Adult height has been hypothesized to be inversely associated with coronary heart disease but studies have produced conflicting results. We sought to examine the relationship between adult height and the prevalence of coronary artery calcium (CAC), a direct measure of subclinical atherosclerosis and surrogate marker of CHD.
Method and Results
We evaluated the relationship between adult height and CAC in 2,703 participants from the NHLBI Family Heart Study who underwent cardiac computed tomography. We used generalized estimating equations to calculate the prevalence odds ratios for the presence of CAC (CAC>0) across sex-specific quartiles of height. The mean age of the sample was 54.8 years and 60.2% were female. There was an inverse association between adult height and CAC. After adjusting for age, race, field center, waist circumference, smoking, alcohol, physical activity, systolic blood pressure, antihypertensive medications, diabetes, diabetic medications, LDL cholesterol, HDL cholesterol, lipid-lowering medications, and income, individuals in the tallest quartile had 30% lower odds of having prevalent CAC. The odds ratios (95% CI) for the presence of CAC across consecutive sex-specific quartiles of height were 1.0 (reference), 1.15 (0.86–1.53), 0.95(0.73–1.22), and 0.70 (0.53–0.93), p for trend <0.01. There was no evidence of effect modification for the relationship between adult height and CAC by age or socioeconomic status.
The results of our study suggest an inverse, independent association between adult height and CAC.
risk factor; imaging; epidemiology
Background & aims
Previous studies have suggested that cocoa products, which are rich sources of flavonoids, may lower blood pressure, serum cholesterol, fasting blood glucose and improve endothelial function. However, it is unclear whether consumption of cocoa products including chocolate influences the risk of metabolic syndrome (MetS).
In a cross-sectional design, we sought to examine the association between chocolate consumption and the prevalence of MetS.
We studied 4098 participants from the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study aged 25–93 years. Chocolate consumption was assessed using a semi-quantitative food-frequency questionnaire. MetS was defined using the NCEP III criteria. Generalized estimating equations were used to estimate prevalence odds ratios of MetS according to frequency of chocolate intake.
Of the 4098 participants (mean age 51.7 y) included in the analyses, 2206 (53.8%) were female. The prevalence of metabolic syndrome in our population was 30.2%. Compared with those who did not consume any chocolate, multivariate adjusted odds ratios (95% CI) for MetS were 1.26 (0.94, 1.69), 1.15 (0.85, 1.55), and 0.99 (0.66, 1.51) among women who reported chocolate consumption of 1–3 times/ month, 1–4 times/week, and 5+ times/week, respectively. Corresponding values for men were: 1.13 (0.82, 1.57), 1.02 (0.74, 1.39), and 1.21 (0.79, 1.85).
These data do not support an association between chocolate intake and the prevalence of MetS in US adult men and women.
Chocolate; Metabolic syndrome; Cardiovascular disease risk
Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15×10−94
Despite the higher prevalence of type 2 diabetes (T2D) in African Americans than in Europeans, recent genome-wide association studies (GWAS) were examined primarily in individuals of European ancestry. In this study, we performed meta-analysis of 17 GWAS in 8,284 cases and 15,543 controls to explore the genetic architecture of T2D in African Americans. Following replication in additional 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry, we identified two novel and three previous reported T2D loci reaching genome-wide significance. We also examined 158 loci previously reported to be associated with T2D or regulating glucose homeostasis. While 56% of these loci were shared between African Americans and the other populations, the strongest associations in African Americans are often found in nearby single nucleotide polymorphisms (SNPs) instead of the original SNPs reported in other populations due to differential genetic architecture across populations. Our results highlight the importance of performing genetic studies in non-European populations to fine map the causal genetic variants.
The plasma levels of high-density lipoprotein cholesterol (HDL) have an inverse relationship to the risks of atherosclerosis and cardiovascular disease (CVD), and have also been associated with longevity. We sought to identify novel loci for HDL that could potentially provide new insights into biological regulation of HDL metabolism in healthy-longevous subjects. We performed a genome-wide association (GWA) scan on HDL using a mixed model approach to account for family structure using kinship coefficients. A total of 4114 subjects of European descent (480 families) were genotyped at ~2.3 million SNPs and ~38 million SNPs were imputed using the 1000 Genome Cosmopolitan reference panel in MACH. We identified novel variants near-NLRP1 (17p13) associated with an increase of HDL levels at genome-wide significant level (p < 5.0E-08). Additionally, several CETP (16q21) and ZNF259-APOA5-A4-C3-A1 (11q23.3) variants associated with HDL were found, replicating those previously reported in the literature. A possible regulatory variant upstream of NLRP1 that is associated with HDL in these elderly Long Life Family Study (LLFS) subjects may also contribute to their longevity and health. Our NLRP1 intergenic SNPs show a potential regulatory function in Encyclopedia of DNA Elements (ENCODE); however, it is not clear whether they regulate NLRP1 or other more remote gene. NLRP1 plays an important role in the induction of apoptosis, and its inflammasome is critical for mediating innate immune responses. Nlrp1a (a mouse ortholog of human NLRP1) interacts with SREBP-1a (17p11) which has a fundamental role in lipid concentration and composition, and is involved in innate immune response in macrophages. The NLRP1 region is conserved in mammals, but also has evolved adaptively showing signals of positive selection in European populations that might confer an advantage. NLRP1 intergenic SNPs have also been associated with immunity/inflammasome disorders which highlights the biological importance of this chromosomal region.
NALP1; lipids; genomewide association study; aging; familial longevity; family-based study
Approaches exploiting extremes of the trait distribution may reveal novel loci for common traits, but it is unknown whether such loci are generalizable to the general population. In a genome-wide search for loci associated with upper vs. lower 5th percentiles of body mass index, height and waist-hip ratio, as well as clinical classes of obesity including up to 263,407 European individuals, we identified four new loci (IGFBP4, H6PD, RSRC1, PPP2R2A) influencing height detected in the tails and seven new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3, ZZZ3) for clinical classes of obesity. Further, we show that there is large overlap in terms of genetic structure and distribution of variants between traits based on extremes and the general population and little etiologic heterogeneity between obesity subgroups.
This article provides a brief overview of the diagnostic criteria and genomic risk factors for the components of metabolic syndrome (MetS).
Contributions of cardiovascular, obesity, and diabetes genomic risk factors to the development of MetS as reported in the literature have been reviewed.
The genomic risk factors for the development of MetS are strongly linked to the genomic risk factors that make up the components of the disease. Many of the cardiovascular and renal genomic risk factors for MetS development are similar to those found in the development of hypertension and dyslipidemia. Obesity may act as a master trigger to turn on the gene expression changes necessary for the other components of the disease. Studies in the genomics of type 2 diabetes show a number of overlapping genes and polymorphisms that influence both the development of diabetes and MetS.
Although health practitioners now have some insights into the genomics of risk factors associated with MetS, the overall understanding of MetS remains inadequate. Clinical applications based on some of the discussed genomic risk factors are being developed but are not yet available for the diagnosis and treatment of MetS.
A broad knowledge of the genomic contributions to disease processes will enable the clinician to better utilize genomics to assess and tailor management of patients.
Genomics; metabolic syndrome; cardiovascular; diabetes; obesity
Sixteen nuclear magnetic resonance (NMR) spectroscopy lipoprotein measurements of more than 1,000 subjects of GOLDN study, at fasting and at 3.5 and 6 h after a postprandial fat (PPL) challenge at visits 2 and 4, before and after a 3 weeks Fenofibrate (FF) treatment, were included in 6 time-independent multivariate factor analyses. Their top 1,541 unique SNPs were assessed for association with GOLDN NMR-particles and classical lipids. Several SNPs with −log10
p > 7.3 and MAF ≥ 0.10, mostly intergenic associated with NMR-single traits near genes FAM84B (8q24.21), CRIPT (2p21), ACOXL (2q13), BCL2L11 (2q13), PCDH10 (4q28.3), NXPH1 (7p22), and SLC24A4 (14q32.12) in association with NMR-LDLs; HOMER1 (5q14.2), KIT (4q11–q12), VSNL1 (2p24.3), QPRT (16p11.2), SYNPR (3p14.2), NXPH1 (7p22), NELL1 (11p15.1), and RUNX3 (1p36) with NMR-HDLs; and DOK5-CBLN4-MC3R (20q13), NELL1 (11p15.1), STXBP6 (14q12), APOB (2p24-p23), GPR133 (12q24.33), FAM84B (8q24.21) and NR5A2 (1q32.1) in association with NMR-VLDLs particles. NMR single traits associations produced 75 % of 114 significant candidates, 7 % belonged to classical lipids and 18 % overlapped, and 16 % matched for time of discovery between NMR- and classical traits. Five proxy genes, (ACOXL, FAM84B, NXPH1, STK40 and VAPA) showed pleiotropic effects. While tagged for significant associations in our study and with some extra evidence from the literature, candidates as CBNL4, FAM84B, NXPH1, SLC24A4 remain unclear for their functional relation to lipid metabolism. Although GOLDN study is one of the largest in studying PPL and FF treatment effects, the relatively small samples (over 700–1,000 subjects) in association tests appeals for a replication of such a study. Thus, further investigation is needed.
Nuclear magnetic resonance particles; Lipoproteins; Fenofibrate; Postprandial challenge; Genome-wide association
Background and Aims
Metabolic syndrome (MetS) is a complex condition characterized by different phenotypes, according to 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 (ATPIII) defined by measured components or by history of treatment, in 1,421 African- American and 1,195 Caucasian non-diabetic HyperGEN participants, without prevalent cardiovascular disease or serum creatinine>2 mg/dL. Of 2,616 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.
There is 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.
Identifying metabolic syndrome (MetS) genes is important for novel drug development and health care. This study extends the findings on human chromosome 3p26-25 for an identified obesity–insulin factor QTL, with an LOD score above 3. A focused association analysis comprising up to 9578 African American and Caucasian subjects from the HyperGEN Network (908 African Americans and 1025 whites), the Family Heart Study (3035 whites in time 1 and 1943 in time 2), and the Framingham Heart Study (1317 in Offspring and 1320 in Generation 3) was performed. The homologous mouse region was explored in an F16 generation of an advanced intercross between the LG/J and SM/J inbred strains, in an experiment where 1002 animals were fed low-fat (247 males; 254 females) or high-fat (253 males; 248 females) diets. Association results in humans indicate pleiotropic effects for SNPs within or surrounding CNTN4 on obesity, lipids and blood pressure traits and for SNPs near IL5RA, TRNT1, CRBN, and LRRN1 on central obesity and blood pressure. Linkage analyses of this region in LG/J × SM/J mice identify a highly significant pleiotropic QTL peak for insulin and glucose levels, as well as response to glucose challenge. The mouse results show that insulin and glucose levels interact with high and low fat diets and differential gene expression was identified for Crbn and Arl8b. In humans, ARL8B resides ~137 kbps away from BHLHE40, expression of which shows up-regulation in response to insulin treatment. This focused human genetic analysis, incorporating mouse research evidenced that 3p26-25 has important genetic contributions to MetS components. Several of the candidate genes have functions in the brain. Their interaction with MetS and the brain warrants further investigation.
Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout.
Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10−8), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
Coronary artery calcification (CAC) detected by computed tomography is a non-invasive measure of coronary atherosclerosis, that underlies most cases of myocardial infarction (MI). We aimed to identify common genetic variants associated with CAC and further investigate their associations with MI.
Methods and Results
Computed tomography was used to assess quantity of CAC. A meta-analysis of genome-wide association studies for CAC was carried out in 9,961 men and women from five independent community-based cohorts, with replication in three additional independent cohorts (n=6,032). We examined the top single nucleotide polymorphisms (SNPs) associated with CAC quantity for association with MI in multiple large genome-wide association studies of MI. Genome-wide significant associations with CAC for SNPs on chromosome 9p21 near CDKN2A and CDKN2B (top SNP: rs1333049, P=7.58×10−19) and 6p24 (top SNP: rs9349379, within the PHACTR1 gene, P=2.65×10−11) replicated for CAC and for MI. Additionally, there is evidence for concordance of SNP associations with both CAC and with MI at a number of other loci, including 3q22 (MRAS gene), 13q34 (COL4A1/COL4A2 genes), and 1p13 (SORT1 gene).
SNPs in the 9p21 and PHACTR1 gene loci were strongly associated with CAC and MI, and there are suggestive associations with both CAC and MI of SNPs in additional loci. Multiple genetic loci are associated with development of both underlying coronary atherosclerosis and clinical events.
cardiac computed tomography; coronary artery calcification; coronary atherosclerosis; genome-wide association studies; myocardial infarction
The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS.
RESEARCH DESIGN AND METHODS
Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected.
Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure.
Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.
Blood pressure (BP), hypertension (HT) and cardiovascular disease (CVD) are common complex phenotypes, which are affected by multiple genetic and environmental factors. This article describes recent genome-wide association studies (GWAS) that have reported causative variants for BP/HT and CVD/heart traits and analyzes the overlapping associated gene polymorphisms. It also examines potential replication of findings from the HyperGEN data on African Americans and whites. Several genes involved in BP/HT regulation also appear to be involved in CVD. A better picture is emerging, with overlapping hot-spot regions and with interconnected pathways between BP/HT and CVD. A systemic approach to full understanding of BP/HT and CVD development and their progression to disease may lead to the identification of gene targets and pathways for the development of novel therapeutic interventions.
Hypertension; Blood pressure; Cardiovascular disease; Single nucleotide polymorphisms; SNPs; Pathways; GWAS; Genome-wide association studies
Because of the low frequency of rare genetic variants in observed data, the statistical power of detecting their associations with target traits is usually low. The collapsing test of collective effect of multiple rare variants is an important and useful strategy to increase the power; in addition, family data may be enriched with causal rare variants and therefore provide extra power. However, when family data are used, both population structure and familial relatedness need to be adjusted for the possible inflation of false positives. Using a unified mixed linear model and family data, we compared six methods to detect the association between multiple rare variants and quantitative traits. Through the analysis of 200 replications of the quantitative trait Q2 from the Genetic Analysis Workshop 17 data set simulated for 697 subjects from 8 extended families, and based on quantile-quantile plots under the null and receiver operating characteristic curves, we compared the false-positive rate and power of these methods. We observed that adjusting for pedigree-based kinship gives the best control for false-positive rate, whereas adjusting for marker-based identity by state slightly outperforms in terms of power. An adjustment based on a principal components analysis slightly improves the false-positive rate and power. Taking into account type-1 error, power, and computational efficiency, we find that adjusting for pedigree-based kinship seems to be a good choice for the collective test of association between multiple rare variants and quantitative traits using family data.
As the cost of sequencing decreases, the demand for association tests that use exhaustive DNA sequence information increases. One such association test is multivariate distance matrix regression (MDMR). We explore some of the features of MDMR using Genetic Analysis Workshop 17 simulated data in search of potential improvements in distance measures. We used genotype data from 697 unrelated individuals, in 200 replications, to test the power of MDMR to detect 13 trait Q2 causative genes based on the Euclidean distance metric. We also estimated the false-positive rate of MDMR using 508 control genes. In addition, we compared MDMR with Mantel’s test and collapsing analysis for rare variants. MDMR performed comparably well even with the Euclidean distance measure.
We present an evaluation of discovery power for two association tests that work well with common alleles but are applied to the Genetic Analysis Workshop 17 simulations with rare causative single-nucleotide polymorphisms (SNPs) (minor allele frequency [MAF] < 1%). The methods used were genome-wide single-SNP association tests based on a linear mixed-effects model for discovery and applied to the familial sample and sliding windows haplotype association tests for replication, implemented within causative genes in the unrelated individuals sample. Both methods are evaluated with respect to the simulated trait Q2. The linear mixed-effects model and haplotype association tests failed to detect the rare alleles of the simulated associations. In contrast, the linear mixed-effects model and haplotype association tests detected effects for the most important simulated SNPs with MAF > 1%. We conclude that these findings reflect inadequate statistical power (the result of small simulated samples) for the complex genetic model that underlies these data.
Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body-mass-index (up to 77,167 participants), following up 16 loci in an additional 29 studies (up to 113,636 subjects). We identified 13 novel loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (P 1.9 × 10−9 to 1.8 × 10−40), and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex-difference 1.9 × 10−3 to 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution, independent of overall adiposity, and reveal powerful gene-by-sex interactions.
genome-wide association; waist-hip-ratio; body fat distribution; central obesity; meta-analysis; genetics; visceral adipose tissue; metabolism; body composition; Expression Quantitative Trait Loci; sex difference
Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10−9 to P = 1.8 × 10−40) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10−3 to P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
Motivation: DNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly used methods for RCNA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden, as well as a loss of the overall statistical power due to segmentation and discretization of individual sample's data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient and particularly suitable for high-resolution and large-population studies.
Results: Our approach, correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using the raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes.
Availability: The R and C programs implementing our method are available at https://dsgweb.wustl.edu/qunyuan/software/cmds.
Supplementary information: Supplementary data are available at Bioinformatics online.
Genetic Analysis Workshop 16 GAW16) was held September 17-20, 2008 in St. Louis, Missouri. The focus of GAW16 was on methods and challenges in analysis of single-nucleotide polymorphism (SNP) data from genome-wide scans. GAW16 attracted 221 participants from 12 countries. The 168 contributions were organized into 17 discussion groups of 6 to 17 papers each. Three data sets were available for analysis. Two of these were data from ongoing studies, generously provided by the investigators. The North American Rheumatoid Arthritis Consortium provided case-control data on rheumatoid arthritis, and the Framingham Heart Study made available information on cardiovascular risk factors for participants in three generations of pedigree data. The third data set included simulated phenotypes for participants in the Framingham Heart Study, using actual pedigree structures and genotypes. This volume includes a paper for each of the 17 discussion groups, summarizing their main findings.
single-nucleotide polymorphism; SNP; genome-wide scan; association; linkage; haplotype
Genome-wide linkage analysis was carried out for systolic and diastolic blood pressures in the Hypertension Genetic Epidemiology Network. We investigated the role of gene-age interactions using a recently developed variance components method that incorporates age variation in genetic effects. Substantially improved linkage evidence, in terms of both the number of linkage peaks and their significance levels, was observed. Twenty-six linkage peaks were identified with maximum LOD scores ranging between 3.0 and 4.6, fifteen of which were cross-validated by the literature. The chromosomal region 1p36 that showed the highest lod score in our study was found being supported by evidences from three literature. The new method also led to vastly improved validation across ethnic groups. Ten out of the fifteen supported linkage peaks were cross validated between two different ethnic groups, and two peaks on chromosomal region 1q31 and 16p11 were validated in three ethnic groups. In conclusion, this investigation demonstrates that genetic effects on blood pressure vary by age. The improved genetic linkage results presented here should help in identifying the specific genetic variants that explain the observed results.
blood pressure; genetics; hypertension; linkage; gene-age interactions; QTL effect
Studies of complex diseases collect panels of disease-related traits, also known as secondary phenotypes or endophenotypes. They reflect intermediate responses to environment exposures, and as such, are likely to contain hidden information of gene-environment (G × E) interactions. The information can be extracted and used in genetic association studies via latent-components analysis. We present such a method that extracts G × E information in longitudinal data of endophenotypes, and apply the method to repeated measures of multiple phenotypes related to coronary heart disease in Genetic Analysis Workshop 16 Problem 2. The new method identified many genes, including SCNN1B (sodium channel nonvoltage-gated 1 beta) and PKP2 (plakophilin 2), with potential time-dependent G × E interactions; and several others including a novel cardiac-specific kinase gene (TNNI3K), with potential G × E interactions independent of time and marginal effects.
The Genetic Analysis Workshop (GAW) 16 Problem 3 comprises simulated phenotypes emulating the lipid domain and its contribution to cardiovascular disease risk. For each replication there were 6,476 subjects in families from the Framingham Heart Study (FHS), with their actual genotypes for Affymetrix 550 k single-nucleotide polymorphisms (SNPs) and simulated phenotypes. Phenotypes are simulated at three visits, 10 years apart. There are up to 6 "major" genes influencing variation in high- and low-density lipoprotein cholesterol (HDL, LDL), and triglycerides (TG), and 1,000 "polygenes" simulated for each trait. Some polygenes have pleiotropic effects. The locus-specific heritabilities of the major genes range from 0.1 to 1.0%, under additive, dominant, or overdominant modes of inheritance. The locus-specific effects of the polygenes ranged from 0.002 to 0.15%, with effect sizes selected from negative exponential distributions. All polygenes act independently and have additive effects. Individuals in the LDL upper tail were designated medicated. Subjects medicated increased across visits at 2%, 5%, and 15%. Coronary artery calcification (CAC) was simulated using age, lipid levels, and CAC-specific polymorphisms. The risk of myocardial infarction before each visit was determined by CAC and its interactions with smoking and two genetic loci. Smoking was simulated to be commensurate with rates reported by the Centers for Disease Control. Two hundred replications were simulated.