Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study

Invited Commentary: The Framingham Offspring Study-A Pioneering Investigation Into Familial Aggregation of Cardiovascular Risk

Launched in 1948, the Framingham Heart Study was a seminal prospective cohort study of 5,209 adult residents of Framingham, Massachusetts, that was designed to uncover the determinants and natural history of coronary heart disease. Data from this original cohort established the cardiac threat posed by high blood pressure, high cholesterol, smoking, obesity, physical inactivity, diabetes, and other factors. In the late 1960s, investigators conceived the innovative idea of assembling a second cohort that comprised the adult children of the original study population (and these children's spouses). From 1971 to 1975, a total of 5,124 individuals were recruited to form the Offspring Cohort. Studying successive generations in this fashion provided an efficient method for examining secular trends in cardiovascular disease and its risk factors, as well as an opportunity to assess familial aggregation of risk without the threat of recall bias. In a paper published in the September 1979 issue of the Journal, then study director William Kannel et al. (Am J Epidemiol. 1979;110(3):281-290) described the sampling design of the Offspring Study and presented selected baseline characteristics of the cohort. The scientific questions addressed by this research provided the impetus for a decades-long effort-still in full force today both within the Framingham Study itself and in the broader cardiovascular epidemiologic community-to quantify the independent and synergistic effects of genetic, lifestyle, and other environmental factors on cardiovascular outcomes.

Parental intermittent claudication as risk factor for claudication in adults

Little is known about the familial aggregation of intermittent claudication (IC). Our objective was to examine whether parental IC increased the risk of IC in adult offspring, independent of the established cardiovascular risk factors. We evaluated the Offspring Cohort Participants of the Framingham Heart Study who were ≥30 years old, cardiovascular disease free, and had both parents enrolled in the Framingham Heart Study (n = 2,970 unique participants, 53% women). Pooled proportional hazards regression analysis was used to examine whether the 12-year risk of incident IC in offspring participants was associated with parental IC, adjusting for age, gender, diabetes, smoking, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, and antihypertensive and lipid treatment. Of the 909 person-examinations in the parental IC history group and 5,397 person-examinations in the no-parental IC history group, there were 101 incident IC events (29 with parental IC history and 72 without a parental IC history) during follow-up. The age- and gender-adjusted 12-year cumulative incidence rate per 1,000 person-years was 5.08 (95% confidence interval [CI] 2.74 to 7.33) and 2.34 (95% CI 1.46 to 3.19) in participants with and without a parental IC history. A parental history of IC significantly increased the risk of incident IC in the offspring (multivariable adjusted hazard ratio 1.81, 95% CI 1.14 to 2.88). The hazard ratio was unchanged, with an adjustment for the occurrence of cardiovascular disease (hazard ratio 1.83, 95% CI 1.15 to 2.91). In conclusion, IC in parents increases the risk of IC in adult offspring, independent of the established risk factors. These data suggest a genetic component of peripheral artery disease and support future research into genetic causes.

Genome-wide association studies and the genetic dissection of complex traits

The availability of affordable high throughput technology for parallel genotyping has opened the field of genetics to genome-wide association studies (GWAS), and in the last few years hundreds of articles reporting results of GWAS for a variety of heritable traits have been published. What do these results tell us? Although GWAS have discovered a few hundred reproducible associations, this number is underwhelming in relation to the huge amount of data produced, and challenges the conjecture that common variants may be the genetic causes of common diseases. We argue that the massive amount of genetic data that result from these studies remains largely unexplored and unexploited because of the challenge of mining and modeling enormous data sets, the difficulty of using nontraditional computational techniques and the focus of accepted statistical analyses on controlling the false positive rate rather than limiting the false negative rate. In this article, we will review the common approach to analysis of GWAS data and then discuss options to learn more from these data. We will use examples from our ongoing studies of sickle cell anemia and also GWAS in multigenic traits.

The genetic contribution to non-syndromic human obesity

The last few years have seen major advances in common non-syndromic obesity research, much of it the result of genetic studies. This Review outlines the competing hypotheses about the mechanisms underlying the genetic and physiological basis of obesity, and then examines the recent explosion of genetic association studies that have yielded insights into obesity, both at the candidate gene level and the genome-wide level. With obesity genetics now entering the post-genome-wide association scan era, the obvious question is how to improve the results obtained so far using single nucleotide polymorphism markers and how to move successfully into the other areas of genomic variation that may be associated with common obesity.

Metabolic syndrome pathophysiology: the role of adipose tissue

The metabolic syndrome comprises a set of metabolic and physiological risk factors associated with elevated cardiovascular disease risk. The expression of each one of its major factors (hypertriglyceridemia, low high-density lipoprotein cholesterol levels, hypertension, abdominal obesity, and insulin resistance) has been found to be the result of complex interactions between genetic and environmental factors. Moreover, one of them, obesity, may play a major role in triggering the metabolic syndrome by interacting with genetic variants at candidate genes for dyslipidemia, hypertension, and insulin resistance. In support of this hypothesis, several studies at several candidate genes, mainly adipokines and perilipin, have already demonstrated the significance of these interactions; however, the information and its solidity are still very limited and in many cases, replication studies are still lacking in the literature. Therefore, more studies with better epidemiological design and standardized adiposity measures are needed to estimate the contribution of body weight and fat distribution to the genetic predisposition to the metabolic syndrome, the most common CVD risk factor in industrialized societies.

Planning and executing a genome wide association study (GWAS)

In recent years, genome-wide association approaches have proven a powerful and successful strategy to identify genetic contributors to complex traits, including a number of endocrine disorders. Their success has meant that genome wide association studies (GWAS) are fast becoming the default study design for discovery of new genetic variants that influence a clinical trait or phenotype. This chapter focuses on a number of key elements that require consideration for the successful conduct of a GWAS. Although many of the considerations are common to any genetic study, the greater cost, extreme multiple testing, and greater openness to data sharing require specific awareness and planning by investigators. In the section on designing a GWAS, we reflect on ethical considerations, study design, selection of phenotype/s, power considerations, sample tracking and storage issues, and genotyping product selection. During execution, important considerations include DNA quantity and preparation, genotyping methods, quality control checks of genotype data, in silico genotyping (imputation), tests of association, and replication of association signals. Although the field of human genetics is rapidly evolving, recent experiences can help guide an investigator in making practical and methodological choices that will eventually determine the overall quality of GWAS results. Given the investment to recruit patient populations or cohorts that are powered for a GWAS, and the still substantial costs associated with genotyping, it is helpful to be aware of these aspects to maximize the likelihood of success, especially where there is an opportunity for implementing them prospectively.

The genetics of cardiovascular disease

Recent advances in genotyping technology and insights into disease mechanisms have increased interest in the genetics of cardiovascular disease. Several candidate genes involved in cardiovascular diseases were identified from studies using animal models, and the translation of these findings to human disease is an exciting challenge. There is a trend towards large-scale genome-wide association studies that are subject to strict quality criteria with regard to both genotyping and phenotyping. Here, we review some of the strategies that have been developed to translate findings from experimental models to human disease and outline the need for optimizing global approaches to analyze such results. Findings from ongoing studies are interpreted in the context of disease pathways instead of the more traditional focus on single genetic variants.