^- 6 and P = 3 × 10^- 4, respectively) was further replicated in the CaPS. Additionally, significant association was found between APOE haplotype and APOE SNP rs445925 with low density lipoprotein cholesterol in CaPS (P = 4 × 10^- 4 and P = 0.001, respectively). After Bonferroni correction, none of the cohorts showed a statistically significant SNP-diet interaction on lipid outcomes.

CONCLUSION

In summary, our findings from the two cohorts confirm that genetic variations at the APOE locus influence plasma total cholesterol concentrations, however, the gene-diet interactions on lipids require further investigation in larger cohorts.

Longitudinal analysis strategies for modelling epigenetic trajectories

Background

DNA methylation levels are known to vary over time, and modelling these trajectories is crucial for our understanding of the biological relevance of these changes over time. However, due to the computational cost of fitting multilevel models across the epigenome, most trajectory modelling efforts to date have focused on a subset of CpG sites identified through epigenome-wide association studies (EWAS) at individual time-points.

Methods

We propose using linear regression across the repeated measures, estimating cluster-robust standard errors using a sandwich estimator, as a less computationally intensive strategy than multilevel modelling. We compared these two longitudinal approaches, as well as three approaches based on EWAS (associated at baseline, at any time-point and at all time-points), for identifying epigenetic change over time related to an exposure using simulations and by applying them to blood DNA methylation profiles from the Accessible Resource for Integrated Epigenomics Studies (ARIES).

Results

Restricting association testing to EWAS at baseline identified a less complete set of associations than performing EWAS at each time-point or applying the longitudinal modelling approaches to the full dataset. Linear regression models with cluster-robust standard errors identified similar sets of associations with almost identical estimates of effect as the multilevel models, while also being 74 times more efficient. Both longitudinal modelling approaches identified comparable sets of CpG sites in ARIES with an association with prenatal exposure to smoking (>70% agreement).

Conclusions

Linear regression with cluster-robust standard errors is an appropriate and efficient approach for longitudinal analysis of DNA methylation data.

SORCS1 and SORCS3 control energy balance and orexigenic peptide production

SORCS1 and SORCS3 are two related sorting receptors expressed in neurons of the arcuate nucleus of the hypothalamus. Using mouse models with individual or dual receptor deficiencies, we document a previously unknown function of these receptors in central control of metabolism. Specifically, SORCS1 and SORCS3 act as intracellular trafficking receptors for tropomyosin-related kinase B to attenuate signaling by brain-derived neurotrophic factor, a potent regulator of energy homeostasis. Loss of the joint action of SORCS1 and SORCS3 in mutant mice results in excessive production of the orexigenic neuropeptide agouti-related peptide and in a state of chronic energy excess characterized by enhanced food intake, decreased locomotor activity, diminished usage of lipids as metabolic fuel, and increased adiposity, albeit at overall reduced body weight. Our findings highlight a novel concept in regulation of the melanocortin system and the role played by trafficking receptors SORCS1 and SORCS3 in this process.

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

BACKGROUND

Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods.

METHODS AND RESULTS

A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P<0.0001) and validation data (AUC=0.769 versus AUC=0.747, P=0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index.

CONCLUSIONS

WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.

Biochemical and cellular properties of insulin receptor signalling

The mechanism of insulin action is a central theme in biology and medicine. In addition to the rather rare condition of insulin deficiency caused by autoimmune destruction of pancreatic β-cells, genetic and acquired abnormalities of insulin action underlie the far more common conditions of type 2 diabetes, obesity and insulin resistance. The latter predisposes to diseases ranging from hypertension to Alzheimer disease and cancer. Hence, understanding the biochemical and cellular properties of insulin receptor signalling is arguably a priority in biomedical research. In the past decade, major progress has led to the delineation of mechanisms of glucose transport, lipid synthesis, storage and mobilization. In addition to direct effects of insulin on signalling kinases and metabolic enzymes, the discovery of mechanisms of insulin-regulated gene transcription has led to a reassessment of the general principles of insulin action. These advances will accelerate the discovery of new treatment modalities for diabetes.

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F2 population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F2 population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q 2) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F2 chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2nd to 10th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F2 chickens when compared with conventional methods.

Genome-Wide Association Study of Piglet Uniformity and Farrowing Interval

Piglet uniformity (PU) and farrowing interval (FI) are important reproductive traits related to production and economic profits in the pig industry. However, the genetic architecture of the longitudinal trends of reproductive traits still remains elusive. Herein, we performed a genome-wide association study (GWAS) to detect potential genetic variation and candidate genes underlying the phenotypic records at different parities for PU and FI in a population of 884 Large White pigs. In total, 12 significant SNPs were detected on SSC1, 3, 4, 9, and 14, which collectively explained 1–1.79% of the phenotypic variance for PU from parity 1 to 4, and 2.58–4.11% for FI at different stages. Of these, seven SNPs were located within 16 QTL regions related to swine reproductive traits. One QTL region was associated with birth body weight (related to PU) and contained the peak SNP MARC0040730, and another was associated with plasma FSH concentration (related to FI) and contained the SNP MARC0031325. Finally, some positional candidate genes for PU and FI were identified because of their roles in prenatal skeletal muscle development, fetal energy substrate, pre-implantation, and the expression of mammary gland epithelium. Identification of novel variants and candidate genes will greatly advance our understanding of the genetic mechanisms of PU and FI, and suggest a specific opportunity for improving marker assisted selection or genomic selection in pigs.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

BACKGROUND

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.