A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort

BACKGROUND

Cognitive decline, and more specifically Alzheimer's disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids.

OBJECTIVES

We aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual's genetically conferred risk of cognitive decline.

METHODS

The Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness).

RESULTS

Our analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits.

CONCLUSIONS

In this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.

Circulating Docosahexaenoic Acid and Risk of All-Cause and Cause-Specific Mortality

OBJECTIVE

To assess the associations of docosahexaenoic acid (DHA), a marine omega-3 fatty acid, with long-term all-cause mortality, cardiovascular (CV) mortality, and cancer mortality.

PATIENTS AND METHODS

We analyzed data from UK Biobank, which included 117,702 subjects with baseline plasma DHA levels and 12.7 years of follow-up between April 2007 and December 2021. Associations with risk for mortality endpoints were analyzed categorically by quintile of DHA plasma levels.

RESULTS

Comparing the lowest to highest quintiles of circulating levels of DHA, there was 21% lower risk of all-cause mortality (HR, 0.79; 95% CI, 0.74 to 0.85; P<.0001). In a secondary analysis, we merged the UK Biobank findings with those from a recent FORCE (Fatty Acid and Outcome Research Consortium) meta-analysis that included 17 prospective cohort studies and 42,702 individuals examining DHA and mortality associations. The cumulative sample population included 160,404 individuals and 24,342 deaths during a median of 14 years of follow-up. After multivariable adjustment for relevant risk factors comparing the lowest to the highest quintiles of DHA, there was 17% lower risk of all-cause mortality (95% CI, 0.79 to 0.87; P<.0001), 21% lower risk for CV disease mortality (95% CI, 0.73 to 0.87; P<.001), 17% lower risk for cancer mortality (95% CI, 0.77 to 0.89; P<.0001), and 15% lower risk for all other mortality (95% CI, 0.79 to 0.91; P<.001).

CONCLUSION

Higher DHA levels were associated with significant risk reductions in all-cause mortality, as well as reduced risks for deaths due to CV disease, cancer, and all other causes. The findings strengthen the hypothesis that DHA, a marine-sourced omega-3, may support CV health and lifespan.

Omega-3 Blood Levels and Stroke Risk: A Pooled and Harmonized Analysis of 183 291 Participants From 29 Prospective Studies

BACKGROUND

The effect of marine omega-3 PUFAs on risk of stroke remains unclear.

METHODS

We investigated the associations between circulating and tissue omega-3 PUFA levels and incident stroke (total, ischemic, and hemorrhagic) in 29 international prospective cohorts. Each site conducted a de novo individual-level analysis using a prespecified analytical protocol with defined exposures, covariates, analytical methods, and outcomes; the harmonized data from the studies were then centrally pooled. Multivariable-adjusted HRs and 95% CIs across omega-3 PUFA quintiles were computed for each stroke outcome.

RESULTS

Among 183 291 study participants, there were 10 561 total strokes, 8220 ischemic strokes, and 1142 hemorrhagic strokes recorded over a median of 14.3 years follow-up. For eicosapentaenoic acid, comparing quintile 5 (Q5, highest) with quintile 1 (Q1, lowest), total stroke incidence was 17% lower (HR, 0.83 [CI, 0.76-0.91]; P<0.0001), and ischemic stroke was 18% lower (HR, 0.82 [CI, 0.74-0.91]; P<0.0001). For docosahexaenoic acid, comparing Q5 with Q1, there was a 12% lower incidence of total stroke (HR, 0.88 [CI, 0.81-0.96]; P=0.0001) and a 14% lower incidence of ischemic stroke (HR, 0.86 [CI, 0.78-0.95]; P=0.0001). Neither eicosapentaenoic acid nor docosahexaenoic acid was associated with a risk for hemorrhagic stroke. These associations were not modified by either baseline history of AF or prevalent CVD.

CONCLUSIONS

Higher omega-3 PUFA levels are associated with lower risks of total and ischemic stroke but have no association with hemorrhagic stroke.

Genome-wide association studies and fine-mapping identify genomic loci for n-3 and n-6 polyunsaturated fatty acids in Hispanic American and African American cohorts

Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) play critical roles in human health. Prior genome-wide association studies (GWAS) of n-3 and n-6 PUFAs in European Americans from the CHARGE Consortium have documented strong genetic signals in/near the FADS locus on chromosome 11. We performed a GWAS of four n-3 and four n-6 PUFAs in Hispanic American (n = 1454) and African American (n = 2278) participants from three CHARGE cohorts. Applying a genome-wide significance threshold of P < 5 × 10-8, we confirmed association of the FADS signal and found evidence of two additional signals (in DAGLA and BEST1) within 200 kb of the originally reported FADS signal. Outside of the FADS region, we identified novel signals for arachidonic acid (AA) in Hispanic Americans located in/near genes including TMX2, SLC29A2, ANKRD13D and POLD4, and spanning a > 9 Mb region on chromosome 11 (57.5 Mb ~ 67.1 Mb). Among these novel signals, we found associations unique to Hispanic Americans, including rs28364240, a POLD4 missense variant for AA that is common in CHARGE Hispanic Americans but absent in other race/ancestry groups. Our study sheds light on the genetics of PUFAs and the value of investigating complex trait genetics across diverse ancestry populations.

Lower omega-3 status associated with higher erythrocyte distribution width and neutrophil-lymphocyte ratio in UK Biobank cohort

High red blood distribution width (RDW) is associated with decreased red blood cell deformability, and high neutrophil-lymphocyte ratio (NLR) is a biomarker of systemic inflammation and innate-adaptive immune system imbalance. Both RDW and NLR are predictors of chronic disease risk and mortality. Omega-3 index (O3I) values have previously been shown to be inversely associated with RDW and NLR levels. Our objective was to determine if total plasma long chain omega-3 fatty acids (Omega3%) measured in the UK Biobank cohort were associated with RDW and NLR values. RDW- and NLR- relationships with Omega3% were characterized in 109,191 adults (58.4% female). RDW- and NLR-Omega3% relationships were inversely associated with Omega3% (both p < 0.0001). These cross-sectional associations confirm previous findings that increasing RDW and NLR values are associated with low O3I. The hypothesis that RDW and/or NLR values can be reduced in individuals with less-than optimal long chain omega 3 values need to be tested in randomized controlled intervention trials using EPA and/or DHA.

Association between blood N-3 fatty acid levels and the risk of coronavirus disease 2019 in the UK Biobank

BACKGROUND

The role of nutritional status and the risk of contracting and/or experiencing adverse outcomes from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are unclear. Preliminary studies suggest that higher n-3 PUFA intakes are protective.

OBJECTIVES

This study aimed to compare the risk of 3 coronavirus disease 2019 (COVID-19) outcomes (testing positive for SARS-CoV-2, hospitalization, and death) as a function of the baseline plasma DHA levels.

METHODS

The DHA levels (% of total fatty acids [FAs]) were measured by nuclear magnetic resonance. The 3 outcomes and relevant covariates were available for 110,584 subjects (hospitalization and death) and for 26,595 ever-tested subjects (positive for SARS-CoV-2) in the UK Biobank prospective cohort study. Outcome data between 1 January, 2020, and 23 March, 2021, were included. The Omega-3 Index (O3I) (RBC EPA + DHA%) values across DHA% quintiles were estimated. The multivariable Cox proportional hazards models were constructed, and linear (per 1 SD) relations with the risk of each outcome were computed as HRs.

RESULTS

In the fully adjusted models, comparing the fifth to the first DHA% quintiles, the HRs (95% confidence intervals) for testing positive, being hospitalized, and dying with COVID-19 were 0.79 (0.71, 0.89, P < 0.001), 0.74 (0.58, 0.94, P < 0.05), and 1.04 (0.69-1.57, not significant), respectively. On a per 1-SD increase in DHA% basis, the HRs for testing positive, hospitalization, and death, were 0.92 (0.89, 0.96, P < 0.001), 0.89 (0.83, 0.97, P < 0.01), and 0.95 (0.83, 1.09), respectively. The estimated O3I values across DHA quintiles ranged from 3.5% (quintile 1) to 8% (quintile 5).

CONCLUSIONS

These findings suggest that nutritional strategies to increase the circulating n-3 PUFA levels, such as increased consumption of oily fish and/or use of n-3 FA supplements, may reduce the risk of adverse COVID-19 outcomes.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

The omega-3 index is inversely associated with the neutrophil-lymphocyte ratio in adults'

The neutrophil-lymphocyte ratio (NLR) is a biomarker of systemic inflammation and measures innate-adaptive immune system balance. The omega-3-index (O3I) measures the amount of EPA+DHA in blood. Both a low O3I and an elevated NLR are associated with increased risk for chronic disease and mortality, including cardiovascular diseases and cancer. Hypothesizing that low O3I may partly contribute to systemic chronic inflammation, we asked if a relationship existed between O3I and NLR in healthy adults (≥18 y, n = 28,871, 51% female) without inflammation [C-reactive protein (CRP) <3 mg/mL)] who underwent a routine clinical assessment. NLR was inversely associated with O3I before (p < 0.0001) and after adjusting for age, sex, BMI, and CRP (p < 0.0001). Pearson correlations of other variables with NLR were r = 0.06 (CRP), r = 0.14 (age), and r = 0.01(BMI). In this healthy population, an O3I < 6.6% was associated with increasing NLR whereas NLR remained relatively constant (low) when O3I > 6.6%, suggestive of a quiescent, balanced immune system.

Omega-3 index is directly associated with a healthy red blood cell distribution width

Low red blood cell (RBC) membrane content of EPA and DHA, i.e., the omega-3 index (O3I), and elevated RBC distribution width (RDW) are risk factors for all-cause mortality. O3I and RDW are related with membrane fluidity and deformability. Our objective was to determine if there is a relationship between O3I and RDW in healthy adults. Subjects without inflammation or anemia, and with values for O3I, RDW, high-sensitivity C-reactive protein (CRP), body mass index (BMI), age and sex were identified (n = 25,485) from a clinical laboratory dataset of  > 45,000 individuals. RDW was inversely associated with O3I in both sexes before and after (both p < 0.00001) adjusting models for sex, age, BMI and CRP. Stratification by sex revealed a sex-O3I interaction with the RDW-O3I slope (p < 0.00066) being especially steep in females with O3I ≤ 5.6%. In healthy adults of both sexes, the data suggested that an O3I of > 5.6% may help maintain normal RBC structural and functional integrity.

Investigating Rurality as a Risk Factor for State and Trait Hopelessness in Hospitalized Patients With Ischemic Heart Disease

Background

Rurality and hopelessness are each associated with increased mortality in adults with ischemic heart disease (IHD), yet there is no known research examining rurality as a risk factor for hopelessness in patients with IHD. This study evaluated rurality as a risk factor for state and trait hopelessness in adults hospitalized with IHD in samples drawn from the Great Lakes and Great Plains regions of the United States.

Methods and Results

A descriptive cross‐sectional design was used. Data were collected from 628 patients hospitalized for IHD in the Great Lakes (n=516) and Great Plains (n=112). Rural–Urban Commuting Area codes were used to stratify study participants by level of rurality. Levels of state hopelessness (measured by the State‐Trait Hopelessness Scale) were higher in rural patients (58.8% versus 48.8%; odds ratio [OR], 1.50; 95% CI, 1.03–2.18), a difference that remained statistically significant after adjusting for demographics, depression severity (measured by the Patient Health Questionnaire–8), and physical functioning (measured by the Duke Activity Status Index; OR, 1.59; 95% CI, 1.06–2.40; P=0.026). There was evidence of an interaction between marital status and rurality on state hopelessness after accounting for covariates (P=0.02). Nonmarried individuals had an increased prevalence of state hopelessness (nonmarried 72.0% versus married 52.0%) in rural areas (P=0.03).

Conclusions

Rural patients with IHD, particularly those who are nonmarried, may be at higher risk for state hopelessness compared with patients with IHD living in urban settings. Understanding rurality differences is important in identifying subgroups most at risk for hopelessness.

Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT04498975.

Reliability and Validity of the State-Trait Hopelessness Scale in Patients With Heart Disease and Moderate to Severe Hopelessness

OBJECTIVE

The aim of this study was to evaluate the reliability and validity of the State-Trait Hopelessness Scale (STHS) in patients with heart disease who report moderate to severe state hopelessness.

METHODS

Reliability, concurrent validity, and convergent validity were evaluated for 20 patients.

RESULTS

Cronbach's α for the State and Trait subscales were .81 and .79, respectively. Strong correlations between the State Hopelessness Subscale and Patient Health Questionnaire-9 (r = 0.77, P < .001), State Hope Scale (r = -0.75, P < .001), EQ-5D-5L (r = 0.59, P < .005), and PROMIS-29 domains of depression (P = .72, P < .001), fatigue (P = .61, P < .001), and social roles (P = .45, P = .047) were found. There were strong correlations between the Trait Hopelessness Subscale and Trait Hope Scale (r = -0.58, P < .005), State Hope Scale (r = -0.49, P = .03), and PROMIS-29 fatigue domain (r = 0.54, P = .015).

CONCLUSIONS

Findings support the reliability and validity of the STHS for evaluation of hopelessness in patients with heart disease.

Using an erythrocyte fatty acid fingerprint to predict risk of all-cause mortality: the Framingham Offspring Cohort

BACKGROUND

RBC long-chain omega-3 (n-3) fatty acid (FA) percentages (of total fatty acids) are associated with lower risk for total mortality, but it is unknown if a suite of FAs could improve risk prediction.

OBJECTIVES

The objective of this study was to compare a combination of RBC FA levels with standard risk factors for cardiovascular disease (CVD) in predicting risk of all-cause mortality.

METHODS

Framingham Offspring Cohort participants without prevalent CVD having RBC FA measurements and relevant baseline clinical covariates (n = 2240) were evaluated during 11 y of follow-up. A forward, stepwise approach was used to systematically evaluate the association of 8 standard risk factors (age, sex, total cholesterol, HDL cholesterol, hypertension treatment, systolic blood pressure, smoking status, and prevalent diabetes) and 28 FA metrics with all-cause mortality. A 10-fold cross-validation process was used to build and validate models adjusted for age and sex.

RESULTS

Four of 28 FA metrics [14:0, 16:1n-7, 22:0, and omega-3 index (O3I; 20:5n-3 + 22:6n-3)] appeared in ≥5 of the discovery models as significant predictors of all-cause mortality. In age- and sex-adjusted models, a model with 4 FA metrics was at least as good at predicting all-cause mortality as a model including the remaining 6 standard risk factors (C-statistic: 0.778; 95% CI: 0.759, 0.797; compared with C-statistic: 0.777; 95% CI: 0.753, 0.802). A model with 4 FA metrics plus smoking and diabetes (FA + Sm + D) had a higher C-statistic (0.790; 95% CI: 0.770, 0.811) compared with the FA (P < 0.01) or Sm + D models alone (C-statistic: 0.766; 95% CI: 0.739, 0.794; P < 0.001). A variety of other highly correlated FAs could be substituted for 14:0, 16:1n-7, 22:0, or O3I with similar predicted outcomes.

CONCLUSIONS

In this community-based population in their mid-60s, RBC FA patterns were as predictive of risk for death during the next 11 y as standard risk factors. Replication is needed in other cohorts to validate this FA fingerprint as a predictor of all-cause mortality.

Effects of menopausal hormone therapy on erythrocyte n-3 and n-6 PUFA concentrations in the Women's Health Initiative randomized trial

BACKGROUND

The factors other than dietary intake that determine tissue concentrations of EPA and DHA remain obscure. Prior studies suggested that, in women, endogenous estrogen may accelerate synthesis of DHA from ɑ-linolenic acid (ALA), but the effects of exogenous estrogen on RBC n-3 (ɷ-3) PUFA concentrations are unknown.

OBJECTIVE

We tested the hypothesis that menopausal hormone therapy (HT) would increase RBC n-3 PUFA concentrations.

METHODS

Postmenopausal women (ages 50-79 y) were assigned to HT or placebo in the Women's Health Initiative (WHI) randomized trial. The present analyses included a subset of 1170 women (ages 65-79 y) who had RBC PUFA concentrations measured at baseline and at 1 y as participants in the WHI Memory Study. HT included conjugated equine estrogens (E) alone for women without a uterus (n = 560) and E plus medroxyprogesterone acetate (P) for those with an intact uterus (n = 610). RBC n-3 and n-6 (ɷ-6) PUFAs were quantified.

RESULTS

Effects of E alone and E+P on PUFA profiles were similar and were thus combined in the analyses. Relative to the changes in the placebo group after 1 y of HT, docosapentaenoic acid (DPA; n-3) concentrations decreased by 10% (95% CI: 7.3%, 12.5%), whereas DHA increased by 11% (95% CI: 7.4%, 13.9%) in the HT group. Like DHA, DPA n-6 increased by 13% from baseline (95% CI: 10.0%, 20.3%), whereas linoleic acid decreased by 2.0% (95% CI: 1.0%, 4.1%; P values at least <0.01 for all). EPA and arachidonic acid concentrations were unchanged.

CONCLUSIONS

HT increased RBC concentrations of the terminal n-3 and n-6 PUFAs (DHA and DPA n-6). These findings are consistent with an estrogen-induced increase in DHA and DPA n-6 synthesis, which is consistent with an upregulation of fatty acid elongases and/or desaturases in the PUFA synthetic pathway. The clinical implications of these changes require further study. The Women's Health Initiative Memory Study is registered at clinicaltrials.gov as NCT00685009. Note that the data presented here were not planned as part of the original trial, and therefore are to be considered exploratory.

Blood omega-3 fatty acids and death from COVID-19: A pilot study

Very-long chain omega-3 fatty acids (EPA and DHA) have anti-inflammatory properties that may help reduce morbidity and mortality from COVID-19 infection. We conducted a pilot study in 100 patients to test the hypothesis that RBC EPA+DHA levels (the Omega-3 Index, O3I) would be inversely associated with risk for death by analyzing the O3I in banked blood samples drawn at hospital admission. Fourteen patients died, one of 25 in quartile 4 (Q4) (O3I ≥5.7%) and 13 of 75 in Q1-3. After adjusting for age and sex, the odds ratio for death in patients with an O3I in Q4 vs Q1-3 was 0.25, p = 0.07. Although not meeting the classical criteria for statistical significance, this strong trend suggests that a relationship may indeed exist, but more well-powered studies are clearly needed.

Association of the Omega-3 Index with Incident Prostate Cancer with Updated Meta-Analysis: The Cooper Center Longitudinal Study

Background: The association between long-chain omega-3 polyunsaturated fatty acids (n-3 PUFA) and prostate cancer (PC) remains unclear. Methods: We compared incident PC rates as a function of the Omega-3 Index [O3I, erythrocyte eicosapentaenoic and docosahexaenoic acids (EPA + DHA)] in 5607 men (40–80 years of age) seen at the Cooper Clinic who were free of PC at baseline. The average follow-up was 5.1 ± 2.8 years until censoring or reporting a new PC diagnosis. Proportional hazards regression was used to model the linear association between baseline O3I and the age-adjusted time to diagnosis. A meta-analysis of n-3 PUFA biomarker-based studies and incident PC was updated with the present findings. Results: A total of 116 cases of incident PC were identified. When O3I was examined as a continuous variable, the age-adjusted hazard ratio (HR) (95% CI) was 0.98 (0.89, 1.07; p = 0.25) for each 1% increment in the O3I. The updated meta-analysis with 10 biomarker-based studies found no significant relationship between EPA or DHA levels and risk for PC. Conclusions: We find no evidence in this study nor in a meta-analysis of similar studies that consuming n-3 PUFA-rich fish or using fish oil supplements affects the risk of PC.

Heart up! RCT protocol to increase physical activity in cardiac patients who report hopelessness: Amended for the COVID-19 pandemic

Hopelessness is associated with decreased physical activity (PA) and increased adverse events and death in patients with ischemic heart disease (IHD). Rates of PA in patients with IHD continue to be low in both hospital-based cardiac rehabilitation and home settings. While researchers have investigated strategies to increase PA among patients with IHD, interventions to promote PA specifically in IHD patients who report hopelessness are lacking. We describe the protocol for a NIH-funded randomized controlled trial designed to establish the effectiveness of a 6-week intervention (Heart Up!) to promote increased PA in IHD patients who report hopelessness. Participants (n = 225) are randomized to one of three groups: (1) motivational social support (MSS) from a nurse, (2) MSS from a nurse plus significant other support (SOS), or (3) attention control. Aims are to: (1) test the effectiveness of 6 weeks of MSS and MSS with SOS on increasing mean minutes per day of moderate to vigorous PA; (2) determine the effects of change in moderate to vigorous PA on hopelessness; and (3) determine if perceived social support and motivation (exercise self-regulation) mediate the effects of the intervention on PA. A total of 69 participants have been enrolled to date. The protocol has been consistently and accurately used by research personnel. We address the protocol challenges presented by the COVID-19 pandemic and steps taken to maintain fidelity to the intervention. Findings from this study could transform care for IHD patients who report hopelessness by promoting self-management of important PA goals that can contribute to better health outcomes.

Lack of perceived social support in patients with ischemic heart disease is associated with hopelessness

OBJECTIVE

To evaluate perceived social support (PSS) in ischemic heart disease (IHD) patients who report hopelessness.

METHODS

Using a cross-sectional design, 156 patients were screened during their hospitalization for moderate to severe state hopelessness. Twenty patients who reported hopelessness during hospitalization and maintained hopelessness one week after hospital discharge were included.

RESULTS

A moderately strong negative correlation was identified between PSS and state hopelessness (r = -0.54, p = .014). PSS was significantly higher in married/partnered patients (26.7 ± 4.85) compared to unmarried/unpartnered patients (18 ± 9.18; t = 2.45, p = .035).

CONCLUSIONS

Social support may help reduce hopelessness in vulnerable cardiac patients, especially those who are unpartnered.

Epidemiology of chronic pain in Ukraine: Findings from the World Mental Health Survey

Chronic pain can pose a serious challenge in everyday life for many individuals globally, especially in developing countries, but studies explicitly exploring risk factors of chronic pain beyond demographic characteristics using survey data have been scarce. To address this problem, this study analyzed World Health Organization data on chronic pain in Ukraine to explore demographic, psychological, and treatment perception-related risk factors to chronic pain. We replicated previous reports of older age, female sex, married status, inadequate financial resources, and comorbidity of other physical conditions as significant demographic risk factors for chronic pain diagnosis but not necessarily for severe pain. We also found evidence for psychological risk factors and treatment perceptions as significant predictors for chronic pain diagnosis and its severity. These results provide a first step in examining beyond demographic risk factors for chronic pain diagnosis and severity and, instead, assessing potential psychological risk factors.

Predicting the effects of supplemental EPA and DHA on the omega-3 index

BACKGROUND

Supplemental long-chain omega-3 (n-3) fatty acids (EPA and DHA) raise erythrocyte EPA + DHA [omega-3 index (O3I)] concentrations, but the magnitude or variability of this effect is unclear.

OBJECTIVE

The purpose of this study was to model the effects of supplemental EPA + DHA on the O3I.

METHODS

Deidentified data from 1422 individuals from 14 published n-3 intervention trials were included. Variables considered included dose, baseline O3I, sex, age, weight, height, chemical form [ethyl ester (EE) compared with triglyceride (TG)], and duration of treatment. The O3I was measured by the same method in all included studies. Variables were selected by stepwise regression using the Bayesian information criterion.

RESULTS

Individuals supplemented with EPA + DHA (n = 846) took a mean ± SD of 1983 ± 1297 mg/d, and the placebo controls (n = 576) took none. The mean duration of supplementation was 13.6 ± 6.0 wk. The O3I increased from 4.9% ± 1.7% to 8.1% ± 2.7% in the supplemented individuals ( P < 0.0001). The final model included dose, baseline O3I, and chemical formulation type (EE or TG), and these explained 62% of the variance in response (P < 0.0001). The model predicted that the final O3I (and 95% CI) for a population like this, with a baseline concentration of 4.9%, given 850 mg/d of EPA + DHA EE would be ∼6.5% (95% CI: 6.3%, 6.7%). Gram for gram, TG-based supplements increased the O3I by about 1 percentage point more than EE products.

CONCLUSIONS

Of the factors tested, only baseline O3I, dose, and chemical formulation were significant predictors of O3I response to supplementation. The model developed here can be used by researchers to help estimate the O3I response to a given EPA + DHA dose and chemical form.

Survey of the erythrocyte EPA+DHA levels in the heart attack/stroke belt

BACKGROUND

The Omega-3 Index (O3I; erythrocyte EPA+DHA as a percent of total fatty acids) is inversely related to risk for cardiovascular disease (CVD). The cardioprotective target O3I is 8%-12%. O3I levels in American regions with high CVD risk are poorly characterized.

PURPOSE

To determine the O3I in individuals participating in a Seafood Nutrition Partnership (SNP) survey in seven US cities in the CVD "belt."

METHODS

Fingerstick blood samples were analyzed for the O3I.

RESULTS

The SNP cohort (n = 2177) had a mean (SD) O3I of 4.42% (1.12%). Only 1.2% were in the desirable range, whereas 42% had an undesirable (<4%) O3I. The mean (SD) O3I in a subset of 772 SNP subjects who were matched for age and sex with the Framingham study was 4.6% (1.2%) compared 5.3% (1.6%) in the Framingham cohort (p < 0.0001).

CONCLUSIONS

Individuals in the CVD "belt" had relatively low O3I levels. Since in other settings, a low O3I is associated with increased risk for CVD, this may be one factor contributing to the higher risk for CVD in this region of the US.

Association of reported fish intake and supplementation status with the omega-3 index

BACKGROUND

An Omega-3 Index (O3I; EPA+DHA as a % of erythrocyte total fatty acids) in the desirable range (8%-12%) has been associated with improved heart and brain health.

OBJECTIVE

To determine the combination of fish intake and supplement use that is associated with an O3I of >8%.

DESIGN

Two cross-sectional studies comparing the O3I to EPA+DHA/fish intake.

PARTICIPANTS/SETTING

The first study included 28 individuals and assessed their fish and EPA+DHA intake using both a validated triple-pass 24-hr recall dietary survey and a single fish-intake question. The second study used de-identified data from 3,458 adults (84% from US) who self-tested their O3I and answered questions about their fish intake and supplement use.

STATISTICAL ANALYSES PERFORMED

Study 1, chi-squared, one-way ANOVA, and Pearson correlations were computed. In Study 2, multi-variable regression models were used to predict O3I levels from reported fish/supplement intakes.

RESULTS

The mean ± SD O3I was 4.87 ± 1.32%, and 5.99 ± 2.29% in the first and second studies, respectively. Both studies showed that for every increase in fish intake category the O3I increased by 0.50-0.65% (p < 0.0001). In the second study, about half of the population was taking omega-3 supplements, 32% reported no fish intake and 17% reported eating fish >2 times per week. Taking an EPA+DHA supplement increased the O3I by 2.2% (p < 0.0001). The odds of having an O3I of ≥8% were 44% in the highest intake group (≥3 servings/week and supplementation) and 2% in the lowest intake group (no fish intake or supplementation); and in those consuming 2 fish meals per week but not taking supplements (as per recommendations), 10%.

CONCLUSIONS

Current AHA recommendations are unlikely to produce a desirable O3I. Consuming at least 3 fish servings per week plus taking an EPA+DHA supplement markedly increases the likelihood of achieving this target level.

Ethnic minority members may be at risk for state hopelessness following hospitalization for ischemic heart disease

OBJECTIVE

To examine differences in state and trait hopelessness between ethnic minority and White patients hospitalized with ischemic heart disease (IHD).

METHODS

A descriptive cross-sectional design was used to enroll 517 patients at one Midwestern U.S. hospital. The State-Trait Hopelessness Scale measured hopelessness.

RESULTS

State hopelessness was higher in ethnic minority patients compared to Whites. Ethnic minority patients who had never been married had higher state hopelessness than those who were married or separated/divorced. There were no differences in trait hopelessness.

CONCLUSIONS

Ethnic minority patients with IHD, who have never been married, may be at higher risk for state hopelessness.

Erythrocyte n-6 Fatty Acids and Risk for Cardiovascular Outcomes and Total Mortality in the Framingham Heart Study

Background: The prognostic value of erythrocyte levels of n-6 fatty acids (FAs) for total mortality and cardiovascular disease (CVD) outcomes remains an open question. Methods: We examined cardiovascular (CV) outcomes and death in 2500 individuals in the Framingham Heart Study Offspring cohort without prevalent CVD (mean age 66 years, 57% women) as a function of baseline levels of different length n-6 FAs (18 carbon, 20 carbon, and 22 carbon) in the erythrocyte membranes. Clinical outcomes were monitored for up to 9.5 years (median follow up, 7.26 years). Cox proportional hazards models were adjusted for a variety of demographic characteristics, clinical status, and red blood cell (RBC) n-6 and long chain n-3 FA content. Results: There were 245 CV events, 119 coronary heart disease (CHD) events, 105 ischemic strokes, 58 CVD deaths, and 350 deaths from all causes. Few associations between either mortality or CVD outcomes were observed for n-6 FAs, with those that were observed becoming non-significant after adjusting for n-3 FA levels. Conclusions: Higher circulating levels of marine n-3 FA levels are associated with reduced risk for incident CVD and ischemic stroke and for death from CHD and all-causes; however, in the same sample little evidence exists for association with n-6 FAs. Further work is needed to identify a full profile of FAs associated with cardiovascular risk and mortality.

Epigenome wide association study of SNP-CpG interactions on changes in triglyceride levels after pharmaceutical intervention: a GAW20 analysis

In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphate-guanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes. In our analysis, we used both a genome-wide and candidate gene approach to investigate potential single-nucleotide polymorphism (SNP)–CpG interactions on changes in triglyceride levels. Although we were able to identify numerous loci of interest when using an exploratory significance threshold, we did not identify any significant interactions using a strict genome-wide significance threshold. We were also able to identify numerous loci using the candidate gene approach, in which we focused on 18 genes with prior evidence of association of triglyceride levels. In particular, we identified GALNT2 loci as containing potential CpG sites that moderate the impact of genetic polymorphisms on triglyceride levels. Further work is needed to provide clear guidance on analytic strategies for testing SNP–CpG interactions, although leveraging prior biological understanding may be needed to improve statistical power in data sets with smaller sample sizes.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

BACKGROUND

The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

RESULTS

Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.

CONCLUSIONS

A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

GAW20: methods and strategies for the new frontiers of epigenetics and pharmacogenomics

GAW20 provided a platform for developing and evaluating statistical methods to analyze human lipid-related phenotypes, DNA methylation, and single-nucleotide markers in a study involving a pharmaceutical intervention. In this article, we present an overview of the data sets and the contributions analyzing these data. The data, donated by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) investigators, included data from 188 families (N = 1105) which included genome-wide DNA methylation data before and after a 3-week treatment with fenofibrate, single-nucleotide polymorphisms, metabolic syndrome components before and after treatment, and a variety of covariates. The contributions from individual research groups were extensively discussed prior, during, and after the Workshop in groups based on discussion themes, before being submitted for publication.

Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20

Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum p value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set.

Common Pitfalls in Analysis of Tissue Scores

Histopathology remains an important source of descriptive biological data in biomedical research. Recent petitions for enhanced reproducibility in scientific studies have elevated the role of tissue scoring (semiquantitative and quantitative) in research studies. Effective tissue scoring requires appropriate statistical analysis to help validate the group comparisons and give the pathologist confidence in interpreting the data. Each statistical test is typically founded on underlying assumptions regarding the data. If the underlying assumptions of a statistical test do not match the data, then these tests can lead to increased risk of erroneous interpretations of the data. The choice of appropriate statistical test is influenced by the study's experimental design and resultant data (eg, paired vs unpaired, normality, number of groups, etc). Here, we identify 3 common pitfalls in the analysis of tissue scores: shopping for significance, overuse of paired t-tests, and misguided analysis of multiple groups. Finally, we encourage pathologists to use the full breadth of resources available to them, such as using statistical software, reading key publications about statistical approaches, and identifying a statistician to serve as a collaborator on the multidisciplinary research team. These collective resources can be helpful in choosing the appropriate statistical test for tissue-scoring data to provide the most valid interpretation for the pathologist.

Transcriptome assembly and annotation of johnsongrass (Sorghum halepense) rhizomes identify candidate rhizome-specific genes

Rhizomes facilitate the wintering and vegetative propagation of many perennial grasses. Sorghum halepense (johnsongrass) is an aggressive perennial grass that relies on a robust rhizome system to persist through winters and reproduce asexually from its rootstock nodes. This study aimed to sequence and assemble expressed transcripts within the johnsongrass rhizome. A de novo transcriptome assembly was generated from a single johnsongrass rhizome meristem tissue sample. A total of 141,176 probable protein-coding sequences from the assembly were identified and assigned gene ontology terms using Blast2GO. Estimated expression analysis and BLAST results were used to reduce the assembly to 64,447 high-confidence sequences. The johnsongrass assembly was compared to Sorghum bicolor, a related nonrhizomatous species, along with an assembly of similar rhizome tissue from the perennial grain crop Thinopyrum intermedium. The presence/absence analysis yielded a set of 98 expressed johnsongrass contigs that are likely associated with rhizome development.

A genome-wide association study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham Heart Study Offspring Cohort

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2 (Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis.

Erythrocyte long-chain omega-3 fatty acid levels are inversely associated with mortality and with incident cardiovascular disease: The Framingham Heart Study

BACKGROUND:

The extent to which omega-3 fatty acid status is related to risk for death from any cause and for incident cardiovascular disease (CVD) remains controversial.

OBJECTIVE:

To examine these associations in the Framingham Heart Study.

DESIGN:

Prospective and observational.

SETTING:

Framingham Heart Study Offspring cohort.

MEASUREMENTS:

The exposure marker was red blood cell levels of eicosapentaenoic and docosahexaenoic acids (the Omega-3 Index) measured at baseline. Outcomes included mortality (total, CVD, cancer, and other) and total CVD events in participants free of CVD at baseline. Follow-up was for a median of 7.3 years. Cox proportional hazards models were adjusted for 18 variables (demographic, clinical status, therapeutic, and CVD risk factors).

RESULTS:

Among the 2500 participants (mean age 66 years, 54% women), there were 350 deaths (58 from CVD, 146 from cancer, 128 from other known causes, and 18 from unknown causes). There were 245 CVD events. In multivariable-adjusted analyses, a higher Omega-3 Index was associated with significantly lower risks (P-values for trends across quintiles) for total mortality (P = .02), for non-CVD and non-cancer mortality (P = .009), and for total CVD events (P = .008). Those in the highest (>6.8%) compared to those in the lowest Omega-3 Index quintiles (<4.2%) had a 34% lower risk for death from any cause and 39% lower risk for incident CVD. These associations were generally stronger for docosahexaenoic acid than for eicosapentaenoic acid. When total cholesterol was compared with the Omega-3 Index in the same models, the latter was significantly related with these outcomes, but the former was not.

LIMITATIONS:

Relatively short follow-up time and one-time exposure assessment.

CONCLUSIONS:

A higher Omega-3 Index was associated with reduced risk of both CVD and allcause mortality.

The Associations between Self-Reported Exposure to the Chernobyl Nuclear Disaster Zone and Mental Health Disorders in Ukraine

BACKGROUND

In 1986, Reactor 4 of the Chernobyl nuclear power plant near Pripyat, Ukraine exploded, releasing highly-radioactive materials into the surrounding environment. Although the physical effects of the disaster have been well-documented, a limited amount of research has been conducted on association of the disaster with long-term, clinically-diagnosable mental health disorders. According to the diathesis-stress model, the stress of potential and unknown exposure to radioactive materials and the ensuing changes to ones life or environment due to the disaster might lead those with previous vulnerabilities to fall into a poor state of mental health. Previous studies of this disaster have found elevated symptoms of stress, substance abuse, anxiety, and depression in exposed populations, though often at a subclinical level.

MATERIALS AND METHODS

With data from The World Mental Health Composite International Diagnostic Interview, a cross-sectional large mental health survey conducted in Ukraine by the World Health Organization, the mental health of Ukrainians was modeled with multivariable logistic regression techniques to determine if any long-term mental health disorders were association with reporting having lived in the zone affected by the Chernobyl nuclear disaster. Common classes of psychiatric disorders were examined as well as self-report ratings of physical and mental health.

RESULTS

Reporting that one lived in the Chernobyl-affected disaster zone was associated with a higher rate of alcohol disorders among men and higher rates of intermittent explosive disorders among women in a prevalence model. Subjects who lived in the disaster zone also had lower ratings of personal physical and mental health when compared to controls.

DISCUSSION

Stress resulting from disaster exposure, whether or not such exposure actually occurred or was merely feared, and ensuing changes in life circumstances is associated with increased rates of mental health disorders. Professionals assisting populations that are coping with the consequences of disaster should be aware of possible increases in psychiatric disorders as well as poorer perceptions regarding personal physical and mental health.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Home- and Hospital-Based Cardiac Rehabilitation Exercise: The Important Role of Physician Recommendation

Exercise reduces morbidity and mortality for patients with heart disease. Despite clear guidelines and known benefits, most cardiac patients do not meet current exercise recommendations. Physician endorsement positively affects patient participation in hospital-based Phase II cardiac rehabilitation programs, yet the importance of physician recommendation for home-based cardiac rehabilitation exercise is unknown. A prospective observational design was used to examine predictors of both home-based and Phase II rehabilitation exercise in a sample of 251 patients with coronary heart disease. Regression analyses were done to examine demographic and clinical characteristics, physical functioning, and patient's report of physician recommendation for exercise. Patients with a strong physician referral, who were married and older, were more likely to participate in Phase II exercise. Increased strength of physician recommendation was the unique predictor of home-based exercise. Further research is needed to examine how health professionals can motivate cardiac patients to exercise in home and outpatient settings.

Cautions about the reliability of pairwise gene correlations based on expression data

BACKGROUND

Rapid growth in the availability of genome-wide transcript abundance levels through gene expression microarrays and RNAseq promises to provide deep biological insights into the complex, genome-wide transcriptional behavior of single-celled organisms. However, this promise has not yet been fully realized.

RESULTS

We find that computation of pairwise gene associations (correlation; mutual information) across a set of 2782 total genome-wide expression samples from six diverse bacteria produces unexpectedly large variation in estimates of pairwise gene association-regardless of the metric used, the organism under study, or the number and source of the samples. We pinpoint the cause to sampling bias. In particular, in repositories of expression data (e.g., Gene Expression Omnibus, GEO), many individual genes show small differences in absolute gene expression levels across the set of samples. We demonstrate that these small differences are due mainly to "noise" instead of "signal" attributable to environmental or genetic perturbations. We show that downstream analysis using gene expression levels of genes with small differences yields biased estimates of pairwise association.

CONCLUSIONS

We propose flagging genes with small differences in absolute, RMA-normalized, expression levels (e.g., standard deviation less than 0.5), as potentially yielding biased pairwise association metrics. This strategy has the potential to substantially improve the confidence in genome-wide conclusions about transcriptional behavior in bacterial organisms. Further work is needed to further refine strategies to identify genes with small difference in expression levels prior to computing gene-gene association metrics.

A genome-wide association study of saturated, mono- and polyunsaturated red blood cell fatty acids in the Framingham Heart Offspring Study

Most genome-wide association studies have explored relationships between genetic variants and plasma phospholipid fatty acid proportions, but few have examined apparent genetic influences on the membrane fatty acid profile of red blood cells (RBC). Using RBC fatty acid data from the Framingham Offspring Study, we analyzed over 2.5 million single nucleotide polymorphisms (SNPs) for association with 14 RBC fatty acids identifying 191 different SNPs associated with at least 1 fatty acid. Significant associations (p<1×10(-8)) were located within five distinct 1MB regions. Of particular interest were novel associations between (1) arachidonic acid and PCOLCE2 (regulates apoA-I maturation and modulates apoA-I levels), and (2) oleic and linoleic acid and LPCAT3 (mediates the transfer of fatty acids between glycerolipids). We also replicated previously identified strong associations between SNPs in the FADS (chromosome 11) and ELOVL (chromosome 6) regions. Multiple SNPs explained 8-14% of the variation in 3 high abundance (>11%) fatty acids, but only 1-3% in 4 low abundance (<3%) fatty acids, with the notable exception of dihomo-gamma linolenic acid with 53% of variance explained by SNPs. Further studies are needed to determine the extent to which variations in these genes influence tissue fatty acid content and pathways modulated by fatty acids.

Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond

When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide polymorphisms, NGS data, and imputed data are generally concordant but that errors are particularly likely at rare variants, for homozygous genotypes, within regions with repeated sequences or structural variants, and within sequence data imputed from unrelated individuals. Admixture complicated identification of cryptic relatedness, but information from Mendelian transmission improved error detection and provided an estimate of the de novo mutation rate. Computationally, fast rule-based imputation was accurate but could not cover as many loci or subjects as more computationally demanding probability-based methods. Incorporating population-level data into pedigree-based imputation methods improved results. Observed data outperformed imputed data in association testing, but imputed data were also useful. We discuss the strengths and weaknesses of existing methods and suggest possible future directions, such as improving communication between data collectors and data analysts, establishing thresholds for and improving imputation quality, and incorporating error into imputation and analytical models.

Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data

Genotype errors are well known to increase type I errors and/or decrease power in related tests of genotype-phenotype association, depending on whether the genotype error mechanism is associated with the phenotype. These relationships hold for both single and multimarker tests of genotype-phenotype association. To assess the potential for genotype errors in Genetic Analysis Workshop 18 (GAW18) data, where no gold standard genotype calls are available, we explored concordance rates between sequencing, imputation, and microarray genotype calls. Our analysis shows that missing data rates for sequenced individuals are high and that there is a modest amount of called genotype discordance between the 2 platforms, with discordance most common for lower minor allele frequency (MAF) single-nucleotide polymorphisms (SNPs). Some evidence for discordance rates that were different between phenotypes was observed, and we identified a number of cases where different technologies identified different bases at the variant site. Type I errors and power loss is possible as a result of missing genotypes and errors in called genotypes in downstream analysis of GAW18 data.

Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants

Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p <1 × 10⁻⁴) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.

Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees

Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.

Application of family-based tests of association for rare variants to pathways

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard "gene-based" test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (>200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches.

The State–Trait Hopelessness Scale

Hopelessness is predictive in the development of coronary heart disease (CHD) and can persist in patients after a CHD event, adversely affecting recovery. Hopelessness may represent a temporary response (state) or a chronic outlook (trait). Common hopelessness measures fail to differentiate state from trait hopelessness, a potentially important differentiation for treatment. The State–Trait Hopelessness Scale (STHS) was developed and pilot tested with two groups of college students ( n = 39 and 190) and patients with CHD ( n = 44). The instrument was then used with 520 patients, confirming reliability (Cronbach’s α) for the State (.88) and Trait (.91) subscales and concurrent and predictive validity. Separate exploratory factor analyses showed two factors (hopelessness present or hopelessness absent) for the State and Trait subscales, accounting for 58.9% and 57.3% of variance, respectively. These findings support future use of the tool in clinical settings and in intervention studies focused on hopelessness.

Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the "noise" from 6-12 non-causal SNPs will cancel out the "signal" of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

A geometric framework for evaluating rare variant tests of association

The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

Background

Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed.

Results

We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size.

Conclusions

Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.

Evaluating methods for combining rare variant data in pathway-based tests of genetic association

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or Fisher’s combined probability method). In the second approach, rare variant methods (e.g., CMC and WS) are applied directly to sets of single-nucleotide polymorphisms (SNPs) representing all SNPs within genes in a pathway. In this paper we use simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17 to analyze sets of rare variants using these two competing approaches. The initial results suggest substantial differences in the methods, with Fisher’s combined probability method and the direct application of the WS method yielding the best power. Evidence suggests that the WS method works well in most situations, although Fisher’s method was more likely to be optimal when the number of causal SNPs in the set was low but the risk of the causal SNPs was high.

Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16

Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SNP data, two popular statistics from gene expression data analysis (gene set enrichment analysis [GSEA] and Fisher's exact test [FET]) have been used. However, GSEA and FET have shown a lack of power and robustness in the analysis of gene expression data. The purpose of this work is to investigate whether the same issues are also true for the analysis of SNP data. Ultimately, we conclude that GSEA and FET are not optimal for the analysis of SNP data when compared with the SUMSTAT method. In analysis of real SNP data from the Framingham Heart Study, we find that SUMSTAT finds many more gene sets to be significant when compared with other methods. In an analysis of simulated data, SUMSTAT demonstrates high power and better control of the type I error rate. GSA is a promising approach to the analysis of SNP data in GWAS and use of the SUMSTAT statistic instead of GSEA or FET may increase power and robustness.