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

Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci

BACKGROUND

Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs).

RESULTS

We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp.

CONCLUSIONS

We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.

No association of nineteen COX-2 gene variants to preclinical markers of atherosclerosis The Cardiovascular Risk in Young Finns Study

Backgroud

The role of cyclooxygenase-2 (COX-2) single nucleotide polymorphisms has mostly been studied in relation to advanced atherosclerosis, but little is known how they contribute to preclinical disease. In the present study we analyzed whether COX-2 gene variants associate independently with the early subclinical markers of atherosclerosis, carotid intima-media thickness and carotid artery distensibility in a population of young healthy Caucasian adults.

Methods

SNPs for association analysis were collected from the COX-2 gene and 5 kb up- and downstream of it. There were 19 SNPs available for analysis, four genotyped and fifteen imputed. Genotype data was available for 2442 individuals participating in the Cardiovascular Risk in Young Finns Study. Genotype imputation was performed using MACH 1.0 and HapMap II CEU (release 22) samples as reference. Association analysis was performed using linear regression with an additive model. PLINK was used for true genotyped SNPs and ProbABEL for imputed genotype dosages. False discovery rate was used to take into account multiple testing bias.

Results

Two of the COX-2 variants (rs689470, rs689462) associated with distensibility (p = 0.005) under the linear regression additive model. After adjustment with gender, age, body mass index and smoking status, association between these SNPs and distensibility remained significant (p = 0.031). Subjects carrying the minor alleles had higher value of carotid artery distensibility compared to the major allele homozygotes. However, after correcting p-values for multiple testing bias using false discovery rate, association was lost. Another COX-2 variant rs4648261 associated with mean carotid intima-media thickness (p = 0.046) and maximal carotid intima-media thickness (p = 0.048) in the linear regression model. Subjects carrying the minor allele of rs4648261 had lower values of mean and maximal carotid intima-media thickness compared to subjects homozygote for major allele. After adjustments the associations were lost with both mean and maximal carotid intima-media thickness. Thus, no statistically significant associations of the studied COX-2 variants with carotid artery distensibility or carotid intima-media thickness were found.

Conclusions

Our results suggest that in a Finnish population, there are no significant associations between COX-2 variants and early atherosclerotic changes in young adulthood.

Evidence of differential allelic effects between adolescents and adults for plasma high-density lipoprotein

A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25–30% of heritability for each trait. Little is known about how these loci affect lipid levels in early life, but there is evidence that genetic effects on HDL- and LDL-cholesterol (HDL-C, LDL-C) and triglycerides vary with age. We studied Australian adults (N = 10,151) and adolescents (N = 2,363) who participated in twin and family studies and for whom we have lipid phenotypes and genotype information for 91 of the 95 genetic variants. Heterogeneity tests between effect sizes in adult and adolescent cohorts showed an excess of heterogeneity for HDL-C (p_Het<0.05 at 5 out of 37 loci), but no more than expected by chance for LDL-C (1 out of 14 loci), or trigycerides (0 out 24). There were 2 (out of 5) with opposite direction of effect in adolescents compared to adults for HDL-C, but none for LDL-C. The biggest difference in effect size was for LDL-C at rs6511720 near LDLR, adolescents (0.021±0.033 mmol/L) and adults (0.157±0.023 mmol/L), p_Het = 0.013; followed by ZNF664 (p_Het = 0.018) and PABPC4 (p_Het = 0.034) for HDL-C. Our findings suggest that some of the previously identified variants associate differently with lipid traits in adolescents compared to adults, either because of developmental changes or because of greater interactions with environmental differences in adults.

Cohort Profile: the international childhood cardiovascular cohort (i3C) consortium

This is a consortium of large children's cohorts that contain measurements of major cardiovascular disease (CVD) risk factors in childhood and had the ability to follow those cohorts into adulthood. The purpose of this consortium is to enable the pooling of data to increase power, most importantly for the follow-up of CVD events in adulthood. Within the consortium, we hope to be able to obtain data on the independent effects of childhood and early adult levels of CVD risk factors on subsequent CVD occurrence.

Genome-wide association study of genetic determinants of LDL-c response to atorvastatin therapy: importance of Lp(a)

We carried out a genome-wide association study (GWAS) of LDL-c response to statin using data from participants in the Collaborative Atorvastatin Diabetes Study (CARDS; n = 1,156), the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT; n = 895), and the observational phase of ASCOT (n = 651), all of whom were prescribed atorvastatin 10 mg. Following genome-wide imputation, we combined data from the three studies in a meta-analysis. We found associations of LDL-c response to atorvastatin that reached genome-wide significance at rs10455872 (P = 6.13 × 10(-9)) within the LPA gene and at two single nucleotide polymorphisms (SNP) within the APOE region (rs445925; P = 2.22 × 10(-16) and rs4420638; P = 1.01 × 10(-11)) that are proxies for the ε2 and ε4 variants, respectively, in APOE. The novel association with the LPA SNP was replicated in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial (P = 0.009). Using CARDS data, we further showed that atorvastatin therapy did not alter lipoprotein(a) [Lp(a)] and that Lp(a) levels accounted for all of the associations of SNPs in the LPA gene and the apparent LDL-c response levels. However, statin therapy had a similar effect in reducing cardiovascular disease (CVD) in patients in the top quartile for serum Lp(a) levels (HR = 0.60) compared with those in the lower three quartiles (HR = 0.66; P = 0.8 for interaction). The data emphasize that high Lp(a) levels affect the measurement of LDL-c and the clinical estimation of LDL-c response. Therefore, an apparently lower LDL-c response to statin therapy may indicate a need for measurement of Lp(a). However, statin therapy seems beneficial even in those with high Lp(a).

Nonparametric evaluation of quantitative traits in population-based association studies when the genetic model is unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.

Longitudinal replication studies of GWAS risk SNPs influencing body mass index over the course of childhood and adulthood

Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for longitudinal associations with BMI during childhood (3-17 years) and adulthood (18-45 years) for 658 subjects. We also proposed a heuristic forward search for the best joint effect model to explain the longitudinal BMI variation. After using false discovery rate (FDR) to adjust for multiple tests, childhood and adulthood BMI were found to be significantly associated with six SNPs each (q-value<0.05), with one SNP associated with both BMI measurements: KCTD15 rs29941 (q-value<7.6*10-4). These 12 SNPs are located at or near genes either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 '-end and rs1121980 and rs8050136 downstream, were associated with childhood and adulthood BMI, respectively, and the risk effects of rs6499640 and rs1121980 increased as birth weight decreased. The best joint effect model for childhood and adulthood BMI contained 14 and 15 SNPs each, with 11 in common, and the percentage of explained variance increased from 0.17% and 9.0*10(-6)% to 2.22% and 2.71%, respectively. In summary, this study evidenced the presence of long-term major effects of genes on obesity development, implicated in pathways related to neural development and cell metabolism, and different sets of genes associated with childhood and adulthood BMI, respectively. The gene effects can vary with age and be modified by prenatal development. The best joint effect model indicated that multiple variants with effects that are weak or absent alone can nevertheless jointly exert a large longitudinal effect on BMI.

Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey--a meta-analysis of three independent studies

Background

Genome-wide association studies (GWASs) have identified a large number of variants (SNPs) associating with an increased risk of coronary artery disease (CAD). Recently, the CARDIoGRAM consortium published a GWAS based on the largest study population so far. They successfully replicated twelve already known associations and discovered thirteen new SNPs associating with CAD. We examined whether the genetic profiling of these variants improves prediction of subclinical atherosclerosis – i.e., carotid intima-media thickness (CIMT) and carotid artery elasticity (CAE) – beyond classical risk factors.

Subjects and Methods

We genotyped 24 variants found in a population of European ancestry and measured CIMT and CAE in 2001 and 2007 from 2,081, and 2,015 subjects (aged 30–45 years in 2007) respectively, participating in the Cardiovascular Risk in Young Finns Study (YFS). The Bogalusa Heart Study (BHS; n = 1179) was used as a replication cohort (mean age of 37.5). For additional replication, a sub-sample of 5 SNPs was genotyped for 1,291 individuals aged 46–76 years participating in the Health 2000 population survey. We tested the impact of genetic risk score (GRS_24SNP/CAD) calculated as a weighted (by allelic odds ratios for CAD) sum of CAD risk alleles from the studied 24 variants on CIMT, CAE, the incidence of carotid atherosclerosis and the progression of CIMT and CAE during a 6-year follow-up.

Results

CIMT or CAE did not significantly associate with GRS_24SNP/CAD before or after adjusting for classical CAD risk factors (p>0.05 for all) in YFS or in the BHS. CIMT and CAE associated with only one SNP each in the YFS. The findings were not replicated in the replication cohorts. In the meta-analysis CIMT or CAE did not associate with any of the SNPs.

Conclusion

Genetic profiling, by using known CAD risk variants, should not improve risk stratification for subclinical atherosclerosis beyond conventional risk factors among healthy young adults.

A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.

Genetic variants and blood pressure in a population-based cohort: the Cardiovascular Risk in Young Finns study

Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2357). Blood pressure was measured at baseline in 1980 (age 3-18 years) and in follow-ups in 1983, 1986, 2001, and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped, and 3 genetic risk scores associated with systolic and diastolic blood pressures and their combination were derived for all of the participants. Effects of the genetic risk score were 0.47 mm Hg for systolic and 0.53 mm Hg for diastolic blood pressures (both P<0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 years onward (β=0.68 mm Hg; P=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (P<0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood; individuals with several susceptibility alleles have, on average, a 0.5-mm Hg higher blood pressure, and this trajectory continues from childhood to adulthood.

A gene-centric association scan for Coagulation Factor VII levels in European and African Americans: the Candidate Gene Association Resource (CARe) Consortium

Polymorphisms in several distinct genomic regions, including the F7 gene, were recently associated with factor VII (FVII) levels in European Americans (EAs). The genetic determinants of FVII in African Americans (AAs) are unknown. We used a 50,000 single nucleotide polymorphism (SNP) gene-centric array having dense coverage of over 2,000 candidate genes for cardiovascular disease (CVD) pathways in a community-based sample of 16,324 EA and 3898 AA participants from the Candidate Gene Association Resource (CARe) consortium. Our aim was the discovery of new genomic loci and more detailed characterization of existing loci associated with FVII levels. In EAs, we identified three new loci associated with FVII, of which APOA5 on chromosome 11q23 and HNF4A on chromosome 20q12-13 were replicated in a sample of 4289 participants from the Whitehall II study. We confirmed four previously reported FVII-associated loci (GCKR, MS4A6A, F7 and PROCR) in CARe EA samples. In AAs, the F7 and PROCR regions were significantly associated with FVII. Several of the FVII-associated regions are known to be associated with lipids and other cardiovascular-related traits. At the F7 locus, there was evidence of at least five independently associated SNPs in EAs and three independent signals in AAs. Though the variance in FVII explained by the existing loci is substantial (20% in EA and 10% in AA), larger sample sizes and investigation of lower frequency variants may be required to identify additional FVII-associated loci in EAs and AAs and further clarify the relationship between FVII and other CVD risk factors.

Lipoprotein association studies: taking stock and moving forward

PURPOSE OF REVIEW

Over the last year, huge progress has been made using SNP arrays to identify genes associated with lipid traits.

RECENT FINDINGS

The single genome-wide association studies (GWASs) may have reached a limit in identifying novel genes, but there has been tremendous progress from collaborative consortia examining pooled data from GWASs, often employing SNP imputation. For example, the Global Lipid Consortium reported that 56 of the 95 lipid-associated loci were novel to the pooled analysis from over 100 000 individuals. Complementary to this, a gene-centric approach, such as the 50K IBC HumanCVD chip, which provides deep coverage of approximately 2100 genes, has had success in ascertaining independently acting variants and helped us to move a step closer to identify functional variants. Yet irrespective of the platform used, for each trait, the percentage variance explained by these genes remains in the order of approximately 10-12% and it is still unclear to what extent rare mutations, identified by resequencing or exome sequencing approaches, or gene × gene and gene × environment studies, might boost this.

SUMMARY

GWAS-identified novel lipid-related loci highlight new pathways in lipid metabolism and may provide innovative drug targets. Furthermore, the use of lipid gene scores, over and above a single lipid measure, may have clinical utility.