Polymorphic variants in tenascin-C (TNC) are associated with atherosclerosis and coronary artery disease

Tenascin-C (TNC) is an extracellular matrix protein implicated in biological processes important for atherosclerotic plaque development and progression, including smooth muscle cell migration and proliferation. Previously, we observed differential expression of TNC in atherosclerotic aortas compared with healthy aortas. The goal of this study was to investigate whether common genetic variation within TNC is associated with risk of atherosclerosis and coronary artery disease (CAD) in three independent datasets. We genotyped 35 single nucleotide polymorphisms (SNPs), including 21 haplotype tagging SNPs, in two of these datasets: human aorta tissue samples (n = 205) and the CATHGEN cardiovascular study (n = 1,325). Eleven of these 35 SNPs were then genotyped in a third dataset, the GENECARD family study of early-onset CAD (n = 879 families). Three SNPs representing a block of linkage disequilibrium, rs3789875, rs12347433, and rs4552883, were significantly associated with atherosclerosis in multiple datasets and demonstrated consistent, but suggestive, genetic effects in all analyses. In combined analysis rs3789875 and rs12347433 were statistically significant after Bonferroni correction for 35 comparisons, p = 2 × 10(-6) and 5 × 10(-6), respectively. The SNP rs12347433 is a synonymous coding SNP and may be biologically relevant to the mechanism by which tenascin-C influences the pathophysiology of CAD and atherosclerosis. This is the first report of genetic association between polymorphisms in TNC and atherosclerosis or CAD.

A common variant in the CDKN2B gene on chromosome 9p21 protects against coronary artery disease in Americans of African ancestry

Background

A 58kb region on chromosome 9p21.3 has consistently shown strong association with coronary artery disease (CAD) in multiple genome-wide association studies in populations of European and East Asian ancestry. In this study we sought to further characterize the role of genetic variants in 9p21.3 in African American individuals.

Methods and Results

Apparently healthy African American siblings (n=548) of patients with documented CAD <60 years of age were genotyped and followed for incident CAD for up to 17 years. Tests of association for 86 SNPs across the 9p21.3 region in a GEE logistic framework under an additive model adjusting for traditional risk factors, family, follow-up time, and population stratification were performed. A single SNP within the CDKN2B gene met stringent criteria for statistical significance, including permutation-based evaluations. This variant, rs3217989, was common (minor allele [G] frequency 0.242), conveyed protection against CAD (OR=0.19, 95% CI: 0.07 to 0.50, p=0.0008) and was replicated in a combined analysis of two additional case/control studies of prevalent CAD/MI in African Americans (n=990, p=0.024, OR= 0.779, 95% CI: 0.626-0.968).

Conclusions

This is the first report of a CAD association signal in a population of African ancestry with a common variant within the CDKN2B gene, independent from previous findings in European and East Asian ancestry populations. The findings demonstrate a significant protective effect against incident CAD in African American siblings of persons with premature CAD, with replication in a combination of two additional African American cohorts.

Ordered subset analysis for case-control studies

Genetic heterogeneity, which may manifest on a population level as different frequencies of a specific disease susceptibility allele in different subsets of patients, is a common problem for candidate gene and genome-wide association studies of complex human diseases. The ordered subset analysis (OSA) was originally developed as a method to reduce genetic heterogeneity in the context of family-based linkage studies. Here, we have extended a previously proposed method (OSACC) for applying the OSA methodology to case-control datasets. We have evaluated the type I error and power of different OSACC permutation tests with an extensive simulation study. Case-control datasets were generated under two different models by which continuous clinical or environmental covariates may influence the relationship between susceptibility genotypes and disease risk. Our results demonstrate that OSACC is more powerful under some disease models than the commonly used trend test and a previously proposed joint test of main genetic and gene-environment interaction effects. An additional unique benefit of OSACC is its ability to identify a more informative subset of cases that may be subjected to more detailed molecular analysis, such as DNA sequencing of selected genomic regions to detect functional variants in linkage disequilibrium with the associated polymorphism. The OSACC-identified covariate threshold may also improve the power of an additional dataset to replicate previously reported associations that may only be detectable in a fraction of the original and replication datasets. In summary, we have demonstrated that OSACC is a useful method for improving SNP association signals in genetically heterogeneous datasets.

Assessment of LD matrix measures for the analysis of biological pathway association

Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.

Effects of FOXO genotypes on longevity: a biodemographic analysis

Based on data from 760 centenarians and 1060 middle-age controls (all Han Chinese), this article contributes biodemographic insights and syntheses concerning the magnitude of effects of the FOXO genotypes on longevity. We also estimate independent and joint effects of the genotypes of FOXO1A and FOXO3A genes on long-term survival, considering carrying or not-carrying the minor allele of the single-nucleotide polymorphism of another relevant gene. We found substantial gender differences in the independent effects; positive effects of FOXO3A and negative effects of FOXO1A largely compensate each other if one carries both, although FOXO3A has a stronger impact. Ten-year follow-up cohort analysis shows that at very advanced ages 92-110, adjusted for various confounders, positive effects of FOXO3A on survival remain statistically significant, but no significant effects of FOXO1A alone; G × G interactions between FOXO1A-209 and FOXO3A-310 or FOXO3A-292 decrease survival likelihood by 32%-36% (p < .05); G × E interactions between FOXO1A-209 and regular exercise increase survival likelihood by 31%-32% (p < .05).

Reclassification of cardiovascular risk using integrated clinical and molecular biosignatures: Design of and rationale for the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Horizon 1 Cardiovascular Disease Study

BACKGROUND

Clinical predictive models leave gaps in our ability to stratify cardiovascular risk. High-throughput molecular profiling promises to improve risk classification.

METHODS

Horizon 1 of the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Study was conceived to apply emerging molecular techniques to existing data sets to characterize mechanistic diversity underlying complex human diseases, response to therapy, and prognosis. No previous studies have applied multiple, complementary molecular techniques in combination with well-developed clinical risk models to refine cardiovascular risk prediction. The MURDOCK Cardiovascular Disease Study will assess molecular profiles integrated with clinical data in "clinomic" profiles for cardiovascular risk classification.

CONCLUSION

Herein, we describe the design of and rationale for the MURDOCK Cardiovascular Disease Study.

Aging-related atherosclerosis is exacerbated by arterial expression of tumor necrosis factor receptor-1: evidence from mouse models and human association studies

Aging is believed to be among the most important contributors to atherosclerosis, through mechanisms that remain largely obscure. Serum levels of tumor necrosis factor (TNF) rise with aging and have been correlated with the incidence of myocardial infarction. We therefore sought to determine whether genetic variation in the TNF receptor-1 gene (TNFR1) contributes to aging-related atherosclerosis in humans and whether Tnfr1 expression aggravates aging-related atherosclerosis in mice. With 1330 subjects from a coronary angiography database, we performed a case-control association study of coronary artery disease (CAD) with 16 TNFR1 single-nucleotide polymorphisms (SNPs). Two TNFR1 SNPs significantly associated with CAD in subjects >55 years old, and this association was supported by analysis of a set of 759 independent CAD cases. In multiple linear regression analysis, accounting for TNFR1 SNP rs4149573 significantly altered the relationship between aging and CAD index among 1811 subjects from the coronary angiography database. To confirm that TNFR1 contributes to aging-dependent atherosclerosis, we grafted carotid arteries from 18- and 2-month-old wild-type (WT) and Tnfr1(-/-) mice into congenic apolipoprotein E-deficient (Apoe(-/-)) mice and harvested grafts from 1 to 7 weeks post-operatively. Aged WT arteries developed accelerated atherosclerosis associated with enhanced TNFR1 expression, enhanced macrophage recruitment, reduced smooth muscle cell proliferation and collagen content, augmented apoptosis and plaque hemorrhage. In contrast, aged Tnfr1(-/-) arteries developed atherosclerosis that was indistinguishable from that in young Tnfr1(-/-) arteries and significantly less than that observed in aged WT arteries. We conclude that TNFR1 polymorphisms associate with aging-related CAD in humans, and TNFR1 contributes to aging-dependent atherosclerosis in mice.

Genome-wide linkage analysis of quantitative biomarker traits of osteoarthritis in a large, multigenerational extended family

OBJECTIVE

The genetic contributions to the multifactorial disorder osteoarthritis (OA) have been increasingly recognized. The goal of the current study was to use OA-related biomarkers of severity and disease burden as quantitative traits to identify genetic susceptibility loci for OA.

METHODS

In a large multigenerational extended family (n = 350), we measured 5 OA-related biomarkers: hyaluronan (HA), cartilage oligomeric matrix protein (COMP), N-propeptide of type IIA collagen (PIIANP), C-propeptide of type II procollagen (CPII), and type II collagen neoepitope (C2C). Single-nucleotide polymorphism markers (n = 6,090) covering the whole genome were genotyped using the Illumina HumanLinkage-12 BeadChip. Variance components analysis, as implemented in the Sequential Oligogenic Linkage Analysis Routines, was used to estimate heritabilities of the quantitative traits and to calculate 2-point and multipoint logarithm of odds (LOD) scores using a polygenic model.

RESULTS

After adjusting for age and sex, we found that 4 of the 5 biomarkers exhibited significant heritability (PIIANP 0.57, HA 0.49, COMP 0.43, C2C 0.30; P < or = 0.01 for all). Fourteen of the 19 loci that had multipoint LOD scores of >1.5 were near to or overlapped with previously reported OA susceptibility loci. Four of these loci were identified by more than 1 biomarker. The maximum multipoint LOD scores for the heritable quantitative biomarker traits were 4.3 for PIIANP (chromosome 8p23.2), 3.2 for COMP (chromosome 8q11.1), 2.0 for HA (chromosome 6q16.3), and 2.0 for C2C (chromosome 5q31.2).

CONCLUSION

Herein, we report the first evidence of genetic susceptibility loci identified by OA-related biomarkers in an extended family. Our results demonstrate that serum concentrations of PIIANP, HA, COMP, and C2C have substantial heritable components, and using these biomarkers, several genetic loci potentially contributing to the genetic diversity of OA were identified.

Association of a Peripheral Blood Metabolic Profile With Coronary Artery Disease and Risk of Subsequent Cardiovascular Events

Background— Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events.

Methods and Results— We performed mass–spectrometry–based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls (“initial”), and 140 CAD cases and 140 controls (“replication”). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined (“event” group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled (“event-replication” group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis–derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P =0.002, replication P =0.01), and 1 comprising urea cycle metabolites (factor 9, initial P =0.0004, replication P =0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P =0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P =0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P =0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P =0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P =0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P =0.01).

Conclusions— Metabolite profiles are associated with CAD and subsequent cardiovascular events.

Validation study of genetic associations with coronary artery disease on chromosome 3q13-21 and potential effect modification by smoking

The CATHGEN study reported associations of chromosome 3q13-21 genes (KALRN, MYLK, CDGAP, and GATA2) with early-onset coronary artery disease (CAD). This study attempted to independently validate those associations. Eleven single nucleotide polymorphisms (SNPs) were examined (rs10934490, rs16834817, rs6810298, rs9289231, rs12637456, rs1444768, rs1444754, rs4234218, rs2335052, rs3803, rs2713604) in patients (N = 1618) from the Intermountain Heart Collaborative Study (IHCS). Given the higher smoking prevalence in CATHGEN than IHCS (41% vs. 11% in controls, 74% vs. 29% in cases), smoking stratification and genotype-smoking interactions were evaluated. Suggestive association was found for GATA2 (rs2713604, p = 0.057, OR = 1.2). Among smokers, associations were found in CDGAP (rs10934490, p = 0.019, OR = 1.6) and KALRN (rs12637456, p = 0.011, OR = 2.0) and suggestive association was found in MYLK (rs16834871, p = 0.051, OR = 1.8, adjusting for gender). No SNP association was found among non-smokers, but smoking/SNP interactions were detected for CDGAP (rs10934491, p = 0.017) and KALRN (rs12637456, p = 0.010). Similar differences in SNP effects by smoking status were observed on re-analysis of CATHGEN. CAD associations were suggestive for GATA2 and among smokers significant post hoc associations were found in KALRN, MYLK, and CDGAP. Genetic risk conferred by some of these genes may be modified by smoking. Future CAD association studies of these and other genes should evaluate effect modification by smoking.

A general integrative genomic feature transcription factor binding site prediction method applied to analysis of USF1 binding in cardiovascular disease

Transcription factors are key mediators of human complex disease processes. Identifying the target genes of transcription factors will increase our understanding of the biological network leading to disease risk. The prediction of transcription factor binding sites (TFBSs) is one method to identify these target genes; however, current prediction methods need improvement. We chose the transcription factor upstream stimulatory factor l (USF1) to evaluate the performance of our novel TFBS prediction method because of its known genetic association with coronary artery disease (CAD) and the recent availability of USF1 chromatin immunoprecipitation microarray (ChIP-chip) results. The specific goals of our study were to develop a novel and accurate genome-scale method for predicting USF1 binding sites and associated target genes to aid in the study of CAD. Previously published USF1 ChIP-chip data for 1 per cent of the genome were used to develop and evaluate several kernel logistic regression prediction models. A combination of genomic features (phylogenetic conservation, regulatory potential, presence of a CpG island and DNaseI hypersensitivity), as well as position weight matrix (PWM) scores, were used as variables for these models. Our most accurate predictor achieved an area under the receiver operator characteristic curve of 0.827 during cross-validation experiments, significantly outperforming standard PWM-based prediction methods. When applied to the whole human genome, we predicted 24,010 USF1 binding sites within 5 kilobases upstream of the transcription start site of 9,721 genes. These predictions included 16 of 20 genes with strong evidence of USF1 regulation. Finally, in the spirit of genomic convergence, we integrated independent experimental CAD data with these USF1 binding site prediction results to develop a prioritised set of candidate genes for future CAD studies. We have shown that our novel prediction method, which employs genomic features related to the presence of regulatory elements, enables more accurate and efficient prediction of USF1 binding sites. This method can be extended to other transcription factors identified in human disease studies to help further our understanding of the biology of complex disease.

High heritability of metabolomic profiles in families burdened with premature cardiovascular disease

Integration of genetic and metabolic profiling holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolomic profiles has not been evaluated in humans. We performed quantitative mass spectrometry-based metabolic profiling in 117 individuals within eight multiplex families from the GENECARD study of premature CAD. Heritabilities were calculated using variance components. We found high heritabilities for amino acids (arginine, ornithine, alanine, proline, leucine/isoleucine, valine, glutamate/glutamine, phenylalanine and glycine; h(2)=0.33-0.80, P=0.005-1.9 x 10(-16)), free fatty acids (arachidonic, palmitic, linoleic; h(2)=0.48-0.59, P=0.002-0.00005) and acylcarnitines (h(2)=0.23-0.79, P=0.05-0.0000002). Principal components analysis was used to identify metabolite clusters. Reflecting individual metabolites, several components were heritable, including components comprised of ketones, beta-hydroxybutyrate and C2-acylcarnitine (h(2)=0.61); short- and medium-chain acylcarnitines (h(2)=0.39); amino acids (h(2)=0.44); long-chain acylcarnitines (h(2)=0.39) and branched-chain amino acids (h(2)=0.27). We report a novel finding of high heritabilities of metabolites in premature CAD, establishing a possible genetic basis for these profiles. These results have implications for understanding CAD pathophysiology and genetics.

Ordered-subset analysis (OSA) for family-based association mapping of complex traits

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.

Polymorphisms of the tumor suppressor gene LSAMP are associated with left main coronary artery disease

Previous association mapping on chromosome 3q13-21 detected evidence for association at the limbic system-associated membrane protein (LSAMP) gene in individuals with late-onset coronary artery disease (CAD). LSAMP has never been implicated in the pathogenesis of CAD. We sought to thoroughly characterize the association and the gene. Non-redundant single nucleotide polymorphisms (SNPs) across the gene were examined in an initial dataset (168 cases with late-onset CAD, 149 controls). Stratification analysis on left main CAD (N = 102) revealed stronger association, which was further validated in a validation dataset (141 cases with left main CAD, 215 controls), a third control dataset (N = 255), and a family-based dataset (N = 2954). A haplotype residing in a novel alternative transcript of the LSAMP gene was significant in all independent case-control datasets (p = 0.0001 to 0.0205) and highly significant in the joint analysis (p = 0.00004). Lower expression of the novel alternative transcript was associated with the risk haplotype (p = 0.0002) and atherosclerosis burden in human aortas (p = 0.0001). Furthermore, silencing LSAMP expression in human aortic smooth muscle cells (SMCs) substantially augmented SMC proliferation (p<0.01). Therefore, the risk conferred by the LSAMP haplotype appears to be mediated by LSAMP down-regulation, which may promote SMC proliferation in the arterial wall and progression of atherosclerosis.

Genetic and functional association of FAM5C with myocardial infarction

Background

We previously identified a 40 Mb region of linkage on chromosome 1q in our early onset coronary artery disease (CAD) genome-wide linkage scan (GENECARD) with modest evidence for linkage (n = 420, LOD 0.95). When the data are stratified by acute coronary syndrome (ACS), this modest maximum in the overall group became a well-defined LOD peak (maximum LOD of 2.17, D1S1589/D1S518). This peak overlaps a recently identified inflammatory biomarker (MCP-1) linkage region from the Framingham Heart Study (maximum LOD of 4.27, D1S1589) and a region of linkage to metabolic syndrome from the IRAS study (maximum LOD of 2.59, D1S1589/D1S518). The overlap of genetic screens in independent data sets provides evidence for the existence of a gene or genes for CAD in this region.

Methods

A peak-wide association screen (457 SNPs) was conducted of a region 1 LOD score down from the peak marker (168–198 Mb) in a linkage peak for acute coronary syndrome (ACS) on chromosome 1, within a family-based early onset coronary artery disease (CAD) sample (GENECARD).

Results

Polymorphisms were identified within the 'family with sequence similarity 5, member C' gene (FAM5C) that show genetic linkage to and are associated with myocardial infarction (MI) in GENECARD. The association was confirmed in an independent CAD case-control sample (CATHGEN) and strong association with MI was identified with single nucleotide polymorphisms (SNPs) in the 3' end of FAM5C. FAM5C genotypes were also correlated with expression of the gene in human aorta. Expression levels of FAM5C decreased with increasing passage of proliferating aortic smooth muscle cells (SMC) suggesting a role for this molecule in smooth muscle cell proliferation and senescence.

Conclusion

These data implicate FAM5C alleles in the risk of myocardial infarction and suggest further functional studies of FAM5C are required to identify the gene's contribution to atherosclerosis.

ALOX5AP variants are associated with in-stent restenosis after percutaneous coronary intervention

BACKGROUND

Use of drug-eluting stents (DES) has reduced in-stent restenosis after percutaneous coronary intervention (PCI); however, DES are associated with late stent thrombosis. There is no accurate way to predict in-stent restenosis, although risk factors for atherosclerosis overlap those for in-stent restenosis. Therefore, we evaluated atherosclerosis candidate genes for association with in-stent restenosis.

METHODS

We identified 46 consecutive cases that had undergone PCI with bare-metal stents who subsequently developed symptomatic in-stent restenosis of the target lesion (>/=75% luminal narrowing) within 6 months. Forty-six age-, race-, vessel-diameter- and sex-matched controls without in-stent restenosis after PCI with bare-metal stent were also identified. Single-nucleotide polymorphisms (SNPs, N=82) from 39 candidate atherosclerosis genes were genotyped. Multivariable logistic regression models were used to test for association.

RESULTS

Five SNPs were associated with in-stent restenosis. Three ALOX5AP SNPs were most strongly associated, two with increased risk (OR 3.74, p=0.01; OR 3.46, p=0.02), and the third with decreased risk of in-stent restenosis (OR 0.09, p=0.004). Two ALOX5AP haplotypes were associated with in-stent restenosis (HapB: OR 3.13, p=0.03); and a haplotype similar to HapA: OR 0.14, p=0.0009).

CONCLUSIONS

ALOX5AP, a gene within the inflammatory leukotriene pathway linked to and associated with coronary atherosclerosis, is also associated with in-stent restenosis. Genotyping these variants may help identify those at risk for in-stent restenosis who would benefit most from use of DES.

Comprehensive genetic analysis of the platelet activating factor acetylhydrolase (PLA2G7) gene and cardiovascular disease in case-control and family datasets

Platelet-activating factor acetylhydrolase (PLA2G7) is a potent pro- and anti-inflammatory molecule that has been implicated in multiple inflammatory disease processes, including cardiovascular disease. The goal of this study was to investigate the genetic effects of PLA2G7 on coronary artery disease (CAD) risk in two large, independent datasets with CAD. Using a haplotype tagging (ht) approach, 19 ht single nucleotide polymorphisms (SNPs) were genotyped in CATHGEN case-control samples (cases = 806 and controls = 267) and in the GENECARD Family Study (n = 1101 families, 2954 individuals). Single SNP analysis using logistic regression revealed nine SNPs with significant association in all CATHGEN subjects (P = 0.0004-0.02). CATHGEN cases were further stratified into subgroups based on age of CAD onset (AOO) and severity of disease; 599 young affecteds (YA, AOO <56) and 207 old affected (OA, AOO >56). Significant genetic effects were observed in both OA and YA (P = 0.0001-0.02). The GENECARD probands demonstrated results similar to those seen in the YA CATHGEN cases (P = 0.002-0.05). Of the 19 SNPs genotyped, 3 SNPs result in nonsynonymous coding changes (I198T, A379V and R92H). Two of the coding SNPs, R92H and A379V, constitute two of the most significantly associated SNPs, even after Bonferroni correction and appear to represent independent associations (r(2) = 0.09). Multiple additional polymorphisms in low linkage disequilibrium with these coding SNPs were also strongly associated. In summary, PLA2G7 represents an important, potentially functional candidate in the pathophysiology of CAD based on replicated associations using two independent datasets and multiple statistical approaches. Further functional studies involving a combination of risk alleles are warranted.

Visualizing genotype x phenotype relationships in the GAW15 simulated data

We have developed a graphical display tool called SIMLAPLOT for visualizing different ways in which continuous covariates may influence the genotype-specific risk for complex human diseases. The purpose of our study was to examine continuous covariates in the Genetic Analysis Workshop 15 simulated data set using our novel graphical display tool, with knowledge of the answers. The generated plots provide information about genetic models for the simulated continuous covariates and may help identify the single-nucleotide polymorphisms associated with the underlying quantitative trait loci.

Comparison of GIST and LAMP on the GAW15 simulated data

After genetic linkage has been identified for a complex disease, the next step is often fine-mapping by association analysis, using single-nucleotide polymorphisms (SNPs) within a linkage region. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained in part or in full by the candidate SNP. The genotype identity-by-descent sharing test (GIST) and linkage and association modeling in pedigrees (LAMP) are two methods that were specifically proposed to address this question. GIST determines whether there is significant correlation between family-specific weights, defined by the presence of a tentatively associated allele in affected siblings, and family-specific nonparametric linkage scores. LAMP constructs a pedigree likelihood function of the marker data conditional on the trait data, and implements three likelihood ratio tests to characterize the relationship between the candidate SNP and the disease locus. The goal of our study was to compare the two approaches and evaluate their ability to identify disease-associated SNPs in the Genetic Analysis Workshop 15 (GAW15) simulated data. Our results can be summarized as follows: 1) GIST is simple and fast but, as a test of association, did not perform well in the GAW15 data, especially with adjustment for multiple testing; 2) as a test of association, the LAMP-LE test performs best when the linkage evidence is strong, or when there is at least moderate linkage disequilibrium between the candidate SNP and the trait locus. We conclude that LAMP is more flexible and reliable to use in practice.

Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies

The incorporation of disease-associated covariates into studies aiming to identify susceptibility genes for complex human traits is a challenging problem. Accounting for such covariates in genetic linkage and association analyses may help reduce the genetic heterogeneity inherent in these complex phenotypes. For Genetic Analysis Workshop 15 (GAW15) Problem 3 simulated data, our goal was to compare the power of several two-stage study designs to identify rheumatoid arthritis-related genes on chromosome 9 (disease severity), 11 (IgM), and 18 (anti-cyclic citrinullated protein), with knowledge of the answers. Five study designs incorporating an initial linkage step, followed by a case-selection scheme and case-control association analysis by logistic regression, were considered. The linkage step was either qualitative-trait linkage analysis as implemented in MERLIN-nonparametric linkage (NPL), or quantitative-trait locus analysis as implemented in MERLIN-REGRESS. A set of cases representing either one case from each available family, one case per linked family (NPL >/= 0), or one case from each family identified by ordered-subset analysis was chosen for comparison with the full set of 2000 simulated controls. As expected, the performance of these study designs depended on the disease model used to generate the data, especially the simulated allele frequency difference between cases and controls. The quantitative trait loci analysis performed well in identifying these loci, and the power to identify disease-associated alleles was increased by using ordered-subset analysis as a case selection tool.

Increased efficiency of case-control association analysis by using allele-sharing and covariate information

OBJECTIVE

We compared the efficiency of case selection strategies for following up a genome-wide linkage screen of multiplex families. We simulated datasets under three models by which continuous environmental or clinical covariates may contribute to disease risk or linkage heterogeneity: (i) a quantitative trait locus (QTL) underlying a continuous disease risk factor, (ii) a gene-environment interaction model, (iii) a heterogeneity model defined by distinct covariate distributions in linked and unlinked families.

METHODS

Marker genotypes and covariate values were generated for affected sibling pair (ASP) families, according to the three models above. We evaluated two case selection strategies relative to a reference design, which compared all family probands to a sample of unrelated controls ('all'). The first strategy ignored covariates and selected probands from families with NPL scores > or =0 ('linked best'). The second strategy selected probands from families identified by an ordered subset analysis (OSA), which utilizes family-specific linkage and covariate information.

RESULTS

The 'linked best' design provided power very similar to the 'all' design under all three models. Under some QTL and heterogeneity models, the OSA design was both most powerful and most efficient.

CONCLUSIONS

Incorporating allele sharing and covariate information from ASP families into a case-control study design can increase power and reduce genotyping cost.

Interpreting analyses of continuous covariates in affected sibling pair linkage studies

Datasets collected for linkage analyses of complex human diseases often include a number of clinical or environmental covariates. In this study, we evaluated the performance of three linkage analysis methods when the relationship between continuous covariates and disease risk or linkage heterogeneity was modeled in three different ways: (1) The covariate distribution is determined by a quantitative trait locus (QTL), which contributes indirectly to the disease risk; (2) the covariate is not genetically determined, but influences the disease risk through statistical interaction with a disease susceptibility locus; (3) the covariate distribution differs in families linked or unlinked to a particular disease susceptibility locus. We analyzed simulated datasets with a regression-based QTL analysis, a nonparametric analysis of the binary affection status, and the ordered subset analysis (OSA). We found that a significant OSA result may be due to a gene that influences variability in the population distribution of a continuous disease risk factor. Conversely, a regression-based QTL analysis may detect the presence of gene-environment (GxE) interaction in a sample of primarily affected individuals. The contribution of unaffected siblings and the size of baseline lod scores may help distinguish between QTL and GxE models. As illustrated by a linkage study of multiplex families with age-related macular degeneration, our findings assist in the interpretation of analysis results in real datasets. They suggest that the side-by-side evaluation of OSA and QTL results may provide important information about the relationship of measured covariates with either disease risk or linkage heterogeneity.

Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease

A susceptibility locus for coronary artery disease (CAD) has been mapped to chromosome 3q13-21 in a linkage study of early-onset CAD. We completed an association-mapping study across the 1-LOD-unit-down supporting interval, using two independent white case-control data sets (CATHGEN, initial and validation) to evaluate association under the peak. Single-nucleotide polymorphisms (SNPs) evenly spaced at 100-kb intervals were screened in the initial data set (N=468). Promising SNPs (P<.1) were then examined in the validation data set (N=514). Significant findings (P<.05) in the combined initial and validation data sets were further evaluated in multiple independent data sets, including a family-based data set (N=2,954), an African American case-control data set (N=190), and an additional white control data set (N=255). The association between genotype and aortic atherosclerosis was examined in 145 human aortas. The peakwide survey found evidence of association in SNPs from multiple genes. The strongest associations were found in three SNPs from the kalirin (KALRN) gene, especially in patients with early-onset CAD (P=.00001-00028 in the combined CATHGEN data sets). In-depth investigation of the gene found that an intronic SNP, rs9289231, was associated with early-onset CAD in all white data sets examined (P<.05). In the joint analysis of all white early-onset CAD cases (N=332) and controls (N=546), rs9289231 was highly significant (P=.00008), with an odds-ratio estimate of 2.1. Furthermore, the risk allele of this SNP was associated with atherosclerosis burden (P=.03) in 145 human aortas. KALRN is a protein with many functions, including the inhibition of inducible nitric oxide synthase and guanine-exchange-factor activity. KALRN and two other associated genes identified in this study (CDGAP and MYLK) belong to the Rho GTPase-signaling pathway. Our data suggest the importance of the KALRN gene and the Rho GTPase-signaling pathway in the pathogenesis of CAD.