Transcriptional Variabilities in Human hiPSC-derived Cardiomyocytes: All Genes Are Not Equal and Their Robustness May Foretell Donor's Disease Susceptibility.

Human induced pluripotent stem cells (hiPSCs) are frequently used to study disease-associated variations. We characterized transcriptional variability from a hiPSC-derived cardiomyocyte (hiPSC-CM) study of left ventricular hypertrophy (LVH) using donor samples from the HyperGEN study. Multiple hiPSC-CM differentiations over reprogramming events (iPSC generation) across 7 donors were used to assess variabilities from reprogramming, differentiation, and donor LVH status. Variability arising from pathological alterations was assessed using a cardiac stimulant applied to the hiPSC-CMs to trigger hypertrophic responses. We found that for most genes (73.3%~85.5%), technical variability was smaller than biological variability. Further, we identified and characterized lists of "noise" genes showing greater technical variability and "signal" genes showing greater biological variability. Together, they support a "genetic robustness" hypothesis of disease-modeling whereby cellular response to relevant stimuli in hiPSC-derived somatic cells from diseased donors tends to show more transcriptional variability. Our findings suggest that hiPSC-CMs can provide a valid model for cardiac hypertrophy and distinguish between technical and disease-relevant transcriptional changes.

Telemedicine Critical Care-Mediated Mortality Reductions in Lower-Performing Patient Diagnosis Groups: A Prospective, Before and After Study.

OBJECTIVES: Studies evaluating telemedicine critical care (TCC) have shown mixed results. We prospectively evaluated the impact of TCC implementation on risk-adjusted mortality among patients stratified by pre-TCC performance. DESIGN: Prospective, observational, before and after study. SETTING: Three adult ICUs at an academic medical center. PATIENTS: A total of 2,429 patients in the pre-TCC (January to June 2016) and 12,479 patients in the post-TCC (January 2017 to June 2019) periods. INTERVENTIONS: TCC implementation which included an acuity-driven workflow targeting an identified "lower-performing" patient group, defined by ICU admission in an Acute Physiology and Chronic Health Evaluation diagnoses category with a pre-TCC standardized mortality ratio (SMR) of greater than 1.5. MEASUREMENTS AND MAIN RESULTS: The primary outcome was risk-adjusted hospital mortality. Risk-adjusted hospital length of stay (HLOS) was also studied. The SMR for the overall ICU population was 0.83 pre-TCC and 0.75 post-TCC, with risk-adjusted mortalities of 10.7% and 9.5% (p = 0.09). In the identified lower-performing patient group, which accounted for 12.6% (n = 307) of pre-TCC and 13.3% (n = 1671) of post-TCC ICU patients, SMR decreased from 1.61 (95% CI, 1.21-2.01) pre-TCC to 1.03 (95% CI, 0.91-1.15) post-TCC, and risk-adjusted mortality decreased from 26.4% to 16.9% (p < 0.001). In the remaining ("higher-performing") patient group, there was no change in pre- versus post-TCC SMR (0.70 [0.59-0.81] vs 0.69 [0.64-0.73]) or risk-adjusted mortality (8.5% vs 8.4%, p = 0.86). There were no pre- to post-TCC differences in standardized HLOS ratio or risk-adjusted HLOS in the overall cohort or either performance group. CONCLUSIONS: In well-staffed and overall higher-performing ICUs in an academic medical center, Acute Physiology and Chronic Health Evaluation granularity allowed identification of a historically lower-performing patient group that experienced a striking TCC-associated reduction in SMR and risk-adjusted mortality. This study provides additional evidence for the relationship between pre-TCC performance and post-TCC improvement.

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.

Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program (n = 63,302) and UK Biobank (n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Gene-lifestyle interactions in the genomics of human complex traits.

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

Whole-Exome Sequencing and hiPSC Cardiomyocyte Models Identify MYRIP, TRAPPC11, and SLC27A6 of Potential Importance to Left Ventricular Hypertrophy in an African Ancestry Population.

Background: Indices of left ventricular (LV) structure and geometry represent useful intermediate phenotypes related to LV hypertrophy (LVH), a predictor of cardiovascular (CV) disease (CVD) outcomes. Methods and Results: We conducted an exome-wide association study of LV mass (LVM) adjusted to height2.7, LV internal diastolic dimension (LVIDD), and relative wall thickness (RWT) among 1,364 participants of African ancestry (AAs) in the Hypertension Genetic Epidemiology Network (HyperGEN). Both single-variant and gene-based sequence kernel association tests were performed to examine whether common and rare coding variants contribute to variation in echocardiographic traits in AAs. We then used a data-driven procedure to prioritize and select genes for functional validation using a human induced pluripotent stem cell cardiomyocyte (hiPSC-CM) model. Three genes [myosin VIIA and Rab interacting protein (MYRIP), trafficking protein particle complex 11 (TRAPPC11), and solute carrier family 27 member 6 (SLC27A6)] were prioritized based on statistical significance, variant functional annotations, gene expression in the hiPSC-CM model, and prior biological evidence and were subsequently knocked down in the hiPSC-CM model. Expression profiling of hypertrophic gene markers in the knockdowns suggested a decrease in hypertrophic expression profiles. MYRIP knockdowns showed a significant decrease in atrial natriuretic factor (NPPA) and brain natriuretic peptide (NPPB) expression. Knockdowns of the heart long chain fatty acid (FA) transporter SLC27A6 resulted in downregulated caveolin 3 (CAV3) expression, which has been linked to hypertrophic phenotypes in animal models. Finally, TRAPPC11 knockdown was linked to deficient calcium handling. Conclusions: The three genes are biologically plausible candidates that provide new insight to hypertrophic pathways.

Donor-specific phenotypic variation in hiPSC cardiomyocyte-derived exosomes impacts endothelial cell function.

Exosomes are an important mechanism of cell-cell interaction in the cardiovascular system, both in maintaining homeostasis and in stress response. Interindividual differences that alter content in exosomes may play a role in cardiovascular disease pathology. To study the effect of interindividual cardiomyocyte (CM) variation, we characterized exosomal content in phenotypically diverse human induced pluripotent stem cell-derived CMs (hiPSC-CMs). Cell lines were generated from six participants in the HyperGEN cohort: three with left ventricular hypertrophy (LVH) and three with normal left ventricular mass (LVM). Sequence analysis of the intracellular and exosomal RNA populations showed distinct expression pattern differences between hiPSC-CM lines derived from individuals with LVH and those with normal LVM. Functional analysis of hiPSC-endothelial cells (hiPSC-ECs) treated with exosomes from both hiPSC-CM groups showed significant variation in response, including differences in tube formation, migration, and proliferation. Overall, treatment of hiPSC-ECs with exosomes resulted in significant expression changes associated with angiogenesis and endothelial cell vasculogenesis. However, the hiPSC-ECs treated with exosomes from the LVH-affected donors exhibited significantly increased proliferation but decreased tube formation and migration, suggesting angiogenic dysregulation.NEW & NOTEWORTHY The intracellular RNA and the miRNA content in exosomes are significantly different in hiPSC-CMs derived from LVH-affected individuals compared with those from unaffected individuals. Treatment of endothelial cells with these exosomes functionally affects cellular phenotypes in a donor-specific manner. These findings provide novel insight into underlying mechanisms of hypertrophic cell signaling between different cell types. With a growing interest in stem cells and exosomes for cardiovascular therapeutic use, this also provides information important for regenerative medicine.

Three genetic-environmental networks for human personality.

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.

Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31.

High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD) and is more prevalent in African Americans as compared to other US groups. Although large, population-based genome-wide association studies (GWAS) have identified over 300 common polymorphisms modulating inter-individual BP variation, largely in European ancestry subjects, most of them do not localize to regions previously identified through family-based linkage studies. This discrepancy has remained unexplained despite the statistical power differences between current GWAS and prior linkage studies. To address this issue, we performed genome-wide linkage analysis of BP traits in African-American families from the Family Blood Pressure Program (FBPP) and genotyped on the Illumina Human Exome BeadChip v1.1. We identified a genomic region on chromosome 1q31 with LOD score 3.8 for pulse pressure (PP), a region we previously implicated in DBP studies of European ancestry families. Although no reported GWAS variants map to this region, combined linkage and association analysis of PP identified 81 rare and low frequency exonic variants accounting for the linkage evidence. Replication analysis in eight independent African ancestry cohorts (N = 16,968) supports this specific association with PP (P = 0.0509). Additional association and network analyses identified multiple potential candidate genes in this region expressed in multiple tissues and with a strong biological support for a role in BP. In conclusion, multiple genes and rare variants on 1q31 contribute to PP variation. Beyond producing new insights into PP, we demonstrate how family-based linkage and association studies can implicate specific rare and low frequency variants for complex traits.

Blood Pressure Genetic Risk Score Predicts Blood Pressure Responses to Dietary Sodium and Potassium: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity).

We examined the association between genetic risk score (GRS) for blood pressure (BP), based on single nucleotide polymorphisms identified in previous BP genome-wide association study meta-analyses, and salt and potassium sensitivity of BP among participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity). The GenSalt study was conducted among 1906 participants who underwent a 7-day low-sodium (51.3 mmol sodium/d), 7-day high-sodium (307.8 mmol sodium/d), and 7-day high-sodium plus potassium (60 mmol potassium/d) intervention. BP was measured 9× at baseline and at the end of each intervention period using a random zero sphygmomanometer. Associations between systolic BP (SBP), diastolic BP, and mean arterial pressure GRS and respective SBP, diastolic BP, and mean arterial pressure responses to the dietary interventions were assessed using mixed linear regression models that accounted for familial dependencies and adjusted for age, sex, field center, body mass index, and baseline BP. As expected, baseline SBP, diastolic BP, and mean arterial pressure significantly increased per quartile increase in GRS (P=2.7×10-8, 9.8×10-8, and 6.4×10-6, respectively). In contrast, increasing GRS quartile conferred smaller SBP, diastolic BP, and mean arterial pressure responses to the low-sodium intervention (P=1.4×10-3, 0.02, and 0.06, respectively) and smaller SBP responses to the high-sodium and potassium interventions (P=0.10 and 0.05). In addition, overall findings were similar when examining GRS as a continuous measure. Contrary to our initial hypothesis, we identified an inverse relationship between BP GRS and salt and potassium sensitivity of BP. These data may provide novel implications on the relationship between BP responses to dietary sodium and potassium and hypertension.

Tissue-specific DNA methylation is conserved across human, mouse, and rat, and driven by primary sequence conservation.

BACKGROUND: Uncovering mechanisms of epigenome evolution is an essential step towards understanding the evolution of different cellular phenotypes. While studies have confirmed DNA methylation as a conserved epigenetic mechanism in mammalian development, little is known about the conservation of tissue-specific genome-wide DNA methylation patterns. RESULTS: Using a comparative epigenomics approach, we identified and compared the tissue-specific DNA methylation patterns of rat against those of mouse and human across three shared tissue types. We confirmed that tissue-specific differentially methylated regions are strongly associated with tissue-specific regulatory elements. Comparisons between species revealed that at a minimum 11-37% of tissue-specific DNA methylation patterns are conserved, a phenomenon that we define as epigenetic conservation. Conserved DNA methylation is accompanied by conservation of other epigenetic marks including histone modifications. Although a significant amount of locus-specific methylation is epigenetically conserved, the majority of tissue-specific DNA methylation is not conserved across the species and tissue types that we investigated. Examination of the genetic underpinning of epigenetic conservation suggests that primary sequence conservation is a driving force behind epigenetic conservation. In contrast, evolutionary dynamics of tissue-specific DNA methylation are best explained by the maintenance or turnover of binding sites for important transcription factors. CONCLUSIONS: Our study extends the limited literature of comparative epigenomics and suggests a new paradigm for epigenetic conservation without genetic conservation through analysis of transcription factor binding sites.

Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci.

Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.

Rare coding variants associated with blood pressure variation in 15 914 individuals of African ancestry.

OBJECTIVES: Hypertension is a major risk factor for all cardiovascular diseases, especially among African Americans. This study focuses on identifying specific blood pressure (BP) genes using 15 914 individuals of African ancestry from eight cohorts (Africa America Diabetes Mellitus, Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in young Adults, Genetics Network, Genetic Epidemiology Network of Arteriopathy, Howard University Family Study, Hypertension Genetic Epidemiology Network, and Loyola University Chicago Cohort) to further genetic findings in this population which has generally been underrepresented in BP studies. METHODS: We genotyped and performed various single variant and gene-based exome-wide analyses on 15 914 individuals on the Illumina HumanExome Beadchip v1.0 or v1.1 to test association with SBP and DBP long-term average residuals that were adjusted for age, age-squared, sex, and BMI. RESULTS: We identified rare variants affecting SBP and DBP in 10 genes: AFF1, GAPDHS, SLC28A3, COL6A1, CRYBA2, KRBA1, SEL1L3, YOD1, CCDC13, and QSOX1. Prior experimental evidence for six of these 10 candidate genes supports their involvement in cardiovascular mechanisms, corroborating their potential roles in BP regulation. CONCLUSION: Although our results require replication or validation due to their low numbers of carriers, and an ethnicity-specific genotyping array may be more informative, this study, which has identified several candidate genes in this population most susceptible to hypertension, presents one of the largest African-ancestry BP studies to date and the largest including analysis of rare variants.

Whole exome analyses to examine the impact of rare variants on left ventricular traits in African American participants from the HyperGEN and GENOA studies.

Left ventricular (LV) hypertrophy, highest in prevalence among African Americans, is an established risk factor heart failure. Several genome wide association studies have identified common variants associated with LV-related quantitative-traits in African Americans. To date, however, the effect of rare variants on these traits has not been extensively studied, especially in minority groups. We therefore investigated the association between rare variants and LV traits among 1,934 African Americans using exome chip data from the Hypertension Genetic Epidemiology Network (HyperGEN) study, with replication in 1,090 African American from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We used single-variant analyses and gene-based tests to investigate the association between 86,927 variants and six structural and functional LV traits including LV mass, LV internal dimension-diastole, relative wall thickness, left atrial dimension (LAD), fractional shortening (FS), and the ratio of LV early-to-late transmitral velocity (E/A ratio). Only rare variants (MAF <1% and <5%) were considered in gene-based analyses. In gene-based analyses, we found a statistically significant association between potassium voltage-gated channel subfamily H member 4 (KCNH4) and E/A ratio (P=8.7*10-8 using a burden test). Endonuclease G (ENDOG) was associated with LAD using the Madsen Browning weighted burden (MB) test (P=1.4*10-7). Neither gene result was replicated in GENOA, but the direction of effect of single variants in common was comparable. G protein-coupled receptor 55 (GPR55) was marginally associated with LAD in HyperGEN (P=3.2*10-5 using the MB test) and E/A ratio in GENOA, but with opposing directions of association for variants in common (P=0.03 for the MB test). No single variant was statistically significantly associated with any trait after correcting for multiple testing. The findings in this study highlight the potential cumulative contributions of rare variants to LV traits which, if validated, could improve our understanding of heart failure in African Americans.

Genetic Susceptibility to Lipid Levels and Lipid Change Over Time and Risk of Incident Hyperlipidemia in Chinese Populations.

BACKGROUND: Multiple genetic loci associated with lipid levels have been identified predominantly in Europeans, and the issue of to what extent these genetic loci can predict blood lipid levels increases over time and the incidence of future hyperlipidemia remains largely unknown. METHODS AND RESULTS: We conducted a meta-analysis of genome-wide association studies of lipid levels in 8344 subjects followed by replication studies including 14 739 additional individuals. We replicated 17 previously reported loci. We also newly identified 3 Chinese-specific variants in previous regions (HLA-C, LIPG, and LDLR) with genome-wide significance. Almost all the variants contributed to lipid levels change and incident hyperlipidemia >8.1-year follow-up among 6428 individuals of a prospective cohort study. The strongest associations for lipid levels change were detected at LPL, TRIB1, APOA1-C3-A4-A5, LIPC, CETP, and LDLR (P range from 4.84×10(-4) to 4.62×10(-18)), whereas LPL, TRIB1, ABCA1, APOA1-C3-A4-A5, CETP, and APOE displayed significant strongest associations for incident hyperlipidemia (P range from 1.20×10(-3) to 4.67×10(-16)). The 4 lipids genetic risk scores were independently associated with linear increases in their corresponding lipid levels and risk of incident hyperlipidemia. A C-statistics analysis showed significant improvement in the prediction of incident hyperlipidemia on top of traditional risk factors including the baseline lipid levels. CONCLUSIONS: These findings identified some evidence for allelic heterogeneity in Chinese when compared with Europeans in relation to lipid associations. The individual variants and those cumulative effects were independent risk factors for lipids increase and incident hyperlipidemia.

Genetic association of left ventricular mass assessed by M-mode and two-dimensional echocardiography.

BACKGROUND: Left ventricular mass offers prognostic information for assessing cardiovascular disease risk. M-mode and two-dimensional (2D) echocardiographically-derived left ventricular mass values have shown high accuracy and reproducibility; however, no studies to date have compared left ventricular mass genetic association findings on the basis of both the methods. The aim of this study was to compare associations of single-nucleotide polymorphisms (SNPs) from genome-wide association study analyses of left ventricular mass using both methods in the same cohort. METHODS AND RESULTS: Left ventricular mass was determined using 2D and M-mode echocardiography in 711 patients (390 women); SNP genotype data were obtained using the Genome-wide Human SNP Array 6.0. Genome-wide association study analyses were performed to obtain panels of SNPs associated with left ventricular mass and left ventricular mass index. The unindexed left ventricular mass showed excellent agreement [M-mode: 170 ±â€Š47 vs. 2D: 178 ±â€Š56 g; intraclass correlation coefficient 0.929 (95% confidence interval 0.932, 0.909)]. The presence of left ventricular hypertrophy based on M-mode and 2D-derived left ventricular mass index values showed moderate agreement (kappa = 0.49). Eleven SNPs showed suggestive association with at least two of the four left ventricular mass traits, with one SNP in CDH13 common to all four derived traits. CONCLUSION: M-mode and 2D echocardiography left ventricular mass measurements in the same cohort identified suggestive genetic associations, both shared and unshared, suggesting common left ventricular mass biology underlying the two measures of left ventricular mass. The combined use of M-mode and 2D echo is a novel approach that may increase the yield of genetic association with left ventricular mass.

A custom correlation coefficient (CCC) approach for fast identification of multi-SNP association patterns in genome-wide SNPs data.

Complex diseases are often associated with sets of multiple interacting genetic factors and possibly with unique sets of the genetic factors in different groups of individuals (genetic heterogeneity). We introduce a novel concept of custom correlation coefficient (CCC) between single nucleotide polymorphisms (SNPs) that address genetic heterogeneity by measuring subset correlations autonomously. It is used to develop a 3-step process to identify candidate multi-SNP patterns: (1) pairwise (SNP-SNP) correlations are computed using CCC; (2) clusters of so-correlated SNPs identified; and (3) frequencies of these clusters in disease cases and controls compared to identify disease-associated multi-SNP patterns. This method identified 42 candidate multi-SNP associations with hypertensive heart disease (HHD), among which one cluster of 22 SNPs (six genes) included 13 in SLC8A1 (aka NCX1, an essential component of cardiac excitation-contraction coupling) and another of 32 SNPs had 29 from a different segment of SLC8A1. While allele frequencies show little difference between cases and controls, the cluster of 22 associated alleles were found in 20% of controls but no cases and the other in 3% of controls but 20% of cases. These suggest that both protective and risk effects on HHD could be exerted by combinations of variants in different regions of SLC8A1, modified by variants from other genes. The results demonstrate that this new correlation metric identifies disease-associated multi-SNP patterns overlooked by commonly used correlation measures. Furthermore, computation time using CCC is a small fraction of that required by other methods, thereby enabling the analyses of large GWAS datasets.