Obesity, which is frequently associated with diabetes, hypertension, and cardiovascular diseases, is primarily the result of a net excess of caloric intake over energy expenditure. Human obesity is highly heritable, but the specific genes mediating susceptibility in non-syndromic obesity remain unclear. We tested candidate genes in pathways related to food intake and energy expenditure for association with body mass index (BMI).
We re-analyzed 355 common genetic variants of 30 candidate genes in 7 molecular pathways related to obesity in 1,982 unrelated European Americans from the New York Health Project. Data were analyzed by using a Bayesian hierarchical generalized linear model. The BMIs were log-transformed and then adjusted for covariates including age, age2, gender, and diabetes status. The single nucleotide polymorphisms (SNPs) were modeled as additive effects.
With the stipulated adjustments, nine SNPs in eight genes were significantly associated with BMI: GHRL (rs35683), AGRP (rs5030980), CPE (rs1946816 and rs4481204), GLP1R (rs2268641), HTR2A (rs912127), NPY5R (Y5R1c52), SOCS3 (rs4969170), and STAT3 (rs4796793). We also found a gender-by-SNP interaction (rs1745837 in HTR2A), which indicated that variants in the gene HTR2A had a stronger association with BMI in males. In addition, NPY1R was detected as having a significant gene effect even though none of the SNPs in this gene was significant.
Variations in genes AGRP, CPE, GHRL, GLP1R, HTR2A, NPY1R, NPY5R, SOCS3, and STAT3 showed modest associations with BMI in European Americans. The pathways in which these genes participate regulate energy intake and thus these associations are mechanistically plausible in this context.
Obesity; genetic association; single nucleotide polymorphism (SNP); Bayesian hierarchical generalized linear model (BhGLM)
Like many other ancient genes, the cystic fibrosis transmembrane conductance regulator (CFTR) has survived for hundreds of millions of years. In this report, we consider whether such prodigious longevity of an individual gene – as opposed to an entire genome or species – should be considered surprising in the face of eons of relentless DNA replication errors, mutagenesis, and other causes of sequence polymorphism. The conventions that modern human SNP patterns result either from purifying selection or random (neutral) drift were not well supported, since extant models account rather poorly for the known plasticity and function (or the established SNP distributions) found in a multitude of genes such as CFTR. Instead, our analysis can be taken as a polemic indicating that SNPs in CFTR and many other mammalian genes may have been generated—and continue to accrue—in a fundamentally more organized manner than would otherwise have been expected. The resulting viewpoint contradicts earlier claims of ‘directional’ or ‘intelligent design-type’ SNP formation, and has important implications regarding the pace of DNA adaptation, the genesis of conserved non-coding DNA, and the extent to which eukaryotic SNP formation should be viewed as adaptive.
Previous studies have reported that risk of cardiovascular morbidity and mortality substantially increases in hypertensive patients, especially among those with inadequate blood pressure control. Two common antihypertensive drug classes including thiazide diuretics and angiotensin-converting enzyme (ACE) inhibitors affect different enzymes in the renin-angiotensin-aldosterone system (RAAS). In the RAAS, angiotensinogen is converted into angiotensin II which increases blood pressure through vasoconstriction. Using a case-only design with 3448 high-risk hypertensive individuals from the Genetics of Hypertension Associated Treatment (GenHAT) study, we examined whether seven single nucleotide polymorphisms (SNPs) in the angiotensinogen gene (AGT) interact with three classes of antihypertensive drugs including chlorthalidone (a thiazide diuretic), lisinopril (an ACE inhibitor), and amlodipine (a calcium channel blocker) to modify the risk of incident coronary heart disease (CHD) and heart failure (HF) among Caucasian and African American participants, separately. We found no gene by treatment interactions to be statistically significant after correction for multiple testing. However, some suggestive results were found. African American participants with the minor allele of rs11122576 had over two-fold higher risk of CHD when using chlorthalidone compared to using amlodipine, or lisinopril compared to amlodipine (p = 0.006 and p = 0.01, respectively). Other marginal associations are also reported among both race groups. The findings reported here suggest that rs11122576 could contribute to future personalization of antihypertensive treatment among African Americans though more studies are needed.
AGT gene; antihypertensive drugs; hypertension; coronary heart disease; heart failure
To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup’ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA).
A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ15N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake.
We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits.
We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.
Alaska Native; metabolism; multi-point linkage genome scan
Lipoprotein subclass concentrations are modifiable markers of cardiovascular disease risk. Fenofibrate is known to show beneficial effects on lipoprotein subclasses, but little is known about the role of genetics in mediating the responses of lipoprotein subclasses to fenofibrate. A recent genomewide association study (GWAS) associated several single nucleotide polymorphisms (SNPs) with lipoprotein measures, and validated these associations in two independent populations. We used this information to construct genetic risk scores (GRSs) for fasting lipoprotein measures at baseline (pre-fenofibrate), and aimed to examine whether these GRSs also associated with the responses of lipoproteins to fenofibrate. Fourteen lipoprotein subclass measures were assayed in 817 men and women before and after a three week fenofibrate trial. We set significance at a Bonferroni corrected alpha <0.05 (p < 0.004). Twelve subclass measures changed with fenofibrate administration (each p = 0.003 to <0.0001). Mixed linear models which controlled for age, sex, body mass index (BMI), smoking status, pedigree and study-center, revealed that GRSs were associated with eight baseline lipoprotein measures (p < 0.004), however no GRS was associated with fenofibrate response. These results suggest that the mechanisms for changes in lipoprotein subclass concentrations with fenofibrate treatment are not mediated by the genetic risk for fasting levels.
pharmacogenetics; candidate gene study; lipoprotein; fenofibrate; NMR; GOLDN; genetic risk score; particle size; LDL size; HDL size
For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called ‘rare variants’ (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the ‘missing heritability’ because very few people may carry these rare variants. The genetic variants that are likely to fill in the ‘missing heritability’ include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.
Similarity; Linkage disequilibrium; Rare variants; JAK2 gene; Body-mass index
Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; and while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected.
We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses.
Our algorithm performs imputation from a pool of replacement subjects with similar values of both treatment and covariate(s) of interest, that is, from a risk-stratified sample. This stratification prior to imputation addresses the requirement of time-to-event analysis that censored observations are representative of all other observations in the risk group with similar exposure variables. We compared our risk-stratified imputation to case deletion and bootstrap imputation in a simulated dataset in which the covariate of interest (study withdrawal) was related to treatment. A motivating example from a recent clinical trial is also presented to demonstrate the utility of our method.
In our simulations, risk-stratified imputation gives estimates of treatment effect comparable to bootstrap and auxiliary variable imputation while avoiding inaccuracies of the latter two in estimating the associated variance. Similar results were obtained in analysis of clinical trial data.
Risk-stratified imputation has little advantage over other imputation methods when covariates of interest are not related to treatment, although its performance is superior when covariates are related to treatment. Risk-stratified imputation is intended for categorical covariates, and may be sensitive to the width of the matching window if continuous covariates are used.
The use of the risk-stratified imputation should facilitate the analysis of many clinical trials, in which one group has a higher withdrawal rate that is related to treatment.
Censoring; Survival; Imputation; Randomized Trials; CREST; Time to Event
Increased postprandial lipid (PPL) response to dietary fat intake is a heritable risk factor for cardiovascular disease (CVD). Variability in postprandial lipids results from the complex interplay of dietary and genetic factors. We hypothesized that detailed lipid profiles (eg, sterols and fatty acids) may help elucidate specific genetic and dietary pathways contributing to the PPL response.
Methods and Results
We used gas chromatography mass spectrometry to quantify the change in plasma concentration of 35 fatty acids and 11 sterols between fasting and 3.5 hours after the consumption of a high-fat meal (PPL challenge) among 40 participants from the GOLDN study. Correlations between sterols, fatty acids and clinical measures were calculated. Mixed linear regression was used to evaluate associations between lipidomic profiles and genomic markers including single nucleotide polymorphisms (SNPs) and methylation markers derived from the Affymetrix 6.0 array and the Illumina Methyl450 array, respectively. After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased. PPL saturated fatty acids strongly correlated with triglycerides, very low-density lipoprotein, and chylomicrons. Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10−10). SORBS1 has been linked to obesity and insulin signaling. No other markers reached the genome-wide significance threshold, yet several other biologically relevant loci are highlighted (eg, PRIC285, a co-activator of PPARa).
Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.
Specific constellations of lipoprotein particle features, reflected as differences in mean lipoprotein particle diameters, are associated with risk of insulin resistance (IR) and cardiovascular disease (CVD). The associations of lipid profiles with disease risk differ by race/ethnicity, the reason for this is not clear. We aimed to examine whether there were additional genetic differences between racial / ethnic groups on lipoprotein profile.
Methods and results
Genotypes were assessed using the Affymetrix 6.0 array in 817 related Caucasian participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN). Association analysis was conducted on fasting mean particle diameters using linear models, adjusted for age, sex and study center as fixed effects, and pedigree as a random effect. Replication of associations reaching P<1.97 * 10−05 (the level at which we achieved at least 80% power to replicate SNP-phenotype associations) was conducted in the Caucasian population of the Multi-Ethnic Study of Atherosclerosis (MESA; N=2430). Variants which replicated across both Caucasian populations were subsequently tested for association in the African-American (N=1594), Chinese (N=758) and Hispanic (N=1422) populations of MESA. Variants in the APOB gene region were significantly associated with mean VLDL diameter in GOLDN, and in the Caucasian and Hispanic populations of MESA, while variation in the hepatic lipase (LIPC) gene was associated with mean HDL diameter in both Caucasians populations only.
Our findings suggest the genetic underpinnings of mean lipoprotein diameter differ by race/ethnicity. As lipoprotein diameters are modifiable, this may lead new strategies to modify lipoprotein profiles during the reduction of IR that are sensitive to race / ethnicity.
Lipoprotein size; race / ethnicity; ApoB; Hepatic Lipase; NMR
Familial transmission of stroke and myocardial infarction (MI) is partially mediated by transmission of cerebrovascular and cardiovascular risk factors. We examined relationships between family risk of stroke and MI with risk factors for these phenotypes.
Cross-sectional association between the stratified log-rank family score (SLFS) for stroke and MI with prevalent risk factors was assessed in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort.
Individuals in the 4th quartile of SLFS scores for stroke were more likely to have prevalent risk factors including hypertension (OR: 1.43; 95% CI: [1.30, 1.58]), left ventricular hypertrophy (OR 1.42; 95% CI: [1.16, 1.42]), diabetes (OR: 1.26; 95% CI: [1.12, 1.43]) and atrial fibrillation (OR 1.23; 95% CI: [1.03, 1.45]) compared to individuals in the 1st quartile. Likewise, individuals in the 4th quartile of SLFS scores for MI were more likely to have prevalent risk factors including hypertension (OR 1.57; 95% CI: [1.27, 1.94]) and diabetes (OR 1.29; 95% CI: [1.12, 1.43]) than the 1st quartile. In contrast to stroke, the family risk score for MI was associated with dyslipidemia (OR 1.38; 95% CI: [1.23, 1.55]) and overweight/obesity (OR 1.22; 95% CI: [1.10, 1.37]).
Family risk of stroke and MI are strongly associated with the majority of risk factors associated with each disease. Family history and genetic studies separating nonspecific contributions of intermediate phenotypes from specific contributions to the disease phenotype may lead to more thorough understanding of transmission for these complex disorders.
stroke; myocardial infarction; cohort studies; family risk; REGARDS
A shift towards overall larger very low-density lipoprotein (VLDL), and smaller low-density lipoprotein and high-density lipoprotein (HDL) diameters occurs in insulin resistance (IR), which reflects shifts in the distribution of the subfraction concentrations. Fenofibrate, indicated for hypertriglyceridemia, simultaneously reduces IR and shifts in lipoprotein diameter. Individual responses to fenofibrate vary, and we conducted a genome-wide association study to identify genetic differences that could contribute to such differences.
Association analysis was conducted between single nucleotide polymorphisms (SNPs) on the Affymetrix 6.0 array and fasting particle diameter responses to a 12-week fenofibrate trial, in 817 related Caucasian participants of the Genetics of Lipid Lowering Drugs and Diet Network. Linear models were conducted, which adjusted for age, sex and study center as fixed effects, and pedigree as a random effect. The top three SNPs associated with each fraction were examined subsequently for associations with changes in subfraction concentrations.
SNPs in AHCYL2 and CD36 genes reached, or closely approached, genome-wide levels of significance with VLDL and HDL diameter responses to fenofibrate, respectively (P=4 × 10−9 and 8 × 10−8). SNPs in AHCYL2 were associated with a decrease in the concentration of the large VLDL subfraction only (P = 0.002). SNPs associated with HDL diameter change were not associated with a single subfraction concentration change (P > 0.05) indicating small shifts across all subfractions.
We report novel associations between lipoprotein diameter responses to fenofibrate and the AHCYL2 and CD36 genes. Previous associations of these genes with IR emphasize the role of IR in mediating lipoprotein response to fenofibrate.
AHCYL2; CD36; fenofibrate; inflammation; insulin resistance; insulin signaling; lipoprotein diameter; methylation; PPARγ; subclass
Detecting uncommon causal variants (minor allele frequency (MAF) < 5%) is difficult with commercial single-nucleotide polymorphism (SNP) arrays that are designed to capture common variants (MAF > 5%). Haplotypes can provide insights into underlying linkage disequilibrium (LD) structure and can tag uncommon variants that are not well tagged by common variants. In this work, we propose a wei-SIMc-matching test that inversely weights haplotype similarities with the estimated standard deviation of haplotype counts, to boost the power of similarity-based approaches for detecting uncommon causal variants. We then compare the power of the wei-SIMc-matching test with that of several popular haplotype-based tests, including four other similarity-based tests, a global score test for haplotypes (global), a test based on the maximum score statistic over all haplotypes (max), and two newly proposed haplotype-based tests for rare variant detection. With systematic simulations under a wide range of LD patterns, the results show that wei-SIMc-matching and global are the two most powerful tests. Among these two tests, wei-SIMc-matching has reliable asymptotic P values, whereas global needs permutations to obtain reliable P values when the frequencies of some haplotype categories are low or when the trait is skewed. Therefore, we recommend wei-SIMc-matching for detecting uncommon causal variants with surrounding common SNPs, in light of its power and computational feasibility.
Haplotype; Similarity; Linkage disequilibrium; Rare variants
n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup’ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ15N) of red blood cells, and genotype–phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1–2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup’ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup’ik people may, in part, be dependent upon an individual’s genetic predisposition to obesity.
Electronic supplementary material
The online version of this article (doi:10.1007/s12263-013-0340-z) contains supplementary material, which is available to authorized users.
BMI; Adiposity; Alaska Native; SNP; δ15N; rs9939609; rs7647305; FTO; ETV5; Genetic risk score; CANHR; Gene-by-environment interactions
Genotype imputation provides imputation of untyped SNPs that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their LD blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still underway, we expect that later versions will provide better imputation performance.
Obesity is a highly heritable trait and a growing public health problem. African Americans are a genetically diverse, yet understudied population with a high prevalence of obesity (body mass index (BMI) greater than 30 kg/m2). Recent studies based upon single nucleotide polymorphisms (SNPs) have identified genetic markers associated with obesity. However, a large proportion of the heritability of obesity remains unexplained. Copy number variation (CNV) has been cited as a possible source of missing heritability in common diseases such as obesity. We conducted a CNV genome-wide association study of BMI in two African American cohorts from GENOA and HyperGEN. We performed independent and identical association analyses in each study, then combined the results in a meta-analysis. We identified three CNVs associated with BMI, obesity, and other obesity-related traits after adjusting for multiple testing. These CNVs overlap the PARK2, GYPA and SGCZ genes. Our results suggest that CNV may play a role in the etiology of obesity in African Americans.
Obesity; CNVs; Meta-analysis; BMI; African Americans
Fenofibrate, a peroxisome proliferator-activated receptor-alpha (PPARα) agonist, reduces triglyceride (TG) concentrations by 25–60%. Given significant inter-individual variation in TG response, we investigated the association of PPARA rare variants with treatment response in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study.
We calculated change in TG concentration (ΔTG) among 861 GOLDN participants treated with fenofibrate (160 mg/day) for 3 weeks. From the distribution of ΔTG adjusted for age and sex, the 150 highest and 150 lowest fenofibrate responders were selected from the tails of the distribution for PPARA resequencing. The resequencing strategy was based on VariantSEQrtm technology for the amplification of exons and regulatory regions.
We identified 73 variants with an average minor allele frequency (MAF) of 4.8% (range 0.2%–16%). We tested the association of rare variants located in a coding or regulatory region (MAF<1%, 13 variants) with treatment response group via an indicator variable (presence/absence of ≥1 rare variant) using general linear mixed models to allow for adjustment for family relationship. After adjusting for baseline fasting TG concentration carrying at least one rare variant was associated with low fenofibrate response (odds ratio=6.46; 95% CI 1.4–30.8). Carrier status was also associated with relative change in total cholesterol concentration (P=0.02), but not high density lipoprotein or low density lipoprotein concentration.
Rare, potentially functional variants in PPARA may play a role in TG response to fenofibrate, but future experimental studies will be necessary to replicate the findings and confirm functional effects.
clinical trials; lipids; DNA sequence analysis; PPARalpha
Testing multiple markers simultaneously not only can capture the linkage disequilibrium patterns but also can decrease the number of tests and thus alleviate the multiple-testing penalty. If a gene is associated with a phenotype, subjects with similar genotypes in this gene should also have similar phenotypes. Based on this concept, we have developed a general framework that is applicable to continuous traits. Two similarity-based tests (namely, SIMc and SIMp tests) were derived as special cases of the general framework. In our simulation study, we compared the power of the two tests with that of the single-marker analysis, a standard haplotype regression, and a popular and powerful kernel machine regression. Our SIMc test outperforms other tests when the average r-square (a measure of linkage disequilibrium) between the causal variant and the surrounding markers is larger than 0.3 or when the causal allele is common (say, frequency = 0.3). Our SIMp test outperforms other tests when the causal variant was introduced at common haplotypes (the maximum frequency of risk haplotypes > 0.4). We also applied our two tests to an adiposity data set to show their utility.
Haplotype; Similarity; Genomic distance; Linkage disequilibrium; Multi-marker test; Body-mass index; CPE gene
Despite evidence in support of anti-inflammatory and triglyceride-lowering effects of fenofibrate, little is known about genetic determinants of the observed heterogeneity in treatment response. This study provides the first genome-wide examination of fenofibrate effects on systemic inflammation.
Biomarkers of inflammation were measured in participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n=1092) before and after a 3-week daily treatment with 160 mg of fenofibrate. Two inflammatory patterns (hsCRP-IL6 and MCP1-TNF-α) were derived using principal component analysis. Associations between single nucleotide polymorphisms on the Affymetrix 6.0 chip and phenotypes were assessed using mixed linear models, adjusted for age, sex, study center, and ancestry as fixed effects and pedigree as a random effect.
Before fenofibrate treatment, the strongest evidence for association was observed for polymorphisms near or within the IL2RA gene with the hsCRP-IL6 pattern (rs7911500, P=5×10−9 and rs12722605, P=5×10−8). Associations of the MCP1-TNF-α pattern with loci in several biologically plausible genes (CYP4F8 (rs3764563), APBB1IP (rs1775246), COL13A1 (rs2683572), and COMMD10 (rs1396485)) approached genome-wide significance (P=3×10−7, 5×10−7, 6×10−7, and 7×10−7 respectively) before fenofibrate treatment. After fenofibrate treatment, the rs12722605 locus in IL2RA was also associated with the MCP1-TNF-α pattern (P=3×10−7). The analyses of individual biomarker response to fenofibrate did not yield genome-wide significant results, but the rs6517147 locus near the immunologically relevant IFNAR2 gene was suggestively associated with IL6 (P=7×10−7).
We have identified several novel biologically relevant loci associated with systemic inflammation before and after fenofibrate treatment.
fenofibrate; inflammation; genome-wide association study
Genome-wide association (GWA) studies have become a standard approach for discovering and validating genomic polymorphisms putatively associated with phenotypes of interest. Accounting for population structure in GWA studies is critical to attain unbiased parameter measurements and control Type I error. One common approach to accounting for population structure is to include several principal components derived from the entire autosomal dataset, which reflects population structure signal. However, knowing which components to include is subjective and generally not conclusive. We examined how phylogenetic signal from mitochondrial DNA (mtDNA) and chromosome Y (chr:Y) markers is concordant with principal component data based on autosomal markers to determine whether mtDNA and chr:Y phylogenetic data can help guide principal component selection. Using HAPMAP and other original data from individuals of multiple ancestries, we examined the relationships of mtDNA and chr:Y phylogenetic signal with the autosomal PCA using best subset logistic regression. We show that while the two approaches agree at times, this is independent of the component order and not completely represented in the Eigen values. Additionally, we use simulations to demonstrate that our approach leads to a slightly reduced Type I error rate compared to the standard approach. This approach provides preliminary evidence to support the theoretical concept that mtDNA and chr:Y data can be informative in locating the PCs that are most associated with evolutionary history of populations that are being studied, although the utility of such information will depend on the specific situation.
phylogeny; PCA; Y chromosome; mitochondria; population sub-structure
Genetic studies may help explain abnormalities of fat distribution in HIV-infected patients treated with antiretroviral therapy (ARV).
Subcutaneous adipose tissue (SAT) volume measured by magnetic resonance imaging (MRI) in leg, lower trunk, upper trunk, and arm was examined in 192 HIV-infected Caucasian men, ARV-treated from the Fat Redistribution and Metabolic Change in HIV infection (FRAM) study. Single nucleotide polymorphisms (SNPs) were assayed using the Illumina HumanCNV370-quad beadchip. Multivariate and univariate genome wide association analyses of the four SAT depots were implemented in PLINK software adjusted for age and ARV duration. Functional annotation analysis (FAA) using Ingenuity Systems Pathway Analysis tool (IPA) was carried out for markers with P<10-3 near known genes identified by multivariate analysis.
Loci (rs10504906, rs13267998, rs921231) in or near the anion exchanger solute carrier family 26, member 7 isoform a (SLC26A7) were strongly associated with upper trunk and arm SAT (9.8*10-7≤P<7.8*10-6). Loci (rs193139, rs7523050, rs1761621) in and near a gene rich region including G-protein-signaling modulator 2 (GPSM2) and syntaxin binding protein 3 (STXBP3) were significantly associated with lower body SAT depots (9.9*10-7≤P<9.5*10-6). GPSM2 is associated with cell division and cancer while STXBP3 is associated with glucose metabolism in adipoctyes. IPA identified atherosclerosis, mitochondrial function and T-Cell mediated apoptosis as processes related to SAT volume in HIV-infected individuals (P<5*10-3).
Our results are limited by the small sample size and replication is needed, however this genomic scan uncovered new genes associated with metabolism and inflammatory pathways that may affect SAT volume in ARV-treated HIV-infected patients.
HIV; HAART; GWAS; Subcutaneous Fat; SAT
African Americans are a genetically diverse population with a high burden of many, common heritable diseases. However, our understanding of genetic variation in African Americans is substandard because of a lack of published population-based genetic studies. We report the distribution of copy-number variation (CNV) in African Americans collected as part of the Hypertension Genetic Epidemiology Network (HyperGEN) using the Affymetrix 6.0 array and the CNV calling algorithms Birdsuite and PennCNV. We present population estimates of CNV from 446 unrelated African-American subjects randomly selected from the 451 families collected within HyperGEN. Although the majority of CNVs discovered were individually rare, we found the frequency of CNVs to be collectively high. We identified a total of 11 070 CNVs greater than 10 kb passing quality control criteria that were called by both algorithms – leading to an average of 24.8 CNVs per person covering 2214 kb (median). We identified 1541 unique copy-number variable regions, 309 of which did not overlap with the Database of Genomic Variants. These results provide further insight into the distribution of CNV in African Americans.
DNA copy-number variation; African American; calling algorithm; Birdsuite; PennCNV; HyperGEN
A recent large-scale meta-analysis of genome-wide studies has identified 95 loci, 59 of them novel, as statistically significant predictors of blood lipid traits; we tested whether the same loci explain the observed heterogeneity in response to lipid-lowering therapy with fenofibrate. Using data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 861) we fit linear mixed models with the genetic markers as predictors and high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, and triglyceride concentrations as outcomes. For all four traits, we analyzed both baseline levels and changes in response to treatment with fenofibrate. For the markers that were significantly associated with fenofibrate response, we fit additional models evaluating potential epistatic interactions. All models were adjusted for age, sex, and study center as fixed effects, and pedigree as a random effect. Statistically significant associations were observed between the rs964184 polymorphism near APOA1 (P-value≤0.0001) and fenofibrate response for HDL and triglycerides. The association was replicated in the Pharmacogenetics of Hypertriglyceridemia in Hispanics study (HyperTG, n = 267). Suggestive associations with fenofibrate response were observed for markers in or near PDE3A, MOSC1, FLJ36070, CETP, the APOE-APOC1-APOC4-APOC2, and CILP2. Finally, we present strong evidence for epistasis (P-value for interaction = 0.0006 in GOLDN, 0.05 in HyperTG) between rs10401969 near CILP2 and rs4420638 in the APOE-APOC1-APOC4-APOC2 cluster with total cholesterol response to fenofibrate. In conclusion, we present evidence linking several novel and biologically relevant genetic polymorphisms to lipid lowering drug response, as well as suggesting novel gene-gene interactions in fenofibrate pharmacogenetics.
Longitudinal cohort studies normally identify and adjudicate incident events detected during follow-up by retrieving medical records. There are several reasons why the adjudication process may not be successfully completed for a suspected event including the inability to retrieve medical records from hospitals and an insufficient time between the suspected event and data analysis. These “incomplete adjudications” are normally assumed not to be events, an approach which may be associated with loss of precision and introduction of bias. In this article, the authors evaluate the use of multiple imputation methods designed to include incomplete adjudications in analysis. Using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study, 2008−2009, they demonstrate that this approach may increase precision and reduce bias in estimates of the relations between risk factors and incident events.
cohort studies; imputation; longitudinal studies; missing data
Numerous studies have provided support for genetic susceptibility to tuberculosis (TB); however, heterogeneity in disease expression has hampered previous genetic studies. The purpose of this work was to investigate possible intermediate phenotypes for TB. A set of cytokine profiles, including antigen-stimulated whole-blood assays for interferon (IFN)–γ, tumor necrosis factor (TNF)–α, transforming growth factor (TGF)–β, and the ratio of IFN to TNF, were analyzed in 177 pedigrees from a community in Uganda with a high prevalence of TB. The heritability of these variables was estimated after adjustment for covariates, and TNF-α, in particular, had an estimated heritability of 68%. A principal component analysis of IFN-γ, TNF-α, and TGF-β reflected the immunologic model of TB. In this analysis, the first component explained >38% of the variation in the data. This analysis illustrates the value of such intermediate phenotypes in mapping susceptibility loci for TB and demonstrates that this area deserves further research.