Role of Polyunsaturated Fat in Modifying Cardiovascular Risk Associated With Family History of Cardiovascular Disease: Pooled De Novo Results From 15 Observational Studies.

BACKGROUND: It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium. METHODS: Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction. RESULTS: After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions. CONCLUSIONS: A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD.

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.

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.

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.

Estimation and predictors of the Omega-3 Index in the UK Biobank.

Information on the Omega-3 Index (O3I) in the United Kingdom (UK) is scarce. The UK-Biobank (UKBB) contains data on total plasma n3-PUFA% and DHA% measured by NMR. The aim of our study was to create an equation to estimate the O3I (eO3I) from these data. We first performed an inter-laboratory experiment with 250 random blood samples in which the O3I was measured in erythrocytes by GC, and total n3 % and DHA% were measured in plasma by NMR. The best predictor of eO3I included both DHA% and a derived metric, the total n3 %-DHA%. Together these explained 65 % of the variability (r = 0·832, P < 0·0001). We then estimated the O3I in 117 108 UKBB subjects and correlated it with demographic and lifestyle variables in multivariable-adjusted models. The mean eO3I was 5·58 % (sd 2·35 %) in this UKBB cohort. Several predictors were significantly correlated with eO3I (all P < 0·0001). In general order of impact and with directionality (-, inverse and +, direct): oily-fish consumption (+), fish oil supplement use (+), female sex (+), older age (+), alcohol use (+), smoking (-), higher waist circumference and BMI (-), lower socioeconomic status and less education (-). Only 20·5 % of eO3I variability could be explained by predictors investigated, and oily fish consumption accounted for 7·0 % of that. With the availability of the eO3I in the UKBB cohort, we will be in a position to link risk for a variety of diseases with this commonly used and well-documented marker of n3-PUFA biostatus.

Reliability and Validity of the State-Trait Hopelessness Scale in a Primarily Rural Population of Adults With Ischemic Heart Disease in the US Great Plains.

PURPOSE: Hopelessness and rurality are each independently associated with increased mortality in adults with ischemic heart disease (IHD), yet there is no known research examining hopelessness in rural patients with IHD. The authors of this study evaluated the reliability and validity of the State-Trait Hopelessness Scale (STHS) in a primarily rural population of adults with IHD living in West North Central United States (US Great Plains). METHODS: Reliability, concurrent validity, and convergent validity were evaluated for 115 adults hospitalized for IHD. Rural-Urban Commuting Area codes were used to stratify participants by rurality level, with 66% categorized as rural. Principal component analysis was used to examine potential factor structure of the STHS. FINDINGS: Cronbach α for the State and Trait Hopelessness subscales were 0.884 and 0.903, respectively. Concurrent validity was supported for the State and Trait subscales using the Patient Health Questionnaire-8 (State: r = 0.50, P < .001; Trait: r = 0.35, P < .001). Convergent validity was supported for the State subscale using the Duke Activity Status Index ( r = -0.23, P = .013). Principal component analysis showed 2 factors (hopelessness present and hopelessness absent) for the State and Trait subscales, accounting for 63% and 58% of variance, respectively. CONCLUSIONS: Findings support the reliability and validity of the STHS for evaluation of hopelessness in rural adults with IHD in clinical and research settings. Results replicated the same factor structure found in testing of the STHS in a primarily urban sample. Because of the prevalence of hopelessness in rural adults with IHD and association with increased mortality, hopelessness should be assessed during hospitalization and in the recovery period.

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.

Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality.

BACKGROUND: Global dietary recommendations for and cardiovascular effects of linoleic acid, the major dietary omega-6 fatty acid, and its major metabolite, arachidonic acid, remain controversial. To address this uncertainty and inform international recommendations, we evaluated how in vivo circulating and tissue levels of linoleic acid (LA) and arachidonic acid (AA) relate to incident cardiovascular disease (CVD) across multiple international studies. METHODS: We performed harmonized, de novo, individual-level analyses in a global consortium of 30 prospective observational studies from 13 countries. Multivariable-adjusted associations of circulating and adipose tissue LA and AA biomarkers with incident total CVD and subtypes (coronary heart disease, ischemic stroke, cardiovascular mortality) were investigated according to a prespecified analytic plan. Levels of LA and AA, measured as the percentage of total fatty acids, were evaluated linearly according to their interquintile range (ie, the range between the midpoint of the first and fifth quintiles), and categorically by quintiles. Study-specific results were pooled using inverse-variance-weighted meta-analysis. Heterogeneity was explored by age, sex, race, diabetes mellitus, statin use, aspirin use, omega-3 levels, and fatty acid desaturase 1 genotype (when available). RESULTS: In 30 prospective studies with medians of follow-up ranging 2.5 to 31.9 years, 15 198 incident cardiovascular events occurred among 68 659 participants. Higher levels of LA were significantly associated with lower risks of total CVD, cardiovascular mortality, and ischemic stroke, with hazard ratios per interquintile range of 0.93 (95% CI, 0.88-0.99), 0.78 (0.70-0.85), and 0.88 (0.79-0.98), respectively, and nonsignificantly with lower coronary heart disease risk (0.94; 0.88-1.00). Relationships were similar for LA evaluated across quintiles. AA levels were not associated with higher risk of cardiovascular outcomes; in a comparison of extreme quintiles, higher levels were associated with lower risk of total CVD (0.92; 0.86-0.99). No consistent heterogeneity by population subgroups was identified in the observed relationships. CONCLUSIONS: In pooled global analyses, higher in vivo circulating and tissue levels of LA and possibly AA were associated with lower risk of major cardiovascular events. These results support a favorable role for LA in CVD prevention.

Fatty acids in the de novo lipogenesis pathway and incidence of type 2 diabetes: A pooled analysis of prospective cohort studies.

BACKGROUND: De novo lipogenesis (DNL) is the primary metabolic pathway synthesizing fatty acids from carbohydrates, protein, or alcohol. Our aim was to examine associations of in vivo levels of selected fatty acids (16:0, 16:1n7, 18:0, 18:1n9) in DNL with incidence of type 2 diabetes (T2D). METHODS AND FINDINGS: Seventeen cohorts from 12 countries (7 from Europe, 7 from the United States, 1 from Australia, 1 from Taiwan; baseline years = 1970-1973 to 2006-2010) conducted harmonized individual-level analyses of associations of DNL-related fatty acids with incident T2D. In total, we evaluated 65,225 participants (mean ages = 52.3-75.5 years; % women = 20.4%-62.3% in 12 cohorts recruiting both sexes) and 15,383 incident cases of T2D over the 9-year follow-up on average. Cohort-specific association of each of 16:0, 16:1n7, 18:0, and 18:1n9 with incident T2D was estimated, adjusted for demographic factors, socioeconomic characteristics, alcohol, smoking, physical activity, dyslipidemia, hypertension, menopausal status, and adiposity. Cohort-specific associations were meta-analyzed with an inverse-variance-weighted approach. Each of the 4 fatty acids positively related to incident T2D. Relative risks (RRs) per cohort-specific range between midpoints of the top and bottom quintiles of fatty acid concentrations were 1.53 (1.41-1.66; p < 0.001) for 16:0, 1.40 (1.33-1.48; p < 0.001) for 16:1n-7, 1.14 (1.05-1.22; p = 0.001) for 18:0, and 1.16 (1.07-1.25; p < 0.001) for 18:1n9. Heterogeneity was seen across cohorts (I2 = 51.1%-73.1% for each fatty acid) but not explained by lipid fractions and global geographical regions. Further adjusted for triglycerides (and 16:0 when appropriate) to evaluate associations independent of overall DNL, the associations remained significant for 16:0, 16:1n7, and 18:0 but were attenuated for 18:1n9 (RR = 1.03, 95% confidence interval (CI) = 0.94-1.13). These findings had limitations in potential reverse causation and residual confounding by imprecisely measured or unmeasured factors. CONCLUSIONS: Concentrations of fatty acids in the DNL were positively associated with T2D incidence. Our findings support further work to investigate a possible role of DNL and individual fatty acids in the development of T2D.

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.

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.

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.

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.

Fatty acid biomarkers of dairy fat consumption and incidence of type 2 diabetes: A pooled analysis of prospective cohort studies.

BACKGROUND: We aimed to investigate prospective associations of circulating or adipose tissue odd-chain fatty acids 15:0 and 17:0 and trans-palmitoleic acid, t16:1n-7, as potential biomarkers of dairy fat intake, with incident type 2 diabetes (T2D). METHODS AND FINDINGS: Sixteen prospective cohorts from 12 countries (7 from the United States, 7 from Europe, 1 from Australia, 1 from Taiwan) performed new harmonised individual-level analysis for the prospective associations according to a standardised plan. In total, 63,682 participants with a broad range of baseline ages and BMIs and 15,180 incident cases of T2D over the average of 9 years of follow-up were evaluated. Study-specific results were pooled using inverse-variance-weighted meta-analysis. Prespecified interactions by age, sex, BMI, and race/ethnicity were explored in each cohort and were meta-analysed. Potential heterogeneity by cohort-specific characteristics (regions, lipid compartments used for fatty acid assays) was assessed with metaregression. After adjustment for potential confounders, including measures of adiposity (BMI, waist circumference) and lipogenesis (levels of palmitate, triglycerides), higher levels of 15:0, 17:0, and t16:1n-7 were associated with lower incidence of T2D. In the most adjusted model, the hazard ratio (95% CI) for incident T2D per cohort-specific 10th to 90th percentile range of 15:0 was 0.80 (0.73-0.87); of 17:0, 0.65 (0.59-0.72); of t16:1n7, 0.82 (0.70-0.96); and of their sum, 0.71 (0.63-0.79). In exploratory analyses, similar associations for 15:0, 17:0, and the sum of all three fatty acids were present in both genders but stronger in women than in men (pinteraction < 0.001). Whereas studying associations with biomarkers has several advantages, as limitations, the biomarkers do not distinguish between different food sources of dairy fat (e.g., cheese, yogurt, milk), and residual confounding by unmeasured or imprecisely measured confounders may exist. CONCLUSIONS: In a large meta-analysis that pooled the findings from 16 prospective cohort studies, higher levels of 15:0, 17:0, and t16:1n-7 were associated with a lower risk of T2D.

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.

Dog Ownership and Dog Walking: The Relationship With Exercise, Depression, and Hopelessness in Patients With Ischemic Heart Disease.

BACKGROUND: Dog ownership has been associated with increased physical activity in the general adult population. OBJECTIVE: The objective of this study was to examine dog ownership and dog walking and their relationship with home-based and phase II cardiac rehabilitation exercise, depression, and hopelessness in patients with ischemic heart disease. METHODS: A total of 122 patients with ischemic heart disease were included in this prospective observational study. Patients completed dog ownership/walking questions during their hospitalization. The Cardiac Rehabilitation Exercise Participation Tool, Patient Health Questionnaire-9, and State-Trait Hopelessness Scale were completed by mail at 3, 8, or 12 months later. Regression modeling was used to evaluate the significance of dog ownership/walking on exercise, depression and hopelessness. RESULTS: The sample was 34.4% female and had a mean age of 64.7 ± 9.1 years. Forty-two patients (34.4%) reported owning a dog. Patients who owned but did not walk their dog reported significantly lower levels of home exercise compared with patients who walked their dogs at least 1 day per week (36.8% for non-dog walkers vs 73.9% for dog walkers, P = .019). The odds of participating in home exercise were significantly higher for dog walkers compared with non-dog walkers (odds ratio, 8.1 [1.7, 38.5] vs 1.0). There were no differences in phase II cardiac rehabilitation exercise, depression, or hopelessness between dog owners and non-dog owners or between dog walkers and non-dog walkers. CONCLUSIONS: These findings show a beneficial effect on home-based exercise for those who dog-walk at least 1 day per week. Healthcare professionals should encourage dog walking to increase dog owners' physical activity levels.

Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18.

Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.

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.

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.