Background

Mild retinopathy (microaneurysms or dot-blot hemorrhages) is observed in persons without diabetes or hypertension and may reflect microvascular disease in other organs. We conducted a genome-wide association study (GWAS) of mild retinopathy in persons without diabetes.

Methods

A working group agreed on phenotype harmonization, covariate selection and analytic plans for within-cohort GWAS. An inverse-variance weighted fixed effects meta-analysis was performed with GWAS results from six cohorts of 19,411 Caucasians. The primary analysis included individuals without diabetes and secondary analyses were stratified by hypertension status. We also singled out the results from single nucleotide polymorphisms (SNPs) previously shown to be associated with diabetes and hypertension, the two most common causes of retinopathy.

Results

No SNPs reached genome-wide significance in the primary analysis or the secondary analysis of participants with hypertension. SNP, rs12155400, in the histone deacetylase 9 gene (HDAC9) on chromosome 7, was associated with retinopathy in analysis of participants without hypertension, −1.3±0.23 (beta ± standard error), p = 6.6×10−9. Evidence suggests this was a false positive finding. The minor allele frequency was low (∼2%), the quality of the imputation was moderate (r2 ∼0.7), and no other common variants in the HDAC9 gene were associated with the outcome. SNPs found to be associated with diabetes and hypertension in other GWAS were not associated with retinopathy in persons without diabetes or in subgroups with or without hypertension.

Conclusions

This GWAS of retinopathy in individuals without diabetes showed little evidence of genetic associations. Further studies are needed to identify genes associated with these signs in order to help unravel novel pathways and determinants of microvascular diseases.

Genetic factors explain a majority of risk variance for age-related macular degeneration (AMD). While genome-wide association studies (GWAS) for late AMD implicate genes in complement, inflammatory and lipid pathways, the genetic architecture of early AMD has been relatively under studied. We conducted a GWAS meta-analysis of early AMD, including 4,089 individuals with prevalent signs of early AMD (soft drusen and/or retinal pigment epithelial changes) and 20,453 individuals without these signs. For various published late AMD risk loci, we also compared effect sizes between early and late AMD using an additional 484 individuals with prevalent late AMD. GWAS meta-analysis confirmed previously reported association of variants at the complement factor H (CFH) (peak P = 1.5×10−31) and age-related maculopathy susceptibility 2 (ARMS2) (P = 4.3×10−24) loci, and suggested Apolipoprotein E (ApoE) polymorphisms (rs2075650; P = 1.1×10−6) associated with early AMD. Other possible loci that did not reach GWAS significance included variants in the zinc finger protein gene GLI3 (rs2049622; P = 8.9×10−6) and upstream of GLI2 (rs6721654; P = 6.5×10−6), encoding retinal Sonic hedgehog signalling regulators, and in the tyrosinase (TYR) gene (rs621313; P = 3.5×10−6), involved in melanin biosynthesis. For a range of published, late AMD risk loci, estimated effect sizes were significantly lower for early than late AMD. This study confirms the involvement of multiple established AMD risk variants in early AMD, but suggests weaker genetic effects on the risk of early AMD relative to late AMD. Several biological processes were suggested to be potentially specific for early AMD, including pathways regulating RPE cell melanin content and signalling pathways potentially involved in retinal regeneration, generating hypotheses for further investigation.

Background

While laboratory data suggest that antidepressants may promote mammary tumor growth, there has been little research investigating whether antidepressant use after breast cancer diagnosis is associated with the risk of breast cancer recurrence.

Methods

We conducted a retrospective cohort study within Group Health, an integrated healthcare delivery system in Washington state. Women diagnosed with a first primary invasive, stage I, IIA, or IIB, unilateral breast carcinoma between 1990–1994 (aged ≥65 years) and 1996–1999 (aged ≥18 years) were eligible for the study (N=1306). Recurrence within 5-years of diagnosis was ascertained by medical chart review. We used the pharmacy database to identify antidepressant dispensings from Group Health pharmacies. We used multiple Cox regression to estimate the hazard ratio for recurrence and breast cancer mortality, comparing users and non-users antidepressant medications. Results for recurrence were examined separately in users and non-users of tamoxifen.

Results

We did not observe an association between antidepressant use after breast cancer diagnosis and the risk of recurrence either in general (hazard ratio for any antidepressant use: 0.8; 95% confidence interval: 0.5 to 1.4) or for specific types of antidepressant medication. Risk of death from breast cancer did not differ between non-users and users of antidepressants.

Conclusions

The results of this study suggest that women who use antidepressants after breast cancer diagnosis do not have an increased risk of recurrence or mortality.

Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the “parameter bootstrap” that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.

Background

For residents of long term care, hospitalisations can cause distress and disruption, and often result in further medical complications. Multi-disciplinary team interventions have been shown to improve the health of Residential Aged Care (RAC) residents, decreasing the need for acute hospitalisation, yet there are few randomised controlled trials of these complex interventions. This paper describes a randomised controlled trial of a structured multi-disciplinary team and gerontology nurse specialist (GNS) intervention aiming to reduce residents’ avoidable hospitalisations.

Methods/Design

This Aged Residential Care Healthcare Utilisation Study (ARCHUS) is a cluster- randomised controlled trial (n = 1700 residents) of a complex multi-disciplinary team intervention in long-term care facilities. Eligible facilities certified for residential care were selected from those identified as at moderate or higher risk of resident potentially avoidable hospitalisations by statistical modelling. The facilities were all located in the Auckland region, New Zealand and were stratified by District Health Board (DHB).

Intervention

The intervention provided a structured GNS intervention including a baseline facility needs assessment, quality indicator benchmarking, a staff education programme and care coordination. Alongside this, three multi-disciplinary team (MDT) meetings were held involving a geriatrician, facility GP, pharmacist, GNS and senior nursing staff.

Outcomes

Hospitalisations are recorded from routinely-collected acute admissions during the 9-month intervention period followed by a 5-month follow-up period. ICD diagnosis codes are used in a pre-specified definition of potentially reducible admissions.

Discussion

This randomised-controlled trial will evaluate a complex intervention to increase early identification and intervention to improve the health of residents of long term care. The results of this trial are expected in early 2013.

Trial registration

Australian New Zealand Clinical Trials Registry: ACTRN 12611000187943

Objectives

To produce valid seroincidence estimates, the serologic testing algorithm for recent HIV seroconversion (STARHS) assumes independence between infection and testing, which may be absent in clinical data. STARHS estimates are generally greater than cohort-based estimates of incidence from observable person-time and diagnosis dates. The authors constructed a series of partial stochastic models to examine whether testing motivated by suspicion of infection could bias STARHS.

Methods

One thousand Monte Carlo simulations of 10,000 men who have sex with men (MSM) were generated using parameters for HIV incidence and testing frequency from data from a clinical testing population in Seattle. In one set of simulations, infection and testing dates were independent. In another set, some intertest intervals were abbreviated to reflect the distribution of intervals between suspected HIV exposure and testing in a group of Seattle MSM recently diagnosed with HIV. Both estimation methods were applied to the simulated datasets. Both cohort-based and STARHS incidence estimates were calculated using the simulated data and compared with previously calculated, empirical cohort-based and STARHS seroincidence estimates from the clinical testing population.

Results

Under simulated independence between infection and testing, cohort-based and STARHS incidence estimates resembled cohort estimates from the clinical dataset. Under simulated motivated testing, cohort-based estimates remained unchanged but STARHS estimates were inflated similar to empirical STARHS estimates. Varying motivation parameters appreciably affected STARHS incidence estimates, but not cohort-based estimates.

Conclusions

Cohort-based incidence estimates are robust against dependence between testing and acquisition of infection whereas STARHS incidence estimates are not.

Objectives

The serologic testing algorithm for recent HIV seroconversion (STARHS) calculates incidence using the proportion of testers who produce a level of HIV antibody high enough to be detected by ELISA but low enough to suggest recent infection. The validity of STARHS relies on independence between dates of HIV infection and dates of antibody testing. When subjects choose the time of their own test, testing may be motivated by risky behaviour or symptoms of infection and the criterion may not be met. This analysis was conducted to ascertain whether estimates of incidence derived using STARHS were consistent with estimates derived using a method more robust against motivated testing.

Methods

A cohort-based incidence estimator and two STARHS methods were applied to identical populations (n=3821) tested for HIV antibody at publicly funded sites in Seattle. Overall seroincidence estimates, demographically stratified estimates and incidence rate ratios were compared across methods. The proportion of low-antibody testers among HIV-infected individuals was compared with the proportion expected given their testing histories.

Results

STARHS estimates generally exceeded cohort-based estimates. Incidence ratios derived using STARHS between demographic strata were not consistent across methods. The proportion of HIV-infected individuals with lower antibody levels exceeded that which would be expected under independence between infection and testing.

Conclusions

Incidence estimates and incidence rate ratios derived using methods that rely on the changing antibody level over the course of HIV infection may be vulnerable to bias when applied to populations who choose the time of their own testing.

The integrated discrimination improvement (IDI) index is a popular tool for evaluating the capacity of a marker to predict a binary outcome of interest. Recent reports have proposed that the IDI is more sensitive than other metrics for identifying useful predictive markers. In this article, the authors use simulated data sets and theoretical analysis to investigate the statistical properties of the IDI. The authors consider the common situation in which a risk model is fitted to a data set with and without the new, candidate predictor(s). Results demonstrate that the published method of estimating the standard error of an IDI estimate tends to underestimate the error. The z test proposed in the literature for IDI-based testing of a new biomarker is not valid, because the null distribution of the test statistic is not standard normal, even in large samples. If a test for the incremental value of a marker is desired, the authors recommend the test based on the model. For investigators who find the IDI to be a useful measure, bootstrap methods may offer a reasonable option for inference when evaluating new predictors, as long as the added predictive capacity is large.

The general availability of reliable and affordable genotyping technology has enabled genetic association studies to move beyond small case-control studies to large prospective studies. For prospective studies, genetic information can be integrated into the analysis via haplotypes, with focus on their association with a censored survival outcome. We develop non-iterative, regression-based methods to estimate associations between common haplotypes and a censored survival outcome in large cohort studies. Our non-iterative methods—weighted estimation and weighted haplotype combination—are both based on the Cox regression model, but differ in how the imputed haplotypes are integrated into the model. Our approaches enable haplotype imputation to be performed once as a simple data-processing step, and thus avoid implementation based on sophisticated algorithms that iterate between haplotype imputation and risk estimation. We show that non-iterative weighted estimation and weighted haplotype combination provide valid tests for genetic associations and reliable estimates of moderate associations between common haplotypes and a censored survival outcome, and are straightforward to implement in standard statistical software. We apply the methods to an analysis of HSPB7-CLCNKA haplotypes and risk of adverse outcomes in a prospective cohort study of outpatients with chronic heart failure.

Background and Purpose

Little is known about acute precipitants of ischemic stroke, although evidence suggests infections contribute to risk. We hypothesized that acute hospitalization for infection is associated with short-term risk of stroke.

Methods

The case-crossover design was used to compare hospitalization for infection during case periods (90, 30, or 14 days prior to incident ischemic stroke) and control periods (equivalent time periods exactly 1 or 2 years prior to stroke) in the Cardiovascular Health Study, a population-based cohort of 5888 elderly participants from 4 US sites. Odds ratios and 95% confidence intervals (OR, 95% CI) were calculated using conditional logistic regression. Confirmatory analyses assessed hazard ratios (HR) of stroke from Cox regression models with hospitalization for infection as a time-varying exposure.

Results

During a median follow-up of 12.2 years, 669 incident ischemic strokes were observed in participants without baseline history of stroke. Hospitalization for infection was more likely during case than control time periods; for 90 days prior to stroke, OR=3.4 (95% CI 1.8–6.5). The point estimates of risks were higher when examining shorter intervals: for 30 days, OR= 7.3 (95% CI 1.9–40.9), and 14 days, OR=8.0 (95% CI 1.7–77.3). In survival analyses, risk of stroke was associated with hospitalization for infection in the preceding 90 days, adjusted HR=2.4 (95% CI 1.6–3.4).

Conclusions

Hospitalization for infection is associated with a short-term increased risk of stroke, with higher risks observed for shorter intervals preceding stroke.

Background

Elevated serum urate levels can lead to gout and are associated with cardiovascular risk factors. We performed genome-wide association to search for genetic susceptibility loci for serum urate and gout, and investigated the causal nature of the associations of serum urate with gout and selected cardiovascular risk factors and coronary heart disease (CHD).

Methods and Results

Meta-analyses of genome-wide association studies (GWAS) were performed in 5 population-based cohorts of the CHARGE consortium for serum urate and gout in 28,283 white individuals. The effect of the most significant SNP at all genome-wide significant loci on serum urate was added to create a genetic urate score. Findings were replicated in the Women’s Genome Health Study (WGHS; n=22,054). SNPs at 8 genetic loci achieved genome-wide significance with serum urate levels (p-values 4×10−8 to 2×10−242; SLC22A11, GCKR, R3HDM2-INHBC region, RREB1, PDZK1, SLC2A9, ABCG2, SLC17A1). Only two loci [SLC2A9, ABCG2] showed genome-wide significant association with gout. The genetic urate score was strongly associated with serum urate and gout (odds ratio 12.4 per 100 umol/L; p-value=3×10−39), but not with blood pressure, glucose, eGFR, chronic kidney disease, or CHD. The lack of association between the genetic score and the latter phenotypes was also observed in WGHS.

Conclusions

The genetic urate score analysis suggested a causal relationship between serum urate and gout but did not provide evidence for one between serum urate and cardiovascular risk factors and CHD.

Objective

White matter hyperintensities (WMH) detectable by magnetic resonance imaging (MRI)are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMH are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified.

Methods

We performed a meta-analysis of genome-wide association studies (GWAS) for WMH burden in 9,361 stroke-free individuals of European descent from 7 community-based cohorts. Significant findings were tested for replication in 3,024 individuals from 2 additional cohorts.

Results

We identified 6 novel risk-associated single nucleotide polymorphisms (SNPs)in one locus on chromosome 17q25 encompassing 6 known genes including WBP2, TRIM65, TRIM47, MRPL38, FBF1, and ACOX1. The most significant association was for rs3744028 (Pdiscovery= 4.0×10−9; Preplication =1.3×10−7; Pcombined =4.0×10−15). Other SNPs in this region also reaching genome-wide significance are rs9894383 (P=5.3×10−9), rs11869977 (P=5.7×10−9), rs936393 (P=6.8×10−9), rs3744017 (P=7.3×10−9), and rs1055129 (P=4.1×10−8). Variant alleles at these loci conferred a small increase in WMH burden (4–8% of the overall mean WMH burden in the sample).

Interpretation

This large GWAS of WMH burden in community-based cohorts of individuals of European descent identifies a novel locus on chromosome 17. Further characterization of this locus may provide novel insights into the pathogenesis of cerebral WMH.

Objective

The withdrawal of cerivastatin involved an uncommon but serious adverse reaction, rhabdomyolysis. The bimodal response--rhabdomyolysis in a small proportion of users-- points to genetic factors as a potential cause. We conducted a case-control study to evaluate genetic markers for cerivastatin-associated rhabdomyolysis.

Methods

The study had two components: a candidate gene study to evaluate variants in CYP2C8, UGT1A1, UGT1A3, and SLCO1B1; and a genome-wide association (GWA) study to identify risk factors in other regions of the genome. 185 rhabdomyolysis cases were frequency matched to statin-using controls from the Cardiovascular Health Study (n=374) and the Heart and Vascular Health Study (n=358). Validation relied on functional studies.

Results

Permutation test results suggested an association between cerivastatin-associated rhabdomyolysis and variants in SLCO1B1 (p = 0.002), but not variants in CYP2C8 (p = 0.073) or the UGTs (p = 0.523). An additional copy of the minor allele of SLCO1B1 rs4149056 (p.Val174Ala) was associated with the risk of rhabdomyolysis (OR: 1.89, 95% CI: 1.40 to 2.56). In transfected cells, this variant reduced cerivastatin transport by 40% compared with the reference transporter (p < 0.001). The GWA identified an intronic variant (rs2819742) in the ryanodine receptor 2 gene (RYR2) as significant (p=1.74E-07). An additional copy of the minor allele of the RYR2 variant was associated with a reduced risk of rhabdomyolysis (OR: 0.48; 95% CI: 0.36 to 0.63).

Conclusion

We identified modest genetic risk factors for an extreme response to cerivastatin. Disabling genetic variants in the candidate genes were not responsible for the bimodal response to cerivastatin.

The two-phase design has recently received attention in the statistical literature as an extension to the traditional case-control study for settings where a predictor of interest is rare or subject to missclassification. Despite a thorough methodological treatment and the potential for substantial efficiency gains, the two-phase design has not been widely adopted. This may be due, in part, to a lack of general-purpose, readily-available software. The osDesign package for R provides a suite of functions for analyzing data from a two-phase and/or case-control design, as well as evaluating operating characteristics, including bias, efficiency and power. The evaluation is simulation-based, permitting flexible application of the package to a broad range of scientific settings. Using lung cancer mortality data from Ohio, the package is illustrated with a detailed case-study in which two statistical goals are considered: (i) the evaluation of small-sample operating characteristics for two-phase and case-control designs and (ii) the planning and design of a future two-phase study.

Summary

Permutation tests are widely used in genomic research as a straightforward way to obtain reliable statistical inference without making strong distributional assumptions. However, in this paper we show that in genetic association studies it is not typically possible to construct exact permutation tests of gene-gene or gene-environment interaction hypotheses. We describe an alternative to the permutation approach in testing for interaction, a parametric bootstrap approach. Using simulations, we compare the finite-sample properties of a few often-used permutation tests and the parametric bootstrap. We consider interactions of an exposure with single and multiple polymorphisms. Finally, we address when permutation tests of interaction will be approximately valid in large samples for specific test statistics.

Background

Fibrin fragment D-dimer is one of several peptides produced when cross-linked fibrin is degraded by plasmin, and is the most widely-used clinical marker of activated blood coagulation. To identity genetic loci influencing D-dimer levels, we performed the first large-scale, genome-wide association search.

Methods and Results

A genome-wide investigation of the genomic correlates of plasma D-dimer levels was conducted among 21,052 European-ancestry adults. Plasma levels of D-dimer were measured independently in each of 13 cohorts. Each study analyzed the association between ~2.6 million genotyped and imputed variants across the 22 autosomal chromosomes and natural-log transformed D-dimer levels using linear regression in additive genetic models adjusted for age and sex. Among all variants, 74 exceeded the genome-wide significance threshold and marked 3 regions. At 1p22, rs12029080 (p-value 6.4×10−52) was 46.0 kb upstream from F3, coagulation factor III (tissue factor). At 1q24, rs6687813 (p-value 2.4×10−14) was 79.7 kb downstream of F5, coagulation factor V. At 4q32, rs13109457 (p-value 2.9×10−18) was located between 2 fibrinogen genes: 10.4 kb downstream from FGG and 3.0 kb upstream from FGA. Variants were associated with a 0.099, 0.096, and 0.061 unit difference, respectively, in natural-log transformed D-dimer and together accounted for 1.8% of the total variance. When adjusted for non-synonymous substitutions in F5 and FGA loci known to be associated with D-dimer levels, there was no evidence of an additional association at either locus.

Conclusions

Three genes were associated with fibrin D-dimer levels, of which the F3 association was the strongest and has not been previously reported.

Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.

BACKGROUND

Diabetes may be an independent risk factor for atrial fibrillation. However, results from prior studies are in conflict, and no study has examined diabetes duration or glycemic control.

OBJECTIVE

To examine the association of diabetes with risk of atrial fibrillation and to describe risk according to diabetes duration and glycemic control.

DESIGN

A population-based case-control study.

PARTICIPANTS

Within a large, integrated healthcare delivery system, we identified 1,410 people with newly-recognized atrial fibrillation from ICD-9 codes and validated cases by review of medical records. 2,203 controls without atrial fibrillation were selected from enrollment lists, stratified on age, sex, hypertension, and calendar year.

MAIN MEASURES

Information on atrial fibrillation, diabetes and other characteristics came from medical records. Diabetes was defined based on physician diagnoses recorded in the medical record, and pharmacologically treated diabetes was defined as receiving antihyperglycemic medications. Information about hemoglobin A1c levels came from computerized laboratory data.

KEY RESULTS

Among people with atrial fibrillation, 252/1410 (17.9%) had pharmacologically treated diabetes compared to 311/2203 (14.1%) of controls. The adjusted OR for atrial fibrillation was 1.40 (95% CI 1.15-1.71) for people with treated diabetes compared to those without diabetes. Among those with treated diabetes, the risk of developing atrial fibrillation was 3% higher for each additional year of diabetes duration (95% CI 1-6%). Compared to people without diabetes, the adjusted OR for people with treated diabetes with average hemoglobin A1c ≤7 was 1.06 (95% CI 0.74-1.51); for A1c >7 but ≤8, 1.48 (1.09-2.01); for A1c >8 but ≤9, 1.46 (1.02-2.08); and for A1c >9, 1.96 (1.22–3.14).

CONCLUSIONS

Diabetes was associated with higher risk of developing atrial fibrillation, and risk was higher with longer duration of treated diabetes and worse glycemic control. Future research should identify and test approaches to reduce the risk of atrial fibrillation in people with diabetes.

Long-chain n-3 polyunsaturated fatty acids (PUFAs) can derive from diet or from α-linolenic acid (ALA) by elongation and desaturation. We investigated the association of common genetic variation with plasma phospholipid levels of the four major n-3 PUFAs by performing genome-wide association studies in five population-based cohorts comprising 8,866 subjects of European ancestry. Minor alleles of SNPs in FADS1 and FADS2 (desaturases) were associated with higher levels of ALA (p = 3×10−64) and lower levels of eicosapentaenoic acid (EPA, p = 5×10−58) and docosapentaenoic acid (DPA, p = 4×10−154). Minor alleles of SNPs in ELOVL2 (elongase) were associated with higher EPA (p = 2×10−12) and DPA (p = 1×10−43) and lower docosahexaenoic acid (DHA, p = 1×10−15). In addition to genes in the n-3 pathway, we identified a novel association of DPA with several SNPs in GCKR (glucokinase regulator, p = 1×10−8). We observed a weaker association between ALA and EPA among carriers of the minor allele of a representative SNP in FADS2 (rs1535), suggesting a lower rate of ALA-to-EPA conversion in these subjects. In samples of African, Chinese, and Hispanic ancestry, associations of n-3 PUFAs were similar with a representative SNP in FADS1 but less consistent with a representative SNP in ELOVL2. Our findings show that common variation in n-3 metabolic pathway genes and in GCKR influences plasma phospholipid levels of n-3 PUFAs in populations of European ancestry and, for FADS1, in other ancestries.

Author Summary

Circulating long-chain n-3 polyunsaturated fatty acids (PUFAs) derive from fatty fish or from the conversion of the plant n-3 PUFA by elongation and desaturation. We looked for common genetic markers throughout the genome that might influence plasma phospholipid levels of the four major n-3 PUFAs in five large studies and pooled the results. We found that levels of all four n-3 PUFAs were associated with genetic markers in known desaturation and elongation genes. We also found evidence that conversion of the plant n-3 PUFA to longer chain n-3 PUFAs is less effective in people with certain desaturation-gene markers, which could be important for people who do not eat fish. We also found a marker in a gene involved in glucose metabolism, called the glucokinase regulator, to be associated with one intermediate n-3 PUFA. Some of these findings were seen across multiple race/ethnicities. Overall, these results have implications for how genes and the environment interact to influence circulating levels of fatty acids.

Little is known of the associations of endogenous fatty acids with sudden cardiac arrest (SCA). We investigated the associations of SCA with red blood cell membrane fatty acids that are end products of de novo fatty acid synthesis: myristic acid (14:0), palmitic acid (16:0), palmitoleic acid (16:1 n7), vaccenic acid (18:1 n7), stearic acid (18:0), oleic acid (18:1 n9) and a related fatty acid cis-7 hexadecenoic acid (16:1 n9). We used data from a population-based case-control study, where cases, aged 25-74 years, were out-of-hospital sudden cardiac arrest patients, attended by paramedics in Seattle, Washington (n=265). Controls, matched to cases by age, sex and calendar year, were randomly identified from the community (n=415). All participants were free of prior clinically-diagnosed heart disease. We observed associations of higher red blood cell membrane levels of 16:0, 16:1n-7, 18:1n-7 and 16:1n-9 with higher risk of SCA. In analyses adjusted for traditional SCA risk factors and trans- and n-3 fatty acids, a one-standard-deviation-higher level of 16:0 was associated with 38% higher risk of SCA (odds ratio [OR] 1.38, 95% confidence interval [CI]: 1.12-1.70) and a one-standard deviation-higher level of 16:1n-9 with 88% higher risk (OR 1.88, 95% CI: 1.27-2.78). Several fatty acids that are end products of fatty acid synthesis are associated with SCA risk. Further work is needed to investigate if conditions that favor de novo fatty acid synthesis, such as high carbohydrate/low fat diets, might also increase the risk of SCA.

White blood cell (WBC) count is a common clinical measure from complete blood count assays, and it varies widely among healthy individuals. Total WBC count and its constituent subtypes have been shown to be moderately heritable, with the heritability estimates varying across cell types. We studied 19,509 subjects from seven cohorts in a discovery analysis, and 11,823 subjects from ten cohorts for replication analyses, to determine genetic factors influencing variability within the normal hematological range for total WBC count and five WBC subtype measures. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We identified and replicated ten associations with total WBC count and five WBC subtypes at seven different genomic loci (total WBC count—6p21 in the HLA region, 17q21 near ORMDL3, and CSF3; neutrophil count—17q21; basophil count- 3p21 near RPN1 and C3orf27; lymphocyte count—6p21, 19p13 at EPS15L1; monocyte count—2q31 at ITGA4, 3q21, 8q24 an intergenic region, 9q31 near EDG2), including three previously reported associations and seven novel associations. To investigate functional relationships among variants contributing to variability in the six WBC traits, we utilized gene expression- and pathways-based analyses. We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. Gene expression data from whole blood was utilized to show that significant biological consequences can be extracted from our genome-wide analyses, with effect estimates for significant loci from the meta-analyses being highly corellated with the proximal gene expression. In addition, collaborative efforts between the groups contributing to this study and related studies conducted by the COGENT and RIKEN groups allowed for the examination of effect homogeneity for genome-wide significant associations across populations of diverse ancestral backgrounds.

Author Summary

WBC traits are highly variable, moderately heritable, and commonly assayed as part of clinical complete blood count (CBC) examinations. The counts of constituent cell subtypes comprising the WBC count measure are assayed as part of a standard clinical WBC differential test. In this study we employed meta-analytic techniques and identified ten associations with WBC measures at seven genomic loci in a large sample set of over 31,000 participants. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We confirm previous associations of WBC traits with three loci and identified seven novel loci. We also utilize a number of additional analytic methods to infer the functional relatedness of independently implicated loci across WBC phenotypes, as well as investigate direct functional consequences of these loci through analyses of genomic variation affecting the expression of proximal genes in samples of whole blood. In addition, subsequent collaborative efforts with studies of WBC traits in African-American and Japanese cohorts allowed for the investigation of the effects of these genomic variants across populations of diverse continental ancestries.

Background

Although genetic factors contribute to the onset of heart failure (HF), no large-scale genome-wide investigation of HF risk has been published to date. We investigated the association of 2,478,304 single nucleotide polymorphisms (SNPs) with incident HF by meta-analyzing data from 4 community-based prospective cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study.

Methods and Results

Eligible participants for these analyses were of European or African ancestry and free of clinical HF at baseline. Each study independently conducted genome-wide scans and imputed data to the ~2.5 million SNPs in HapMap. Within each study, Cox proportional hazards regression models provided age- and sex-adjusted estimates of the association between each variant and time to incident HF. Fixed-effect meta-analyses combined results for each SNP from the 4 cohorts to produce an overall association estimate and p-value. A genome-wide significance p-value threshold was set a priori at 5.0×10−7. During a mean follow-up of 11.5 years, 2,526 incident HF events (12%) occurred in 20,926 European-ancestry participants. The meta-analysis identified a genome-wide significant locus at chromosomal position 15q22 (1.4×10−8), which was 58.8 kb from USP3. Among 2,895 African-ancestry participants, 466 incident HF events (16%) occurred during a mean follow-up of 13.7 years. One genome-wide significant locus was identified at 12q14 (6.7×10−8), which was 6.3 kb from LRIG3.

Conclusions

We identified 2 loci that were associated with incident HF and exceeded genome-wide significance. The findings merit replication in other community-based settings of incident HF.

Background

Prognosis and survival are significant concerns for individuals with heart failure (HF). In order to better understand the pathophysiology of HF prognosis, the association between 2,366,858 single nucleotide polymorphisms (SNPs) and all-cause mortality was evaluated among individuals with incident HF from four community-based prospective cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study.

Methods and Results

Participants were 2,526 individuals of European ancestry and 466 individuals of African ancestry who suffered an incident HF event during follow-up in the respective cohorts. Within each study, the association between genetic variants and time to mortality among individuals with HF was assessed by Cox proportional hazards models that included adjustment for sex and age at the time of the HF event. Prospective fixed-effect meta-analyses were conducted for the four study populations of European ancestry (N=1,645 deaths) and for the two populations of African ancestry (N=281 deaths). Genome-wide significance was set at P=5.0×10-7. Meta-analytic findings among individuals of European ancestry revealed one genome-wide significant locus on chromosome 3p22 in an intron of CKLF-like MARVEL transmembrane domain containing 7 (CMTM7, p = 3.2×10-7). Eight additional loci in individuals of European ancestry and four loci in individuals of African ancestry were identified by high-signal SNPs (p < 1.0×10-5), but did not meet genome-wide significance.

Conclusions

This study identified a novel locus associated with all-cause mortality among individuals of European ancestry with HF. This finding warrants additional investigation, including replication, in other studies of HF.

Genome-wide association studies of gene-environment interaction (GxE GWAS) are becoming popular. As with main effects GWAS, quantile-quantile plots (QQ-plots) and Genomic Control are being used to assess and correct for population substructure. However, in GE work these approaches can be seriously misleading, as we illustrate; QQ-plots may give strong indications of substructure when absolutely none is present. Using simulation and theory, we show how and why spurious QQ-plot inflation occurs in GE GWAS, and how this differs from main-effects analyses. We also explain how simple adjustments to standard regression-based methods used in GE GWAS can alleviate this problem.

Chronic kidney disease (CKD) has a heritable component and is an important global public health problem because of its high prevalence and morbidity.1 We conducted genome-wide association studies (GWAS) to identify susceptibility loci for glomerular filtration rate estimated by serum creatinine (eGFRcrea), cystatin C (eGFRcys), and CKD (eGFRcrea<60 ml/min/1.73m2) in European-ancestry participants of four populations-based cohorts (ARIC, CHS, FHS, RS; n=19,877, 2,388 CKD cases), and tested for external replication in 21,466 participants (1,932 CKD cases). Significant associations (p<5*10−8) were identified for SNPs with [1] CKD at the UMOD locus; [2] eGFRcrea at the UMOD, SHROOM3, and GATM/SPATA5L1 loci; [3] eGFRcys at the CST and STC1 loci. UMOD encodes the most common protein in human urine, Tamm-Horsfall protein,2 and rare mutations in UMOD cause Mendelian forms of kidney disease.3 Our findings provide new insights into CKD pathogenesis and underscore the importance of common genetic variants influencing renal function and disease.