Identification of chromosomal regions linked to premature myocardial infarction: a meta-analysis of whole-genome searches

Identification of Chromosomal Regions Linked to Autism-Spectrum Disorders: A Meta-Analysis of Genome-Wide Linkage Scans

Background: Autism spectrum disorder (ASD) is a clinically and genetically heterogeneous group of pervasive neurodevelopmental disorders with a strong hereditary component. Although, genome-wide linkage scans (GWLS) and association studies (GWAS) have previously identified hundreds of ASD risk gene loci, the results remain inconclusive. Method: We performed a heterogeneity-based genome search meta-analysis (HEGESMA) of 15 genome scans of autism and ASD. Results: For strictly defined autism, data were analyzed across six separate genome scans. Region 7q22-q34 reached statistical significance in both weighted and unweighted analyses, with evidence of significantly low between-scan heterogeneity. For ASDs (data from 12 separate scans), chromosomal regions 5p15.33-5p15.1 and 15q22.32-15q26.1 reached significance in both weighted and unweighted analyses but did not reach significance for either low or high heterogeneity. Region 1q23.2-1q31.1 was significant in unweighted analyses with low between-scan heterogeneity. Finally, region 8p21.1-8q13.2 reached significant linkage peak in all our meta-analyses. When we combined all available genome scans (15), the same results were produced. Conclusions: This meta-analysis suggests that these regions should be further investigated for autism susceptibility genes, with the caveat that autism spectrum disorders have different linkage signals across genome scans, possibly because of the high genetic heterogeneity of the disease.

Clinical Profile and Long-Term Prognostic Factors of a Young Chinese Han Population (≤ 40 Years) Having ST-Segment Elevation Myocardial Infarction

BACKGROUND

The proportion of the mainland Chinese population with premature ST-segment elevation myocardial infarction is significantly elevated. Young patients with ST-segment elevation myocardial infarction have a different risk factor profile and clinical outcome compared with elder patients, and may also differ as compared to young patients in Western populations.

METHODS

We analyzed a total of 9462 consecutive ST-segment elevation myocardial infarction patients, and recruited 341 consecutive cases who had survived their first ST-segment elevation myocardial infarction at the age less than 40 years, and followed-up these patients for 5 years.

RESULTS

The most prevalent risk factor in young Chinese ST-segment elevation myocardial infarction patients was smoking (307/341, 90.03%) and male gender (328/341, 96.19%), although young patients had fewer traditional risk factors of acute myocardial infarction than the control group [(1.63 ± 1.03) vs. (2.38 ± 1.15), p < 0.01]. The number of affected vessels in cases was significantly less than in the elder control group (p < 0.01). During the follow-up, blood lipids and blood pressure of most patients reached the target level, while 42.10% of patients reported continuation of smoking. Multivariable data analysis showed that persistence of smoking (OR: 3.784, 95% CI: 1.636-8.751, p < 0.01) was the most significant prognostic factor of cardiac events after adjusting for various confounding factors.

CONCLUSIONS

We demonstrated that cigarette smoking is the most prevalent factor among the avoidable cardiovascular risk factors for young ST-segment elevation myocardial infarctions in China. Accordingly, continued smoking is the most powerful predictor for the recurrence of cardiac events in young Chinese patients with ST-segment elevation myocardial infarction.

KEY WORDS

Premature myocardial infarction; Prognosis; Risk factor.

C771G (His241Gln) polymorphism of MLXIPL gene, TG levels and coronary artery disease: a case control study

Objective:

It is suggested that C771G (His241Gln) polymorphism of MLXIPL gene might be a genetic risk factor for coronary artery disease (CAD); therefore, the aim of the present study was to investigate the association between C771G polymorphism of MLXIPL gene and the pathogenesis of CAD in Iranian patients with coronary artery stenosis and control subjects.

Methods:

Two hundred and five patients with coronary artery stenosis and 195 healthy control subjects were included in this study. MLXIPL genotypes were determined by polymerase chain reaction and restriction fragment length polymorphism (RFLP).

Results:

There was an association between the MLXIPL polymorphism and quantitative lipid traits in patient group. Distribution of the CC genotype of MLXIPL was more frequent in patients, (χ²=5.13; p<0.005) and after adjustment for classical CAD risk factors, the MLXIPL CC genotype was independently associated with CAD (OR=1.98, 95% CI, 1.12-4.11; p=0.02). Distribution of MLXIPL genotypes were significantly different as compared with the severity of stenosis (χ²=6.34; p<0.05).

Conclusion:

These results suggest that C771G polymorphism of MLXIPL gene is associated with stenosis and its severity.

Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family

Given the importance of cardiovascular disease (CVD) to public health and the demonstrated heritability of both disease status and its related risk factors, identifying the genetic variation underlying these susceptibilities is a critical step in understanding the pathogenesis of CVD and informing prevention and treatment strategies. Although one can look for genetic variation underlying susceptibility to CVD per se, it can be difficult to define the disease phenotype for such a qualitative analysis and CVD itself represents a convergence of diverse etiologic pathways. Alternatively, one can study the genetics of intermediate traits that are known risk factors for CVD, which can be measured quantitatively. Using the latter strategy, we have measured 21 cardiovascular-related biomarkers in an extended multigenerational pedigree, the CARRIAGE family (Carolinas Region Interaction of Aging, Genes, and Environment). These biomarkers belong to inflammatory and immune, connective tissue, lipid, and hemostasis pathways. Of these, 18 met our quality control standards. Using the pedigree and biomarker data, we have estimated the broad sense heritability (H2) of each biomarker (ranging from 0.09–0.56). A genome-wide panel of 6,015 SNPs was used subsequently to map these biomarkers as quantitative traits. Four showed noteworthy evidence for linkage in multipoint analysis (LOD score ≥ 2.6): paraoxonase (chromosome 8p11, 21), the chemokine RANTES (22q13.33), matrix metalloproteinase 3 (MMP3, 17p13.3), and granulocyte colony stimulating factor (GCSF, 8q22.1). Identifying the causal variation underlying each linkage score will help to unravel the genetic architecture of these quantitative traits and, by extension, the genetic architecture of cardiovascular risk.

Insights into the genetic architecture of diabetic nephropathy

Diabetic nephropathy (DN) is a devastating complication of type 1 and type 2 diabetes and leads to increased morbidity and premature mortality. Susceptibility to DN has an inherent genetic basis as evidenced by familial aggregation and ethnic-specific prevalence rates. Progress in identifying the underlying genetic architecture has been arduous with the realization that a single locus of large effect does not exist, unlike in predisposition to non-diabetic nephropathy in individuals with African ancestry. Numerous risk variants have been identified, each with a nominal effect, and they collectively contribute to disease. These results have identified loci targeting novel pathways for disease susceptibility. With continued technological advances and development of new analytic methods, additional genetic variants and mechanisms (e.g., epigenetic variation) will be identified and help to elucidate the pathogenesis of DN. These advances will lead to early detection and development of novel therapeutic strategies to decrease the incidence of disease.

Association of angiotensin-converting enzyme I/D polymorphism with heart failure: a meta-analysis

Heart failure (HF) is a complex clinical syndrome and is thought to have a genetic basis. Numerous case-control studies have investigated the association between heart failure and polymorphisms in candidate genes. Most studies focused on the angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphism, however, the results were inconsistent because of small studies and heterogeneous samples. The objective was to assess the association between the ACE I/D polymorphism and HF. We performed a meta-analysis of all case-control studies that evaluated the association between ACE I/D polymorphism and HF in humans. Studies were identified in the PUBMED and EMBASE databases, reviews, and reference lists of relevant articles. Two reviewers independently assessed the studies. Seventeen case-control studies with a total of 5576 participants were included in the meta-analysis, including 2453 cases with HF and 3123 controls. The heterogeneity between studies was significant. No association was found under all the four genetic models (D vs. I, DD vs. ID and II, DD and ID vs. II, DD vs. ID). Subgroup analyses for ischemic HF (IHF) and HF because of dilated cardiomyopathy (DHF) also showed no significant association between ACE I/D polymorphism and HF. No significant association between the ACE I/D polymorphism and risk of HF was found in this meta-analysis. The future studies should focus on large-scale prospective and case-control studies which designed to investigate gene-gene and gene-environment interactions to shed light on the genetics of HF.

Meta-analysis of genome-wide linkage scans for renal function traits

BACKGROUND

Several genome scans have explored the linkage of chronic kidney disease phenotypes to chromosomic regions with disparate results. Genome scan meta-analysis (GSMA) is a quantitative method to synthesize linkage results from independent studies and assess their concordance.

METHODS

We searched PubMed to identify genome linkage analyses of renal function traits in humans, such as estimated glomerular filtration rate (GFR), albuminuria, serum creatinine concentration and creatinine clearance. We contacted authors for numerical data and extracted information from individual studies. We applied the GSMA nonparametric approach to combine results across 14 linkage studies for GFR, 11 linkage studies for albumin creatinine ratio, 11 linkage studies for serum creatinine and 4 linkage studies for creatinine clearance.

RESULTS

No chromosomal region reached genome-wide statistical significance in the main analysis which included all scans under each phenotype; however, regions on Chromosomes 7, 10 and 16 reached suggestive significance for linkage to two or more phenotypes. Subgroup analyses by disease status or ethnicity did not yield additional information.

CONCLUSIONS

While heterogeneity across populations, methodologies and study designs likely explain this lack of agreement, it is possible that linkage scan methodologies lack the resolution for investigating complex traits. Combining family-based linkage studies with genome-wide association studies may be a powerful approach to detect private mutations contributing to complex renal phenotypes.

Proteasome modulator 9 and macrovascular pathology of T2D

Aims

Coronary artery disease (CAD) and stroke share a major linkage at the chromosome 12q24 locus. The same chromosome region entails at least a major risk gene for type 2 diabetes (T2D) within NIDDM2, the non-insulin-dependent-diabetes 2 locus. The gene of Proteasome Modulator 9 (PSMD9) lies in the NIDDM2 region and is implicated in diabetes in mice. PSMD9 mutations rarely cause T2D and common variants are linked to both late-onset T2D and maturity-onset-diabetes of the young (MODY3). In this study, we aimed at determining whether PSMD9 is linked to macrovascular pathology of T2D.

Methods and Results

In our 200 T2D families from Italy, we characterized the clinical phenotype of macrovascular pathology by defining the subjects for presence or absence of CAD, stroke and/or transitory ischemic attacks (TIA), plaques of the large arterial vessels (macro-vasculopathy) and arterial angioplasty performance. We then screened 200 T2D siblings/families for PSMD9 +nt460A/G, +nt437C/T and exon E197G A/G single nucleotide polymorphisms (SNPs) and performed a non-parametric linkage study to test for linkage for coronary artery disease, stroke/TIA, macro-vasculopathy and macrovascular pathology of T2D. We performed 1,000 replicates to test the power of our significant results. Our results show a consistent significant LOD score in linkage with all the above-mentioned phenotypes. Our 1000 simulation analyses, performed for each single test, confirm that the results are not due to random chance.

Conclusions

In summary, the PSMD9 IVS3+nt460A/G, +nt437C/T and exon E197G A/G SNPs are linked to CAD, stroke/TIA and macrovascular pathology of T2D in Italians.

Heterogeneity of the phenotypic definition of coronary artery disease and its impact on genetic association studies

BACKGROUND

Variability in phenotypic characterization of coronary artery disease (CAD) may contribute to the heterogeneity of genetic association studies, and more consistency in phenotype definitions might improve replication of genetic associations. We assessed the extent of phenotypic heterogeneity and quantified its impact in a large literature sample of association studies.

METHODS AND RESULTS

We searched for large (≥15 studies) meta-analyses of genetic associations and reviewed all studies included therein. From each primary study, we extracted phenotypic definitions, demographics, study design characteristics, and genotypic data. For each association, we assessed the magnitude and heterogeneity of genetic effects within and across CAD phenotypes, using meta-analytic methodologies. A total of 965 individual studies investigating 32 distinct variants in 22 genes were included, from which we grouped CAD phenotypes into 3 categories: acute coronary syndromes (ACS) (426 [44%] studies); angiographically documented disease (323 [34%] studies); and broad, not otherwise specified CAD (216 [22%] studies). These clinical phenotypes were overlapping. Subgroup meta-analyses by phenotype showed discordant results, but phenotypic classification generally explained small proportions of between-study heterogeneity. Differences between phenotypic groups were minimized for associations with robust statistical support. No CAD phenotype was consistently associated with larger or more homogeneous genetic effects in meta-analyses.

CONCLUSIONS

Substantial phenotypic heterogeneity exists in CAD genetic associations, but differences in phenotype definition make a small contribution to between-study heterogeneity. We did not find a consistent effect in terms of the magnitude or homogeneity of summary effects for a specific phenotype to support its preferential use in genetic studies or meta-analyses for CAD.

Methods for combining multiple genome-wide linkage studies

Cardiovascular disease, metabolic syndrome, schizophrenia, diabetes, bipolar disorder, and autism are a few of the numerous complex diseases for which researchers are trying to decipher the genetic composition. One interest of geneticists is to determine the quantitative trait loci (QTLs) that underlie the genetic portion of these diseases and their risk factors. The difficulty for researchers is that the QTLs underlying these diseases are likely to have small to medium effects which will necessitate having large studies in order to have adequate power. Combining information across multiple studies provides a way for researchers to potentially increase power while making the most of existing studies.Here, we will explore some of the methods that are currently being used by geneticists to combine information across multiple genome-wide linkage studies. There are two main types of meta-analyses: (1) those that yield a measure of significance, such as Fisher's p-value method along with its extensions/modifications and the genome search meta-analysis (GSMA) method, and (2) those that yield a measure of a common effect size and the corresponding standard error, such as model-based methods and Bayesian methods. Some of these methods allow for the assessment of heterogeneity. This chapter will conclude with a recommendation for usage.

Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits

Background

The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).

Results

The KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.

Conclusion

The KWII and PAI are promising metrics for analyzing the GEI of QT.

ACE (I/D) polymorphism and response to treatment in coronary artery disease: a comprehensive database and meta-analysis involving study quality evaluation

BACKGROUND

The role of angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism in modifying the response to treatment modalities in coronary artery disease is controversial.

METHODS

PubMed was searched and a database of 58 studies with detailed information regarding ACE I/D polymorphism and response to treatment in coronary artery disease was created. Eligible studies were synthesized using meta-analysis methods, including cumulative meta-analysis. Heterogeneity and study quality issues were explored.

RESULTS

Forty studies involved invasive treatments (coronary angioplasty or coronary artery by-pass grafting) and 18 used conservative treatment options (including anti-hypertensive drugs, lipid lowering therapy and cardiac rehabilitation procedures). Clinical outcomes were investigated by 11 studies, while 47 studies focused on surrogate endpoints. The most studied outcome was the restenosis following coronary angioplasty (34 studies). Heterogeneity among studies (p < 0.01) was revealed and the risk of restenosis following balloon angioplasty was significant under an additive model: the random effects odds ratio was 1.42 (95% confidence interval:1.07-1.91). Cumulative meta-analysis showed a trend of association as information accumulates. The results were affected by population origin and study quality criteria. The meta-analyses for the risk of restenosis following stent angioplasty or after angioplasty and treatment with angiotensin-converting enzyme inhibitors produced non-significant results. The allele contrast random effects odds ratios with the 95% confidence intervals were 1.04(0.92-1.16) and 1.10(0.81-1.48), respectively. Regarding the effect of ACE I/D polymorphism on the response to treatment for the rest outcomes (coronary events, endothelial dysfunction, left ventricular remodeling, progression/regression of atherosclerosis), individual studies showed significance; however, results were discrepant and inconsistent.

CONCLUSION

In view of available evidence, genetic testing of ACE I/D polymorphism prior to clinical decision making is not currently justified. The relation between ACE genetic variation and response to treatment in CAD remains an unresolved issue. The results of long-term and properly designed prospective studies hold the promise for pharmacogenetically tailored therapy in CAD.

Mapping a gene for rheumatoid arthritis on chromosome 18q21

Although single chi-square analysis of the North American Rheumatoid Arthritis Consortium (NARAC) data identifies many single-nucleotide polymorphisms (SNPs) with p-values less than 0.05, none remain significant after Bonferroni correction. In contrast, CHROMSCAN evades heavy Bonferroni correction and auto-correlation between SNPs by using composite likelihood to model association across all markers in a region and permutation to assess significance. Analysis by CHROMSCAN identifies a 36-kb interval that includes the most significant SNP (msSNP) observed in a 10-Mb target suggested by linkage. Unexpectedly, stratification by gender and age of onset shows that association evidence comes almost entirely from females with age of onset less than 40. Combining evidence from a meta-analysis of linkage studies and three subsets of the NARAC data provides significant evidence for a determinant of rheumatoid arthritis in a 36-kb interval and illustrates the principle that estimates of location and its information are more powerful than estimates of p-values alone.

Genetic and genomic insights into the molecular basis of atherosclerosis

Atherosclerosis is a complex disease involving genetic and environmental risk factors, acting on their own or in synergy. Within the general population, polymorphisms within genes in lipid metabolism, inflammation, and thrombogenesis are probably responsible for the wide range of susceptibility to myocardial infarction, a fatal consequence of atherosclerosis. Genetic linkage studies have been carried out in both humans and mouse models to identify these polymorphisms. Approximately 40 quantitative trait loci for atherosclerotic disease have been found in humans, and approximately 30 in mice. Recently, genome-wide association studies have been used to identify atherosclerosis-susceptibility polymorphisms. Although discovering new atherosclerosis genes through these approaches remains challenging, the pace at which these polymorphisms are being found is accelerating due to rapidly improving bioinformatics resources and biotechnologies. The outcome of these efforts will not only unveil the molecular basis of atherosclerosis but also facilitate the discovery of drug targets and individualized medication against the disease.

Genome-wide scans meta-analysis for pulse pressure

Genome scans for identifying susceptibility loci for pulse pressure have produced inconclusive results. A heterogeneity-based genome search meta-analysis was applied to available genome-scan data on pulse pressure. A genome search meta-analysis divides the whole genome into 120 bins and identifies bins that rank high on average in terms of linkage statistics across genome scans unweighted or weighted by study size. The significance of each bin's average rank (right-sided test) and heterogeneity among studies (left-sided test) was calculated using a Monte Carlo test. The meta-analysis involved 7 genome scans, 3 consisting of subjects of European descent. Of the 120 bins, 5 bins had significant average rank (P(rank)<or=0.05) by either unweighted or weighted analyses, 4 of which (bins 21.2: 21q22.11 to 21q22.3, 18.3: 18q12.2 to 18q21.33, 18.4: 18q21.33 to 18q23, and 6.2: 6p22.3 to 6p21.1) were significant by both. In subjects of European descent, 3 bins (22.1: 22q11.1 to 22q12.3, 22.2: 22q12.3 to 22q13.3, 10.4: 10q22.1 to 10q23.32) had P(rank)<or=0.05 with both unweighted and weighted analyses. Bin 10.4 showed low heterogeneity (P(Q)=0.04). None of the bins showed low heterogeneity (P(Q)>0.05), indicating variation in the strength of association. Further investigation of these regions may help to direct the identification of candidate genes for pulse pressure variation.

A common variant on chromosome 9p21 affects the risk of myocardial infarction

The global endemic of cardiovascular diseases calls for improved risk assessment and treatment. Here, we describe an association between myocardial infarction (MI) and a common sequence variant on chromosome 9p21. This study included a total of 4587 cases and 12,767 controls. The identified variant, adjacent to the tumor suppressor genes CDKN2A and CDKN2B, was associated with the disease with high significance. Approximately 21% of individuals in the population are homozygous for this variant, and their estimated risk of suffering myocardial infarction is 1.64 times as great as that of noncarriers. The corresponding risk is 2.02 times as great for early-onset cases. The population attributable risk is 21% for MI in general and 31% for early-onset cases.