Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions

BACKGROUND

Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific variants. Interactions between genes and BP-associated factors may explain some "missing heritability." Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown.

METHODS

We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers ("ever smokers," N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations.

RESULTS

Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD > 10) but decreased SBP (7 mmHg) in light smokers (CPD ≤ 10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached P < 1 × 10(-6).

DISCUSSION

Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking.

PCSK9 variation and association with blood pressure in African Americans: preliminary findings from the HyperGEN and REGARDS studies

Proprotein convertase subtilisin/kexin type 9 (encoded by PCSK9) plays a well-known role in the regulation of low-density lipoprotein (LDL) receptors, and an inhibitor of this enzyme is a promising new therapeutic for hyperlipidemia. Recently, animal and human studies also implicate PCSK9 genetic variation in the regulation of blood pressure. The goal of this study was to examine if common and rare polymorphisms in PCSK9 are associated with blood pressure in an African-American population at high risk for cardiovascular disease. Using genomic data assayed on the Affymetrix 6.0 array (n = 1199) and the Illumina HumanExome Beadchip (n = 1966) from the Hypertension Genetic Epidemiology Network (HyperGEN), we tested the association of PCSK9 polymorphisms with blood pressure. We used linear mixed models and the sequence kernel association test (SKAT) to assess the association of 31 common and 19 rare variants with blood pressure. The models were adjusted for age, sex, center, smoking status, principal components for ancestry and diabetes as fixed effects and family as a random effect. The results showed a marginally significant effect of two genome-wide association study (GWAS) single-nucleotide polymorphisms (SNPs) (rs12048828: β = 1.8, P = 0.05 and rs9730100: β = 1.0, P = 0.05) with diastolic blood pressure (DBP); however these results were not significant after correction for multiple testing. Rare variants were cumulatively associated with DBP (P = 0.04), an effect that was strengthened by restriction to non-synonymous or stop-gain SNPs (P = 0.02). While gene-based results for DBP did not replicate (P = 0.36), we found an association with SBP (P = 0.04) in the Reasons for Geographic And Racial Differences in Stroke study (REGARDS). The findings here suggest rare variants in PCSK9 may influence blood pressure among African Americans, laying the ground work for further validation studies.

New genetic loci link adipose and insulin biology to body fat distribution

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, we conducted genome-wide association meta-analyses of waist and hip circumference-related traits in up to 224,459 individuals. We identified 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and an additional 19 loci newly associated with related waist and hip circumference measures (P<5×10⁻⁸). Twenty of the 49 WHRadjBMI loci showed significant sexual dimorphism, 19 of which displayed a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation, and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Defining the role of common variation in the genomic and biological architecture of adult human height

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: the Family Blood Pressure Program

OBJECTIVE

To detect novel loci with age-dependent effects on fasting (≥ 8 h) levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides using 3600 African Americans, 1283 Asians, 3218 European Americans, and 2026 Mexican Americans from the Family Blood Pressure Program (FBPP).

METHODS

Within each subgroup (defined by network, race, and sex), we employed stepwise linear regression (retention p ≤ 0.05) to adjust lipid levels for age, age-squared, age-cubed, body-mass-index, current smoking status, current drinking status, field center, estrogen therapy (females only), as well as antidiabetic, antihypertensive, and antilipidemic medication use. For each trait, we pooled the standardized male and female residuals within each network and race and fit a generalized variance components model that incorporated gene-age interactions. We conducted FBPP-wide and race-specific meta-analyses by combining the p-values of each linkage marker across subgroups using a modified Fisher's method.

RESULTS

We identified seven novel loci with age-dependent effects; four total cholesterol loci from the meta-analysis of Mexican Americans (on chromosomes 2q24.1, 4q21.21, 8q22.2, and 12p11.23) and three high-density lipoprotein loci from the meta-analysis of all FBPP subgroups (on chromosomes 1p12, 14q11.2, and 21q21.1). These loci lacked significant genome-wide linkage or association evidence in the literature and had logarithm of odds (LOD) score ≥ 3 in the meta-analysis with LOD ≥ 1 in at least two network and race subgroups (exclusively of non-European descent).

CONCLUSION

Incorporating gene-age interactions into the analysis of lipids using multi-ethnic cohorts can enhance gene discovery. These interaction loci can guide the selection of families for sequencing studies of lipid-associated variants.

Comparison of two methods for analysis of gene-environment interactions in longitudinal family data: the Framingham heart study

Gene–environment interaction (GEI) analysis can potentially enhance gene discovery for common complex traits. However, genome-wide interaction analysis is computationally intensive. Moreover, analysis of longitudinal data in families is much more challenging due to the two sources of correlations arising from longitudinal measurements and family relationships. GWIS of longitudinal family data can be a computational bottleneck. Therefore, we compared two methods for analysis of longitudinal family data: a methodologically sound but computationally demanding method using the Kronecker model (KRC) and a computationally more forgiving method using the hierarchical linear model (HLM). The KRC model uses a Kronecker product of an unstructured matrix for correlations among repeated measures (longitudinal) and a compound symmetry matrix for correlations within families at a given visit. The HLM uses an autoregressive covariance matrix for correlations among repeated measures and a random intercept for familial correlations. We compared the two methods using the longitudinal Framingham heart study (FHS) SHARe data. Specifically, we evaluated SNP–alcohol (amount of alcohol consumption) interaction effects on high density lipoprotein cholesterol (HDLC). Keeping the prohibitive computational burden of KRC in mind, we limited the analysis to chromosome 16, where preliminary cross-sectional analysis yielded some interesting results. Our first important finding was that the HLM provided very comparable results but was remarkably faster than the KRC, making HLM the method of choice. Our second finding was that longitudinal analysis provided smaller P-values, thus leading to more significant results, than cross-sectional analysis. This was particularly pronounced in identifying GEIs. We conclude that longitudinal analysis of GEIs is more powerful and that the HLM method is an optimal method of choice as compared to the computationally (prohibitively) intensive KRC method.

The role of SNP-loop diuretic interactions in hypertension across ethnic groups in HyperGEN

Blood pressure (BP) is significantly influenced by genetic factors; however, less than 3% of the BP variance has been accounted for by variants identified from genome-wide association studies (GWAS) of primarily European-descent cohorts. Other genetic influences, including gene-environment (GxE) interactions, may explain more of the unexplained variance in BP. African Americans (AA) have a higher prevalence and earlier age of onset of hypertension (HTN) as compared with European Americans (EA); responses to anti-hypertensive drugs vary across race groups. To examine potential interactions between the use of loop diuretics and HTN traits, we analyzed systolic (SBP) and diastolic (DBP) blood BP from 1222 AA and 1231 EA participants in the Hypertension Genetic Epidemiology Network (HyperGEN). Population-specific score tests were used to test associations of SBP and DBP, using a panel of genotyped and imputed single nucleotide polymorphisms (SNPs) for AA (2.9 million SNPs) and EA (2.3 million SNPs). Several promising loci were identified through gene-loop diuretic interactions, although no SNP reached genome-wide significance after adjustment for genomic inflation. In AA, SNPs in or near the genes NUDT12, CHL1, GRIA1, CACNB2, and PYHIN1 were identified for SBP, and SNPs near ID3 were identified for DBP. For EA, promising SNPs for SBP were identified in ESR1 and for DBP in SPATS2L and EYA2. Among these SNPs, none were common across phenotypes or population groups. Biologic plausibility exists for many of the identified genes, suggesting that these are candidate genes for regulation of BP and/or anti-hypertensive drug response. The lack of genome-wide significance is understandable in this small study employing gene-drug interactions. These findings provide a set of prioritized SNPs/candidate genes for future studies in HTN. Studies in more diversified population samples may help identify previously missed variants.

Gene-alcohol interactions identify several novel blood pressure loci including a promising locus near SLC16A9

Alcohol consumption is a known risk factor for hypertension, with recent candidate studies implicating gene-alcohol interactions in blood pressure (BP) regulation. We used 6882 (predominantly) Caucasian participants aged 20-80 years from the Framingham SNP Health Association Resource (SHARe) to perform a genome-wide analysis of SNP-alcohol interactions on BP traits. We used a two-step approach in the ABEL suite to examine genetic interactions with three alcohol measures (ounces of alcohol consumed per week, drinks consumed per week, and the number of days drinking alcohol per week) on four BP traits [systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) pressure]. In the first step, we fit a linear mixed model of each BP trait onto age, sex, BMI, and antihypertensive medication while accounting for the phenotypic correlation among relatives. In the second step, we conducted 1 degree-of-freedom (df) score tests of the SNP main effect, alcohol main effect, and SNP-alcohol interaction using the maximum likelihood estimates (MLE) of the parameters from the first step. We then calculated the joint 2 df score test of the SNP main effect and SNP-alcohol interaction using MixABEL. The effect of SNP rs10826334 (near SLC16A9) on SBP was significantly modulated by both the number of alcoholic drinks and the ounces of alcohol consumed per week (p-values of 1.27E-08 and 3.92E-08, respectively). Each copy of the G-allele decreased SBP by 3.79 mmHg in those consuming 14 drinks per week vs. a 0.461 mmHg decrease in non-drinkers. Index SNPs in 20 other loci exhibited suggestive (p-value ≤ 1E-06) associations with BP traits by the 1 df interaction test or joint 2 df test, including 3 rare variants, one low-frequency variant, and SNPs near/in genes ESRRG, FAM179A, CRIPT-SOCS5, KAT2B, ADCY2, GLI3, ZNF716, SLIT1, PDE3A, KERA-LUM, RNF219-AS1, CLEC3A, FBXO15, and IGSF5. SNP-alcohol interactions may enhance discovery of novel variants with large effects that can be targeted with lifestyle modifications.

Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations

High blood pressure (BP) is more prevalent and contributes to more severe manifestations of cardiovascular disease (CVD) in African Americans than in any other United States ethnic group. Several small African-ancestry (AA) BP genome-wide association studies (GWASs) have been published, but their findings have failed to replicate to date. We report on a large AA BP GWAS meta-analysis that includes 29,378 individuals from 19 discovery cohorts and subsequent replication in additional samples of AA (n = 10,386), European ancestry (EA) (n = 69,395), and East Asian ancestry (n = 19,601). Five loci (EVX1-HOXA, ULK4, RSPO3, PLEKHG1, and SOX6) reached genome-wide significance (p < 1.0 × 10(-8)) for either systolic or diastolic BP in a transethnic meta-analysis after correction for multiple testing. Three of these BP loci (EVX1-HOXA, RSPO3, and PLEKHG1) lack previous associations with BP. We also identified one independent signal in a known BP locus (SOX6) and provide evidence for fine mapping in four additional validated BP loci. We also demonstrate that validated EA BP GWAS loci, considered jointly, show significant effects in AA samples. Consequently, these findings suggest that BP loci might have universal effects across studied populations, demonstrating that multiethnic samples are an essential component in identifying, fine mapping, and understanding their trait variability.

Partial normalization of components of metabolic syndrome does not influence prevalent echocardiographic abnormalities: the HyperGEN study

BACKGROUND AND AIMS

Metabolic syndrome (MetS) is a complex condition characterized by different phenotypes, according to the combinations of risk factors and is associated with cardiovascular abnormalities. Whether control of MetS components by treatment produces improvement in the associated cardiovascular abnormalities is unknown. We investigated whether partial control of components of MetS was associated with less echocardiographic abnormalities than the complete presentation of MetS based on measured components.

METHODS AND RESULTS

We evaluated markers of echocardiographic preclinical cardiovascular disease in MetS (ATP III) defined by measured components or by history of treatment, in 1421 African-American and 1195 Caucasian non-diabetic HyperGEN participants, without prevalent cardiovascular disease or serum creatinine >2 mg/dL. Of 2616 subjects, 512 subjects had MetS by measured components and 328 by history. Hypertension was found in 16% of participants without MetS, 6% of those with MetS by history and 42% of those with MetS by measured components. Obesity and central fat distribution had similar prevalence in both MetS groups (both p < 0.0001 vs. No-MetS). Blood pressure was similar in MetS by history and No-MetS, and lower than in MetS by measured components (p < 0.0001). LV mass and midwall shortening, left atrial (LA) dimension and LA systolic force were similarly abnormal in both MetS groups (all p < 0.0001 vs. No-MetS) without difference between them.

CONCLUSIONS

There is a little impact of control by treatment of single components of MetS (namely hypertension) on echocardiographic abnormalities. Lower blood pressure in participants with MetS by history was not associated with substantially reduced alterations in cardiac geometry and function.

Subclinical, hemodynamic, and echocardiographic abnormalities of high pulse pressure in hypertensive and non-hypertensive adults

BACKGROUND

High pulse pressure (PP) is associated with cardiovascular events, but subclinical abnormalities in cardiac structure and function in relation to high pulse pressure are not well described.

METHODS AND RESULTS

2225 hypertensive and 1380 non-hypertensive participants with adequate echocardiographic left ventricular measurements were evaluated. Non-hypertensives in the highest PP tertile (compared to the lower tertiles) were older (44 years vs. 40 years, p<0.009), had higher systolic pressure [(SBP) 136 mmHg vs. 108 mmHg] and lower diastolic pressure [(DBP) 54 vs. 71 mmHg (p=.0001)], greater BMI (27 vs. 25 kg/m2, p<.001) and more diabetes (4% vs. 2.25%, p<.001). In the hypertensive group, subjects in the highest PP tertile were older (52 vs 42 years), had higher SBP (157 vs. 116 mmHg) but lower DBP (65 vs. 83 mmHg). In the non-hypertensive group, higher PP (>60 mmHG) was associated with a higher frequency of echocardiographic structural and functional abnormalities, specifically, greater posterior and relative wall thickness, longer isovolumic relaxation time, and concentric left ventricular (LV) hypertrophy.

CONCLUSION

In a population-based sample of hypertensive and non-hypertensive participants, higher PP was associated with subclinical abnormalities of cardiac structure and function, which exist even in the absence of hypertension and/or the use of antihypertensive treatment.

Maternal history of hypertension and blood pressure response to potassium intake: the GenSalt Study

The relation between parental history of hypertension and blood pressure response to potassium intake is unknown. A 7-day high-sodium followed by a 7-day high-sodium plus potassium dietary-feeding study was conducted from 2003 to 2005 among 1,871 Chinese participants. Those with a maternal history of hypertension had larger systolic blood pressure responses to potassium compared with those without: -4.31 (95% confidence interval (CI): -4.99, -3.62) mm Hg versus -3.35 (95% CI: -4.00, -2.70) mm Hg, respectively (P(difference) = 0.002). A consistent trend was observed for diastolic blood pressure responses: -1.80 (95% CI: -2.41, -1.20) mm Hg versus -1.35 (95% CI: -1.95, -0.74) mm Hg, respectively (P = 0.07). Stronger associations between early onset maternal hypertension and blood pressure responses were noted, with systolic blood pressure decreases of -4.80 (95% CI: -5.65, -3.95) mm Hg versus -3.55 (95% CI: -4.17, -2.93) mm Hg and diastolic blood pressure decreases of -2.25 (95% CI: -3.01, -1.50) mm Hg versus -1.42 (95% CI: -1.99, -0.85) mm Hg among those with early onset maternal hypertension versus those without, respectively (P = 0.001 and 0.009, respectively). Odds ratios for high potassium sensitivity were 1.36 (95% CI: 0.96, 1.92) and 1.60 (95% CI: 1.08, 2.36) for those with maternal hypertension and early onset maternal hypertension, respectively (P = 0.08 and 0.02, respectively). Potassium supplementation could help to reduce blood pressure among those with a maternal history of hypertension.

An Exploratory Investigation of Genetic Linkage with Body Composition and Fatness Phenotypes: The Québec Family Study

In the present investigation, we have attempted to identify regions of the genome in which "obesity genes" potentially reside using robust sib-pair linkage analysis. Data were collected on 1,628 individuals in 301 nuclear families residing in the environs of Québec City during the period 1978-1981. In addition to traditional blood group antigens and enzyme polymorphisms, several phenotypes in the obesity domain that are associated with increased morbidity were assessed, including measures relating to heaviness (i.e., the body mass index), body composition and nutrient partitioning (i.e., % body fat), and regional fat distribution without and with standardization for total fat mass (i.e., the sum of six skinfold thicknesses, and the ratio of the sums of trunk to extremity skinfold thicknesses). Three consistent patterns of potential linkage relationships with obesity phenotypes were revealed in these data, involving the marker loci adenosine deaminase, the Kell blood group antigen, and esterase D, which identify chromosomal regions 20q13, 7q33, and 13q14, respectively. Other potential linkages also were identified in the short arm of chromosome 1, interesting because of the presence of the db and fa loci on homologous regions of chromosome 1 in mouse and rat models of obesity, respectively. Each of the tentative linkage relationships reported here warrant follow-up using alternative methods and require replication in independent studies.

Protein Tyrosine Phosphatase 1B Variant Associated with Fat Distribution and Insulin Metabolism

Protein tyrosine phosphatase 1B (PTPN1) affects the regulation of insulin signaling and energy metabolism. We studied whether polymorphisms in the PTPN1 gene impact body fat distribution in the HERITAGE Family Study cohort in 502 white and 276 black subjects. Insulin sensitivity index, glucose disappearance index, acute insulin response to glucose (AIR(glucose)), and the disposition index (DI) were obtained from the frequently sampled intravenous glucose tolerance test. White subjects with the G82G at the PTPN1 IVS6+G82A polymorphism had higher body fat levels (p = 0.031) and sum of eight skinfolds (p = 0.003) and highest subcutaneous fat on the limbs (p = 0.002). G82A subjects had the lowest AIR(glucose) (p = 0.005) and disposition index (p = 0.040). Interaction effects between PTPN1 and leptin receptor gene variants influenced insulin sensitivity index and AIR(glucose) (p from 0.006 to 0.010). The variant PTPN1 Pro387Leu was associated with lower fasting insulin level (p = 0.035) and glucose disappearance index (p = 0.038). In summary, PTPN1 IVS6+G82G homozygotes showed higher levels of all measures of adiposity. G82 allele heterozygotes are potentially at higher risk for type 2 diabetes. Gene-gene interactions between the PTPN1 and leptin receptor genes contributed to the phenotypic variability of insulin sensitivity. The PTPN1 Pro387Leu variant was associated with lower glucose tolerance.

Segregation Analysis of Body Mass Index in an Unselected French-Canadian Sample: The Québec Family Study

Interest in a single gene etiology for obesity, as assessed by the body mass index (BMI), has been spurred recently by reports of a putative recessive major gene for extreme values, which accounts for as much as 40% of the variance. The major gene hypothesis was evaluated here in the Québec Family Study, a random sample of 375 French-Canadian volunteer families. This report represents one component in a more complete investigation of obesity in these families. In contrast to the recent studies, a major gene hypothesis for BMI was not verified here. Although there was a major effect, it did not conform to a Mendelian pattern of transmission. A multifactorial component (i.e., polygenic and/or common environmental factors) accounted for 42% of the phenotypic variance. In addition, evidence of heterogeneity between the generations was found. The heterogeneity was traced to the major non-Mendelian component (which accounted for 0.01% of the variance in parents and over 40% in offspring) rather than to the multifactorial one. These results would suggest that a simple recessive gene mixed model may not be sufficient to explain the familial distribution of the BMI. Several factors which may have contributed to these results include temporal trends and surrogate effects such as those related to variation in body composition and energy balance components.

Adverse metabolic response to regular exercise: is it a rare or common occurrence?

Background

Individuals differ in the response to regular exercise. Whether there are people who experience adverse changes in cardiovascular and diabetes risk factors has never been addressed.

Methodology/Principal Findings

An adverse response is defined as an exercise-induced change that worsens a risk factor beyond measurement error and expected day-to-day variation. Sixty subjects were measured three times over a period of three weeks, and variation in resting systolic blood pressure (SBP) and in fasting plasma HDL-cholesterol (HDL-C), triglycerides (TG), and insulin (FI) was quantified. The technical error (TE) defined as the within-subject standard deviation derived from these measurements was computed. An adverse response for a given risk factor was defined as a change that was at least two TEs away from no change but in an adverse direction. Thus an adverse response was recorded if an increase reached 10 mm Hg or more for SBP, 0.42 mmol/L or more for TG, or 24 pmol/L or more for FI or if a decrease reached 0.12 mmol/L or more for HDL-C. Completers from six exercise studies were used in the present analysis: Whites (N = 473) and Blacks (N = 250) from the HERITAGE Family Study; Whites and Blacks from DREW (N = 326), from INFLAME (N = 70), and from STRRIDE (N = 303); and Whites from a University of Maryland cohort (N = 160) and from a University of Jyvaskyla study (N = 105), for a total of 1,687 men and women. Using the above definitions, 126 subjects (8.4%) had an adverse change in FI. Numbers of adverse responders reached 12.2% for SBP, 10.4% for TG, and 13.3% for HDL-C. About 7% of participants experienced adverse responses in two or more risk factors.

Conclusions/Significance

Adverse responses to regular exercise in cardiovascular and diabetes risk factors occur. Identifying the predictors of such unwarranted responses and how to prevent them will provide the foundation for personalized exercise prescription.

Three ontologies to define phenotype measurement data

BACKGROUND

There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol.

RESULTS

Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository.

CONCLUSION

An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies.

Whole-exome sequencing and an iPSC-derived cardiomyocyte model provides a powerful platform for gene discovery in left ventricular hypertrophy

Rationale: Left ventricular hypertrophy (LVH) is a heritable predictor of cardiovascular disease, particularly in blacks. Objective: Determine the feasibility of combining evidence from two distinct but complementary experimental approaches to identify novel genetic predictors of increased LV mass. Methods: Whole-exome sequencing (WES) was conducted in seven African-American sibling trios ascertained on high average familial LV mass indexed to height (LVMHT) using Illumina HiSeq technology. Identified missense or nonsense (MS/NS) mutations were examined for association with LVMHT using linear mixed models adjusted for age, sex, body weight, and familial relationship. To functionally assess WES findings, human induced pluripotent stem cell-derived cardiomyocytes (induced pluripotent stem cell-CM) were stimulated to induce hypertrophy; mRNA sequencing (RNA-seq) was used to determine gene expression differences associated with hypertrophy onset. Statistically significant findings under both experimental approaches identified LVH candidate genes. Candidate genes were further prioritized by seven supportive criteria that included additional association tests (two criteria), regional linkage evidence in the larger HyperGEN cohort (one criterion), and publically available gene and variant based annotations (four criteria). Results: WES reads covered 91% of the target capture region (of size 37.2 MB) with an average coverage of 65×. WES identified 31,426 MS/NS mutations among the 21 individuals. A total of 295 MS/NS variants in 265 genes were associated with LVMHT with q-value <0.25. Of the 265 WES genes, 44 were differentially expressed (P < 0.05) in hypertrophied cells. Among the 44 candidate genes identified, 5, including HLA-B, HTT, MTSS1, SLC5A12, and THBS1, met 3 of 7 supporting criteria. THBS1 encodes an adhesive glycoprotein that promotes matrix preservation in pressure-overload LVH. THBS1 gene expression was 34% higher in hypertrophied cells (P = 0.0003) and a predicted conserved and damaging NS variant in exon 13 (A2099G) was significantly associated with LVHMT (P = 4 × 10⁻⁶). Conclusion: Combining evidence from cutting-edge genetic and cellular experiments can enable identification of novel LVH risk loci.

Smoking and genetic risk variation across populations of European, Asian, and African American ancestry--a meta-analysis of chromosome 15q25

Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations. Association results for a dichotomized cigarettes smoked per day phenotype in 27 datasets (European ancestry (N = 14,786), Asian (N = 6,889), and African American (N = 10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations. We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (P < 0.01) in each of these three populations (odds ratio [OR] = 1.33, 95% CI = 1.25-1.42, P = 1.1 × 10(-17) in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans. The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments.

Performance of genotype imputations using data from the 1000 Genomes Project

Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq <0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.

Heritability of submaximal exercise heart rate response to exercise training is accounted for by nine SNPs

Endurance training-induced changes in hemodynamic traits are heritable. However, few genes associated with heart rate training responses have been identified. The purpose of our study was to perform a genome-wide association study to uncover DNA sequence variants associated with submaximal exercise heart rate training responses in the HERITAGE Family Study. Heart rate was measured during steady-state exercise at 50 W (HR50) on 2 separate days before and after a 20-wk endurance training program in 483 white subjects from 99 families. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. After quality control procedures, 320,000 single-nucleotide polymorphisms (SNPs) were available for the genome-wide association study analyses, which were performed using the MERLIN software package (single-SNP analyses and conditional heritability tests) and standard regression models (multivariate analyses). The strongest associations for HR50 training response adjusted for age, sex, body mass index, and baseline HR50 were detected with SNPs at the YWHAQ locus on chromosome 2p25 (P = 8.1 × 10(-7)), the RBPMS locus on chromosome 8p12 (P = 3.8 × 10(-6)), and the CREB1 locus on chromosome 2q34 (P = 1.6 × 10(-5)). In addition, 37 other SNPs showed P values <9.9 × 10(-5). After removal of redundant SNPs, the 10 most significant SNPs explained 35.9% of the ΔHR50 variance in a multivariate regression model. Conditional heritability tests showed that nine of these SNPs (all intragenic) accounted for 100% of the ΔHR50 heritability. Our results indicate that SNPs in nine genes related to cardiomyocyte and neuronal functions, as well as cardiac memory formation, fully account for the heritability of the submaximal heart rate training response.