The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports

Endothelial dysfunction and cardiovascular diseases: The role of human induced pluripotent stem cells and tissue engineering

Cardiovascular disease (CVD) remains to be the leading cause of death globally today and therefore the need for the development of novel therapies has become increasingly important in the cardiovascular field. The mechanism(s) behind the pathophysiology of CVD have been laboriously investigated in both stem cell and bioengineering laboratories. Scientific breakthroughs have paved the way to better mimic cell types of interest in recent years, with the ability to generate any cell type from reprogrammed human pluripotent stem cells. Mimicking the native extracellular matrix using both organic and inorganic biomaterials has allowed full organs to be recapitulated in vitro. In this paper, we will review techniques from both stem cell biology and bioengineering which have been fruitfully combined and have fueled advances in the cardiovascular disease field. We will provide a brief introduction to CVD, reviewing some of the recent studies as related to the role of endothelial cells and endothelial cell dysfunction. Recent advances and the techniques widely used in both bioengineering and stem cell biology will be discussed, providing a broad overview of the collaboration between these two fields and their overall impact on tissue engineering in the cardiovascular devices and implications for treatment of cardiovascular disease.

Digital Clock Drawing as an Alzheimer's Disease Susceptibility Biomarker: Associations with Genetic Risk Score and APOE in Older Adults

BACKGROUND

Alzheimer's disease (AD) is the leading cause of dementia in older adults, but most people are not diagnosed until significant neuronal loss has likely occurred along with a decline in cognition. Non-invasive and cost-effective digital biomarkers for AD have the potential to improve early detection.

OBJECTIVE

We examined the validity of DCTclockTM (a digitized clock drawing task) as an AD susceptibility biomarker.

DESIGN

We used two primary independent variables, Apolipoprotein E (APOE) ε4 allele carrier status and polygenic risk score (PRS). We examined APOE and PRS associations with DCTclockTM composite scores as dependent measures.

SETTING

We used existing data from the Framingham Heart Study (FHS), a community-based study with the largest dataset of digital clock drawing data to date.

PARTICIPANTS

The sample consisted of 2,398 older adults ages 60-94 with DCTclockTM data (mean age of 72.3, 55% female and 92% White).

MEASUREMENTS

PRS was calculated using 38 variants identified in a recent large genome-wide association study (GWAS) and meta-analysis of late-onset AD (LOAD).

RESULTS

Results showed that DCTclockTM performance decreased with advancing age, lower education, and the presence of one or more copies of APOE ε4. Lower DCTclockTM Total Score as well as lower composite scores for Information Processing Speed (both command and copy conditions) and Drawing Efficiency (command condition) were significantly associated with higher PRS levels and more copies of APOE ε4. APOE and PRS associations displayed similar effect sizes in both men and women.

CONCLUSIONS

Our results indicate that higher AD genetic risk is associated with poorer DCTclockTM performance in older adults without dementia. This is the first study to demonstrate significant differences in clock drawing performance on the basis of APOE status or PRS.

Bitter- and Umami-Related Genes are Differentially Associated with Food Group Intakes: the Framingham Heart Study

BACKGROUND

As suboptimal diet quality remains the leading modifiable contributor to chronic disease risk, it is important to better understand the individual-level drivers of food choices. Recently, a genetic component of food choices was proposed based on variants (SNPs) in genes related to taste perception (taste-related SNPs).

OBJECTIVES

This study aimed to determine the cumulative contribution of taste-related SNPs for basic tastes (bitter, sweet, umami, salt, and sour), summarized as "polygenic taste scores," to food group intakes among adults.

METHODS

Cross-sectional analyses were performed on 6230 Framingham Heart Study participants (mean age ± SD: 50 ± 14 y; 54% female). Polygenic taste scores were derived for tastes with ≥2 related SNPs identified in prior genome-wide association studies, and food group intakes (servings per week [sev/wk]) were tabulated from food frequency questionnaires. Associations were determined via linear mixed-effects models, using false discovery rates and bootstrap resampling to determine statistical significance.

RESULTS

Thirty-three taste-related SNPs (9 bitter, 19 sweet, 2 umami, 2 sour, 1 salt) were identified and used to derive polygenic taste scores for bitter, sweet, umami, and sour. Per additional allele for higher bitter perception, whole grain intakes were lower by 0.17 (95% CI: -0.28, -0.06) sev/wk, and for higher umami perception, total and red/orange vegetable intakes were lower by 0.73 (95% CI: -1.12, -0.34) and 0.25 (95% CI: -0.40, -0.10) sev/wk, respectively. Subsequent analyses at the SNP level identified four novel SNP-diet associations-two bitter-related SNPs with whole grains (rs10960174 and rs6782149) and one umami-related SNP with total and red/orange vegetables (rs7691456)-which may have been driving the identified associations.

CONCLUSIONS

Taste-related genes for bitter and umami were differentially associated with food choices that may impact diet quality. Hence, a benefit could be derived from leveraging knowledge of taste-related genes when developing personalized risk reduction dietary guidance.

Weak Association Between Genetic Markers of Hyperuricemia and Cardiorenal Outcomes: Insights From the STANISLAS Study Cohort With a 20-Year Follow-Up

Background Hyperuricemia is associated with poor cardiovascular outcomes, although it is uncertain whether this relationship is causal in nature. This study aimed to: (1) assess the heritability of serum uric acid (SUA) levels, (2) conduct a genome-wide association study on SUA levels, and (3) investigate the association between certain single-nucleotide polymorphisms and target organ damage. Methods and Results The STANISLAS (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux) study cohort is a single-center longitudinal cohort recruited between 1993 and 1995 (visit 1), with a last visit (visit 4 [V4]) performed ≈20 years apart. Serum lipid profile, SUA, urinary albumin/creatinine ratio, estimated glomerular filtration rate, 24-hour ambulatory blood pressure monitoring, transthoracic echocardiography, pulse wave velocity, and genotyping for each participant were assessed at V4. A total of 1573 participants were included at V4, among whom 1417 had available SUA data at visit 1. Genome-wide association study results highlighted multiple single-nucleotide polymorphisms on the SLC2A9 gene linked to SUA levels. Carriers of the most associated mutated SLC2A9 allele (rs16890979) had significantly lower SUA levels. Although SUA level at V4 was highly associated with diabetes, prediabetes, higher body mass index, CRP (C-reactive protein) levels, estimated glomerular filtration rate variation (visit 1-V4), carotid intima-media thickness, and pulse wave velocity, rs16890979 was only associated with higher carotid intima-media thickness. Conclusions Our findings demonstrate that rs16890979, a genetic determinant of SUA levels located on the SLC2A9 gene, is associated with carotid intima-media thickness despite significant associations between SUA levels and several clinical outcomes, thereby lending support to the hypothesis of a link between SUA and cardiovascular disease.

Interaction Effects of DRD2 Genetic Polymorphism and Interpersonal Stress on Problematic Gaming in College Students

Problematic gaming has become a public concern, influenced both by genetic factors and stressful environments. Studies have reported the effects of dopamine-related genes and interpersonal stressors on problematic gaming, but gene and environment interaction (G × E) studies have not been conducted. In this study, we investigated the interaction effects of dopamine receptor D2 (DRD2) polymorphisms and interpersonal stress on problematic gaming and the mediating effect of avoidant coping to reveal the mechanism of the G × E process. We recruited 168 college students (mean age = 22; male 63.1%) and genotyped their DRD2 C957T (rs6277) and Taq1 (rs1800497) polymorphisms. The results of the mediated moderation analysis showed that, when experiencing interpersonal stressors, individuals with both the C957T T allele and the Taq1 A1 allele showed more elevated problematic gaming scores than non-carriers. Moreover, the interaction effect of the combined DRD2 polymorphisms and interpersonal stress was significantly mediated by avoidant coping. These findings suggest that the influence of interpersonal stress on problematic gaming can be changed as a function of DRD2 genotypes, which may be because of the avoidant coping styles of C957T T allele and Taq1 A1 allele carriers in response to stress.

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models

Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposure, and the additional conditions required to causally interpret MR estimates as a point effect, a period effect or a lifetime effect depending on whether researchers have measurements at a single or multiple time points. We propose methods to incorporate time-varying exposures in MR analyses based on g-estimation of structural mean models, and demonstrate its application by estimating the period effect of alcohol intake, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol on intermediate coronary heart disease outcomes using data from the Framingham Heart Study. We use this data example to highlight the challenges of interpreting MR estimates as causal effects, and describe other extensions of structural mean models for more complex data scenarios.

A transcriptome-wide association study to detect novel genes for volumetric bone mineral density

Transcriptome-wide association studies (TWAS) systematically investigate the association of genetically predicted gene expression with disease risk, providing an effective approach to identify novel susceptibility genes. Osteoporosis is the most common metabolic bone disease, associated with reduced bone mineral density (BMD) and increased risk of osteoporotic fractures, whereas genetic factors explain approximately 70% of the variance in phenotypes associated with bone. BMD is commonly assessed using dual-energy X-ray absorptiometry (DXA) to obtain measurements (g/cm2) of areal BMD. However, quantitative computed tomography (QCT) measured 3D volumetric BMD (vBMD) (g/cm3) has important advantages compared with DXA since it can evaluate cortical and trabecular microstructural features of bone quality, which can be used to directly predict fracture risk. Here, we performed the first TWAS for volumetric BMD (vBMD) by integrating genome-wide association studies (GWAS) data from two independent cohorts, namely the Framingham Heart Study (FHS, n = 3298) and the Osteoporotic Fractures in Men (MrOS, n = 4641), with tissue-specific gene expression data from the Genotype-Tissue Expression (GTEx) project. We first used stratified linkage disequilibrium (LD) score regression approach to identify 12 vBMD-relevant tissues, for which vBMD heritability is enriched in tissue-specific genes of the given tissue. Focusing on these tissues, we subsequently leveraged GTEx expression reference panels to predict tissue-specific gene expression levels based on the genotype data from FHS and MrOS. The associations between predicted gene expression levels and vBMD variation were then tested by MultiXcan, an innovative TWAS method that integrates information available across multiple tissues. We identified 70 significant genes associated with vBMD, including some previously identified osteoporosis-related genes such as LYRM2 and NME8, as well as some novel loci such as DNAAF2 and SPAG16. Our findings provide novel insights into the pathophysiological mechanisms of osteoporosis and highlight several novel vBMD-associated genes that warrant further investigation.

SNP Development in Penaeus vannamei via Next-Generation Sequencing and DNA Pool Sequencing

Next-generation sequencing and pool sequencing have been widely used in SNP (single-nucleotide polymorphism) detection and population genetics research; however, there are few reports on SNPs related to the growth of Penaeus vannamei. The purpose of this study was to call SNPs from rapid-growing (RG) and slow-growing (SG) individuals’ transcriptomes and use DNA pool sequencing to assess the reliability of SNPs. Two parameters were applied to detect SNPs. One parameter was the p-values generated using Fisher’s exact test, which were used to calculate the significance of allele frequency differences between RG and SG. The other one was the AFI (minor allele frequency imbalance), which was defined to highlight the fold changes in MAF (minor allele frequency) values between RG and SG. There were 216,015 hypothetical SNPs, which were obtained based on the transcriptome data. Finally, 104 high-quality SNPs and 96,819 low-quality SNPs were predicted. Then, 18 high-quality SNPs and 17 low-quality SNPs were selected to assess the reliability of the detection process. Here, 72.22% (13/18) accuracy was achieved for high-quality SNPs, while only 52.94% (9/17) accuracy was achieved for low-quality SNPs. These SNPs enrich the data for population genetics studies of P. vannamei and may play a role in the development of SNP markers for future breeding studies.

Genetics of canine myxomatous mitral valve disease

Myxomatous mitral valve disease (MMVD) is the most common heart disease and cause of cardiac death in domestic dogs. MMVD is characterised by slow progressive myxomatous degeneration from the tips of the mitral valves onwards with subsequent mitral valve regurgitation, and left atrial and ventricular dilatation. Although the disease usually has a long asymptomatic period, in dogs with severe disease, mortality is typically secondary to left-sided congestive heart failure. Although it is not uncommon for dogs to survive long enough in the asymptomatic period to die from unrelated causes; a proportion of dogs rapidly advance into congestive heart failure. Heightened prevalence in certain breeds, such as the Cavalier King Charles Spaniel, has indicated that MMVD is under a genetic influence. The genetic characterisation of the factors that underlie the difference in progression of disease is of strong interest to those concerned with dog longevity and welfare. Advanced genomic technologies have the potential to provide information that may impact treatment, prevalence, or severity of MMVD through the elucidation of pathogenic mechanisms and the detection of predisposing genetic loci of major effect. Here we describe briefly the clinical nature of the disorder and consider the physiological mechanisms that might impact its occurrence in the domestic dog. Using results from comparative genomics we suggest possible genetic approaches for identifying genetic risk factors within breeds. The Cavalier King Charles Spaniel breed represents a robust resource for uncovering the genetic basis of MMVD.

Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

Haplotype-based association methods have been developed to understand the genetic architecture of complex diseases. Compared to single-variant-based methods, haplotype methods are thought to be more biologically relevant, since there are typically multiple non-independent genetic variants involved in complex diseases, and the use of haplotypes implicitly accounts for non-independence caused by linkage disequilibrium. In recent years, with the focus moving from common to rare variants, haplotype-based methods have also evolved accordingly to uncover the roles of rare haplotypes. One particular approach is regularization-based, with the use of Bayesian least absolute shrinkage and selection operator (Lasso) as an example. This type of methods has been developed for either case-control population data (the logistic Bayesian Lasso (LBL)) or family data (family-triad-based logistic Bayesian Lasso (famLBL)). In some situations, both family data and case-control data are available; therefore, it would be a waste of resources if only one of them could be analyzed. To make full usage of available data to increase power, we propose a unified approach that can combine both case-control and family data (combined logistic Bayesian Lasso (cLBL)). Through simulations, we characterized the performance of cLBL and showed the advantage of cLBL over existing methods. We further applied cLBL to the Framingham Heart Study data to demonstrate its utility in real data applications.

Association of 3p27.1 Variants with Whole Body Lean Mass Identified by a Genome-wide Association Study

Whole body lean mass (WBLM) is a heritable trait predicting sarcopenia. To identify genomic locus underlying WBLM, we performed a genome-wide association study of fat-adjusted WBLM in the Framingham Heart Study (FHS, N = 6,004), and replicated in the Kansas City Osteoporosis Study (KCOS, N = 2,207). We identified a novel locus 3p27.1 that was associated with WBLM (lead SNP rs3732593 P = 7.19 × 10^-8) in the discovery FHS sample, and the lead SNP was successfully replicated in the KCOS sample (one-sided P = 0.04). Bioinformatics analysis found that this SNP and its adjacent SNPs had the function of regulating enhancer activity in skeletal muscle myoblasts cells, further confirming the regulation of WBLM by this locus. Our finding provides new insight into the genetics of WBLM and enhance our understanding of sarcopenia.

Variants in ADIPOQ gene are linked to adiponectin levels and lung function in young males independent of obesity

Background

Obesity is a major risk factor for many chronic diseases, including reduced lung function. The role of polymorphisms of the adiponectin gene, though linked with cardiometabolic consequences of obesity, has not been studied in relation to lung function.

Objectives

The aim of this study is to examine polymorphisms in the ADIPOQ, ADIPOR1, and ADIPOR2 genes in relation to adiponectin serum levels, BMI, and adiposity in 18-year old Cypriot males, as well as determine whether BMI, adipokines levels and polymorphisms in adipokine related genes are associated with lung function levels.

Results

From the participants, 8% were classified as obese, 22% as overweight, and the remaining 71% as normal. We found that rs266729 and rs1501299 in ADIPOQ and rs10920531 in ADIPOR1 were significantly associated with serum adiponectin levels, after adjusting for ever smoking. In addition, there was an overall significant increase in FEV₁% predicted with increasing BMI (β = 0.53, 95% CI: 0.27, 0.78) and in FVC % predicted (β = 1.02, 95% CI: 0.73, 1.30). There was also a decrease in FEV₁/FVC with increasing BMI (β = -0.53, 95% CI: -0.71, -0.35). Finally, rs1501299 was associated with lung function measures.

Discussion

Functional variants in the ADIPOQ gene were linked with lung function in young males. Further studies should concentrate on the role of adipokines on lung function which may direct novel therapeutic approaches.

Two functional variants at 6p21.1 were associated with lean mass

Background

Low lean body mass is the most important predictor of sarcopenia with strong genetic background. The aim of this study was to uncover genetic factors underlying lean mass development.

Materials and methods

We performed a genome-wide association study (GWAS) of fat-adjusted leg lean mass in the Framingham Heart Study (FHS, N = 6587), and replicated in the Women’s Health Initiative–African American sub-sample (WHI-AA, N = 847) and the Kansas City Osteoporosis Study (KCOS, N = 2219). We also cross-validated significant variants in the publicly available body mass index (BMI) summary results (N ~ 700,000). We then performed a series of functional investigations on the identified variants.

Results

Four correlated SNPs at 6p21.1 were identified at the genome-wide significance (GWS, α = 5.0 × 10⁻⁸) level in the discovery FHS sample (rs551145, rs524533, rs571770, and rs545970, p = 3.40–9.77 × 10⁻⁹), and were successfully replicated in both the WHI-AA and the KCOS samples (one-sided p = 1.61 × 10⁻³–0.04). They were further cross-validated by the large-scale BMI summary results (p = 7.0–9.8 × 10⁻³). Cis-eQTL analyses associated these SNPs with the NFKBIE gene expression. Electrophoresis mobility shift assay (EMSA) in mouse C2C12 myoblast cells implied that rs524533 and rs571770 were bound to an unknown transcription factor in an allelic specific manner, while rs551145 and rs545970 did not. Dual-luciferase reporter assay revealed that both rs524533 and rs571770 downregulated luciferase expression by repressing promoter activity. Moreover, the regulation pattern was allelic specific, strengthening the evidence towards their differential regulatory effects.

Conclusions

Through a large-scale GWAS followed by a series of functional investigations, we identified 2 correlated functional variants at 6p21.1 associated with leg lean mass. Our findings not only enhanced our understanding of molecular basis of lean mass development but also provided useful candidate genes for further functional studies.

Imputation accuracy of wheat genotyping-by-sequencing (GBS) data using barley and wheat genome references

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.

Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

Context

Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive.

Objective

To explore the relationship between blood metabolites and osteoporosis.

Design and Methods

We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate.

Results

We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249).

Conclusions

Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

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.

Analyzing metabolomics data for association with genotypes using two-component Gaussian mixture distributions

Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model. Then, using real data from the Framingham Heart Study, we analyzed 20,315 SNPs on chromosome 11, demonstrating that the proposed likelihood ratio test identifies SNPs well known to participate in the desaturation of certain fatty acids. Our study both validates the approach of increasing power by using the likelihood ratio test that leverages Gaussian mixture models, and creates a model with improved sensitivity and interpretability.

Genome-Wide Interaction Study of Omega-3 PUFAs and Other Fatty Acids on Inflammatory Biomarkers of Cardiovascular Health in the Framingham Heart Study

Numerous genetic loci have been identified as being associated with circulating fatty acid (FA) levels and/or inflammatory biomarkers of cardiovascular health (e.g., C-reactive protein). Recently, using red blood cell (RBC) FA data from the Framingham Offspring Study, we conducted a genome-wide association study of over 2.5 million single nucleotide polymorphisms (SNPs) and 22 RBC FAs (and associated ratios), including the four Omega-3 FAs (ALA, DHA, DPA, and EPA). Our analyses identified numerous causal loci. In this manuscript, we investigate the extent to which polyunsaturated fatty acid (PUFA) levels moderate the relationship of genetics to cardiovascular health biomarkers using a genome-wide interaction study approach. In particular, we test for possible gene–FA interactions on 9 inflammatory biomarkers, with 2.5 million SNPs and 12 FAs, including all Omega-3 PUFAs. We identified eighteen novel loci, including loci which demonstrate strong evidence of modifying the impact of heritable genetics on biomarker levels, and subsequently cardiovascular health. The identified genes provide increased clarity on the biological functioning and role of Omega-3 PUFAs, as well as other common fatty acids, in cardiovascular health, and suggest numerous candidate loci for future replication and biological characterization.

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies

Background

Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2.

Results

We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes.

Conclusions

These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1142-6) contains supplementary material, which is available to authorized users.

Functionally Diverse NK-Like T Cells Are Effectors and Predictors of Successful Aging

The fundamental challenge of aging and long-term survivorship is maintenance of functional independence and compression of morbidity despite a life history of disease. Inasmuch as immunity is a determinant of individual health and fitness, unraveling novel mechanisms of immune homeostasis in late life is of paramount interest. Comparative studies of young and old persons have documented age-related atrophy of the thymus, the contraction of diversity of the T cell receptor (TCR) repertoire, and the intrinsic inefficiency of classical TCR signaling in aged T cells. However, the elderly have highly heterogeneous health phenotypes. Studies of defined populations of persons aged 75 and older have led to the recognition of successful aging, a distinct physiologic construct characterized by high physical and cognitive functioning without measurable disability. Significantly, successful agers have a unique T cell repertoire; namely, the dominance of highly oligoclonal αβT cells expressing a diverse array of receptors normally expressed by NK cells. Despite their properties of cell senescence, these unusual NK-like T cells are functionally active effectors that do not require engagement of their clonotypic TCR. Thus, NK-like T cells represent a beneficial remodeling of the immune repertoire with advancing age, consistent with the concept of immune plasticity. Significantly, certain subsets are predictors of physical/cognitive performance among older adults. Further understanding of the roles of these NK-like T cells to host defense, and how they integrate with other physiologic domains of function are new frontiers for investigation in Aging Biology. Such pursuits will require a research paradigm shift from the usual young-versus-old comparison to the analysis of defined elderly populations. These endeavors may also pave way to age-appropriate, group-targeted immune interventions for the growing elderly population.

Association of 3q13.32 variants with hip trochanter and intertrochanter bone mineral density identified by a genome-wide association study

We performed a GWAS of trochanter and intertrochanter bone mineral density (BMD) in the Framingham Heart Study and replicated in three independent studies. Our results identified one novel locus around the associated variations at chromosomal region 3q13.32 and replicated two loci at chromosomal regions 3p21 and 8q24. Our findings provide useful insights that enhance our understanding of bone development, osteoporosis, and fracture pathogenesis.

INTRODUCTION

Hip trochanter (TRO) and intertrochanter (INT) subregions have important clinical relevance to subtrochanteric and intertrochanteric fractures but have rarely been studied by genome-wide association studies (GWASs).

METHODS

Aiming to identify genomic loci associated with BMD variation at TRO and INT regions, we performed a GWAS utilizing the Framingham Heart Study (FHS, N = 6,912) as discovery sample and utilized the Women's Health Initiative (WHI) African-American subsample (N = 845), WHI Hispanic subsample (N = 446), and Omaha osteoporosis study (N = 971), for replication.

RESULTS

Combining the evidence from both the discovery and the replication samples, we identified one novel locus around the associated variations at chromosomal region 3q13.32 (rs1949542, discovery p = 6.16 × 10^-8, replication p = 2.86 × 10^-4 for INT-BMD; discovery p = 1.35 × 10^-7, replication p = 4.16 × 10^-4 for TRO-BMD, closest gene RP11-384F7.1). We also replicated two loci at chromosomal regions 3p21 (rs148725943, discovery p = 6.61 × 10^-7, replication p = 5.22 × 10^-4 for TRO-BMD, closest gene CTNNB1) and 8q24 (rs7839059, discovery p = 2.28 × 10^-7, replication p = 1.55 × 10^-3 for TRO-BMD, closest gene TNFRSF11B) that were reported previously. We demonstrated that the effects at both 3q13.32 and 3p21 were specific to the TRO, but not to the femoral neck and spine. In contrast, the effect at 8q24 was common to all the sites.

CONCLUSION

Our findings provide useful insights that enhance our understanding of bone development, osteoporosis, and fracture pathogenesis.

Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event

The “winner's curse” is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large‐scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1,213 single‐nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1,361 white probands. Among 15 top‐ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome‐wide bootstrap estimates for false‐positive SNPs and for true‐positive SNPs with low‐to‐moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias‐reduction technique is generally applicable for complex trait studies including quantitative, binary, and time‐to‐event traits.

Surveillance of cardiovascular diseases using a multivariate dynamic screening system

In the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute, one major task is to monitor several health variables (e.g., blood pressure and cholesterol level) so that their irregular longitudinal pattern can be detected as soon as possible and some medical treatments applied in a timely manner to avoid some deadly cardiovascular diseases (e.g., stroke). To handle this kind of applications effectively, we propose a new statistical methodology called multivariate dynamic screening system (MDySS) in this paper. The MDySS method combines the major strengths of the multivariate longitudinal data analysis and the multivariate statistical process control, and it makes decisions about the longitudinal pattern of a subject by comparing it with other subjects cross sectionally and by sequentially monitoring it as well. Numerical studies show that MDySS works well in practice.

A powerful nonparametric statistical framework for family-based association analyses

Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.

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.

Cohort of birth modifies the association between FTO genotype and BMI

A substantial body of research has explored the relative roles of genetic and environmental factors on phenotype expression in humans. Recent research has also sought to identify gene-environment (or g-by-e) interactions, with mixed success. One potential reason for these mixed results may relate to the fact that genetic effects might be modified by changes in the environment over time. For example, the noted rise of obesity in the United States in the latter part of the 20th century might reflect an interaction between genetic variation and changing environmental conditions that together affect the penetrance of genetic influences. To evaluate this hypothesis, we use longitudinal data from the Framingham Heart Study collected over 30 y from a geographically relatively localized sample to test whether the well-documented association between the rs993609 variant of the FTO (fat mass and obesity associated) gene and body mass index (BMI) varies across birth cohorts, time period, and the lifecycle. Such cohort and period effects integrate many potential environmental factors, and this gene-by-environment analysis examines interactions with both time-varying contemporaneous and historical environmental influences. Using constrained linear age-period-cohort models that include family controls, we find that there is a robust relationship between birth cohort and the genotype-phenotype correlation between the FTO risk allele and BMI, with an observed inflection point for those born after 1942. These results suggest genetic influences on complex traits like obesity can vary over time, presumably because of global environmental changes that modify allelic penetrance.

Correcting for population structure and kinship using the linear mixed model: theory and extensions

Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.

Gene expression signatures of coronary heart disease

OBJECTIVE

To identify transcriptomic biomarkers of coronary heart disease (CHD) in 188 cases with CHD and 188 age- and sex-matched controls who were participants in the Framingham Heart Study.

APPROACH AND RESULTS

A total of 35 genes were differentially expressed in cases with CHD versus controls at false discovery rate<0.5, including GZMB, TMEM56, and GUK1. Cluster analysis revealed 3 gene clusters associated with CHD, 2 linked to increased erythrocyte production and a third to reduced natural killer and T cell activity in cases with CHD. Exon-level results corroborated and extended the gene-level results. Alternative splicing analysis suggested that GUK1 and 38 other genes were differentially spliced in cases with CHD versus controls. Gene Ontology analysis linked ubiquitination and T-cell-related pathways with CHD.

CONCLUSIONS

Two bioinformatically defined groups of genes show consistent associations with CHD. Our findings are consistent with the hypotheses that hematopoesis is upregulated in CHD, possibly reflecting a compensatory mechanism, and that innate immune activity is disrupted in CHD or altered by its treatment. Transcriptomic signatures may be useful in identifying pathways associated with CHD and point toward novel therapeutic targets for its treatment and prevention.

Genome-wide association study of sleep in Drosophila melanogaster

BACKGROUND

Sleep is a highly conserved behavior, yet its duration and pattern vary extensively among species and between individuals within species. The genetic basis of natural variation in sleep remains unknown.

RESULTS

We used the Drosophila Genetic Reference Panel (DGRP) to perform a genome-wide association (GWA) study of sleep in D. melanogaster. We identified candidate single nucleotide polymorphisms (SNPs) associated with differences in the mean as well as the environmental sensitivity of sleep traits; these SNPs typically had sex-specific or sex-biased effects, and were generally located in non-coding regions. The majority of SNPs (80.3%) affecting sleep were at low frequency and had moderately large effects. Additive models incorporating multiple SNPs explained as much as 55% of the genetic variance for sleep in males and females. Many of these loci are known to interact physically and/or genetically, enabling us to place them in candidate genetic networks. We confirmed the role of seven novel loci on sleep using insertional mutagenesis and RNA interference.

CONCLUSIONS

We identified many SNPs in novel loci that are potentially associated with natural variation in sleep, as well as SNPs within genes previously known to affect Drosophila sleep. Several of the candidate genes have human homologues that were identified in studies of human sleep, suggesting that genes affecting variation in sleep are conserved across species. Our discovery of genetic variants that influence environmental sensitivity to sleep may have a wider application to all GWA studies, because individuals with highly plastic genotypes will not have consistent phenotypes.

Gene-gene interaction between RBMS3 and ZNF516 influences bone mineral density

Osteoporosis is characterized by low bone mineral density (BMD), a highly heritable trait that is determined, in part, by the actions and interactions of multiple genes. Although an increasing number of genes have been identified to have independent effects on BMD, few studies have been performed to identify genes that interact with one another to affect BMD. In this study, we performed gene-gene interaction analyses in selected candidate genes in individuals with extremely high versus low hip BMD (20% tails of the distributions), in two independent U.S. Caucasian samples. The first sample contained 916 unrelated subjects with extreme hip BMD Z-scores selected from a population composed of 2286 subjects. The second sample consisted of 400 unrelated subjects with extreme hip BMD Z-scores selected from a population composed of 1000 subjects. Combining results from these two samples, we found one interacting gene pair (RBMS3 versus ZNF516) which, even after Bonferroni correction for multiple testing, showed consistently significant effects on hip BMD. RMBS3 harbored two single-nucleotide polymorphisms (SNPs), rs6549904 and rs7640046, both of which had significant interactions with an SNP, rs4891159, located on ZNF516 (p = 7.04 × 10(-11) and 1.03 × 10(-10) ). We further validated these results in two additional samples of Caucasian and African descent. The gene pair, RBMS3 versus ZNF516, was successfully replicated in the Caucasian sample (p = 8.07 × 10(-3) and 2.91 × 10(-3) ). For the African sample, a significant interaction was also detected (p = 0.031 and 0.043), but the direction of the effect was opposite to that observed in the three Caucasian samples. By providing evidence for genetic interactions underlying BMD, this study further delineates the genetic architecture of osteoporosis.

Lipids and vascular disease: a framingham perspective

Research related to lipid levels, correlates of lipid levels, and how lipid levels are related to vascular disease outcomes in the Framingham cohorts are summarized for data obtained from 1948 to the present day. Initial lipid data in Framingham participants were largely confined to cholesterol and triglycerides. Technology evolved to later include lipoprotein cholesterol quantification using ultracentrifugation, apolipoproteins, genetics, lipid particle size and number, and use of lipid information in multivariable equations to estimate risk for the development of initial cardiovascular disease outcomes. The information is presented chronologically to highlight the developments related to the lipids and heart disease over the past 50 years.

Resequencing and clinical associations of the 9p21.3 region: a comprehensive investigation in the Framingham heart study

BACKGROUND

9p21.3 is among the most strongly replicated regions for cardiovascular disease. There are few reports of sequencing the associated 9p21.3 interval. We set out to sequence the 9p21.3 region followed by a comprehensive study of genetic associations with clinical and subclinical cardiovascular disease and its risk factors, as well as with copy number variation and gene expression, in the Framingham Heart Study (FHS).

METHODS AND RESULTS

We sequenced 281 individuals (94 with myocardial infarction, 94 with high coronary artery calcium levels, and 93 control subjects free of elevated coronary artery calcium or myocardial infarction), followed by genotyping and association in >7000 additional FHS individuals. We assessed genetic associations with clinical and subclinical cardiovascular disease, risk factor phenotypes, and gene expression levels of the protein-coding genes CDKN2A and CDKN2B and the noncoding gene ANRIL in freshly harvested leukocytes and platelets. Within this large sample, we found strong associations of 9p21.3 variants with increased risk for myocardial infarction, higher coronary artery calcium levels, and larger abdominal aorta diameters and no evidence for association with traditional cardiovascular disease risk factors. No common protein-coding variation, variants in splice donor or acceptor sites, or copy number variation events were observed. By contrast, strong associations were observed between genetic variants and gene expression, particularly for a short isoform of ANRIL and for CDKN2B.

CONCLUSIONS

Our thorough genomic characterization of 9p21.3 suggests common variants likely account for observed disease associations and provides further support for the hypothesis that complex regulatory variation affecting ANRIL and CDKN2B gene expression may contribute to increased risk for clinically apparent and subclinical coronary artery disease and aortic disease.

The robustness of generalized estimating equations for association tests in extended family data

Variance components analysis (VCA), the traditional method for handling correlations within families in genetic association studies, is computationally intensive for genome-wide analyses, and the computational burden of VCA increases with family size and the number of genetic markers. Alternative approaches that do not require the computation of familial correlations are preferable, provided that they do not inflate type I error or decrease power. We performed a simulation study to evaluate practical alternatives to VCA that use regression with generalized estimating equations (GEE) in extended family data. We compared the properties of linear regression with GEE applied to an entire extended family structure (GEE-EXT) and GEE applied to nuclear family structures split from these extended families (GEE-SPL) to variance components likelihood-based methods (FastAssoc). GEE-EXT was evaluated with and without robust variance estimators to estimate the standard errors. We observed similar average type I error rates from GEE-EXT and FastAssoc compared to GEE-SPL. Type I error rates for the GEE-EXT method with a robust variance estimator were marginally higher than the nominal rate when the minor allele frequency (MAF) was <0.1, but were close to the nominal rate when the MAF was ≥0.2. All methods gave consistent effect estimates and had similar power. In summary, the GEE framework with the robust variance estimator, the computationally fastest and least data management-intensive approach, appears to work well in extended families and thus provides a reasonable alternative to full variance components approaches for extended pedigrees in a genome-wide association study setting.

Constructing endophenotypes of complex diseases using non-negative matrix factorization and adjusted rand index

Complex diseases are typically caused by combinations of molecular disturbances that vary widely among different patients. Endophenotypes, a combination of genetic factors associated with a disease, offer a simplified approach to dissect complex trait by reducing genetic heterogeneity. Because molecular dissimilarities often exist between patients with indistinguishable disease symptoms, these unique molecular features may reflect pathogenic heterogeneity. To detect molecular dissimilarities among patients and reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytical procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI), a measure of the similarity of two clusterings of a data set. To evaluate this procedure, we compared it with a commonly used method, principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression dataset and genotype information was conducted to examine the performance of our procedure and PCA-K. The results showed that NMF mostly outperformed PCA-K. Additionally, we applied our endophenotype-identification analytical procedure to a publicly available dataset containing data derived from patients with late-onset Alzheimer's disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes (MS) for patients in the LOAD dataset: MS1 (n1=80), MS2 (n2=73), and MS3 (n3=23). ARI was then used to determine the most representative transcripts for each metagene; 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts were identified as the endophenotypes. Our results showed that 14, 38, 0, and 28 candidate susceptibility genes listed in AlzGene database were found by all patients, MS1, MS2, and MS3, respectively. Moreover, we found that MS2 might be a normal-like subtype. Our proposed procedure provides an alternative approach to investigate the pathogenic mechanism of disease and better understand the relationship between phenotype and genotype.

von Willebrand factor plasma levels, genetic variations and coronary heart disease in an older population

BACKGROUND

High von Willebrand factor (VWF) levels are associated with an increased risk of coronary heart disease (CHD). However, it remains unclear whether VWF is causally related to the occurrence of CHD or primarily mirrors endothelial dysfunction, which predisposes to atherosclerosis and subsequent CHD.

OBJECTIVES

Because VWF is largely determined by genetic factors, we investigated whether VWF antigen levels (VWF:Ag) and the risk of CHD are affected by common variations in the VWF gene.

METHODS

We included 7002 participants (≥ 55 years) from the large prospective population-based Rotterdam Study in the discovery cohort. The extension cohort of the Rotterdam Study, consisting of 3011 participants, was used as a replication cohort. We determined VWF:Ag levels and genotype data of 38 single-nucleotide polymorphisms (SNPs) in VWF. Subsequently, hazard ratios for CHD were calculated and genetic analyses were performed to assess the relationship between SNPs, VWF:Ag levels and CHD risk.

RESULTS

We identified and replicated three SNPs that were associated with VWF:Ag: rs216321 (β = 0.10 [95% confidence interval, CI, 0.06;0.13]) (Ala852Gln), rs1063856 (β = 0.05 [95% CI 0.03;0.07]) (Thr789Ala) and rs2283333 (β = 0.09 [95% CI 0.05;0.21]) (intron 15). However, genetic polymorphisms in the VWF gene were not associated with the risk of CHD.

CONCLUSIONS

In this study we have shown that genetic variations in VWF strongly affect VWF plasma levels, but are not associated with the risk of CHD. Our findings therefore do not support a strong causal relationship between VWF and CHD in elderly individuals of ≥ 55 years, but suggest that VWF is primarily a marker of CHD.

A novel test of informative missingness using inconsistent linkage disequilibrium signals between case-parent triads and incomplete data

In general, multiple issues are examined before the analysis of genetic data such as Hardy-Weinberg Equilibrium and Mendelian errors. Although missing genotypes are commonly observed in genetic studies, potential bias due to informative missingness is usually overlooked. Therefore, the Test of Informative Missingness (TIM) was the first attempt to determine whether or not parental genotypes are missing informatively. The TIM is a useful tool for genetic data cleaning. For example, excluding single-nucleotide polymorphisms that appear to be missing informatively may further improve the quality of genetic data. Although the TIM has decent power, its performance is discernibly weaker when the minor allele/genotype introduces informative missingness. In an effort to avoid such reduced power, the newly proposed strategy detects informative missingness by comparing inconsistent linkage disequilibrium signals between intact case-parent triads and incomplete data. Computer simulations revealed that the new method was robust to population stratifications and more powerful than the TIM in most situations. In addition, the new method demonstrated decent power in the genome-wide association study, even if the most conservative correction for multiple testing was adopted.

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets

Genome-wide association (GWA) studies of complex diseases including coronary heart disease (CHD) challenge investigators attempting to identify relevant genetic variants among hundreds of thousands of markers being tested. A selection strategy based purely on statistical significance will result in many false negative findings after adjustment for multiple testing. Thus, an integrated analysis using information from the learned genetic pathways, molecular functions, and biological processes is desirable. In this study, we applied a customized method, variable set enrichment analysis (VSEA), to the Framingham Heart Study data (404,467 variants, n=6421) to evaluate enrichment of genetic association in 1395 gene sets for their contribution to CHD. We identified 25 gene sets with nominal P<0.01; at least four sets are previously known for their roles in CHD: vascular genesis (GO:0001570), fatty-acid biosynthetic process (GO:0006633), fatty-acid metabolic process (GO:0006631), and glycerolipid metabolic process (GO:0046486). Although the four gene sets include 170 genes, only three of the genes contain a variant ranked among the top 100 in single-variant association tests of the 404,467 variants tested. Significant enrichment for novel gene sets less known for their importance to CHD were also identified: Rac 1 cell-motility signaling pathway (h_rac1 Pathway, P<0.001) and sulfur amino-acid metabolic process (GO:0000096, P<0.001). In summary, we showed that the pathway-based VSEA can help prioritize association signals in GWA studies by identifying biologically plausible targets for downstream searches of genetic variants associated with CHD.

Evaluating the evidence for transmission distortion in human pedigrees

Children of a heterozygous parent are expected to carry either allele with equal probability. Exceptions can occur, however, due to meiotic drive, competition among gametes, or viability selection, which we collectively term “transmission distortion” (TD). Although there are several well-characterized examples of these phenomena, their existence in humans remains unknown. We therefore performed a genome-wide scan for TD by applying the transmission disequilibrium test (TDT) genome-wide to three large sets of human pedigrees of European descent: the Framingham Heart Study (FHS), a founder population of European origin (HUTT), and a subset of the Autism Genetic Resource Exchange (AGRE). Genotyping error is an important confounder in this type of analysis. In FHS and HUTT, despite extensive quality control, we did not find sufficient evidence to exclude genotyping error in the strongest signals. In AGRE, however, many signals extended across multiple SNPs, a pattern highly unlikely to arise from genotyping error. We identified several candidate regions in this data set, notably a locus in 10q26.13 displaying a genome-wide significant TDT in combined female and male transmissions and a signature of recent positive selection, as well as a paternal TD signal in 6p21.1, the same region in which a significant TD signal was previously observed in 30 European males. Neither region replicated in FHS, however, and the paternal signal was not visible in sperm competition assays or as allelic imbalance in sperm. In maternal transmissions, we detected no strong signals near centromeres or telomeres, the regions predicted to be most susceptible to female-specific meiotic drive, but we found a significant enrichment of top signals among genes involved in cell junctions. These results illustrate both the potential benefits and the challenges of using the TDT to study transmission distortion and provide candidates for investigation in future studies.