A new estimate of family disease history providing improved prediction of disease risks

A polygenic and family risk score are both independently associated with risk of type 2 diabetes in a population-based study

The availability of polygenic scores for type 2 diabetes (T2D) raises the question, whether assessing family history might become redundant. However, family history not only involves shared genetics, but also shared environment. It was the aim of this study to assess the independent and combined effects of one family risk score (FamRS) and a polygenic score (PGS) on prevalent and incident T2D risk in a population-based study from Germany (n = 3071). The study was conducted in 2004/2005 with up to 12 years of follow-up. The FamRS takes into account not only the number of diseased first grade relatives, but also age at onset of the relatives and age of participants. 256 prevalent and additional 163 incident T2D cases were recorded. Prevalence of T2D increased sharply for those within the top quantile of the PGS distribution resulting in an OR of 19.16 (p < 2 × 10^-16) for the top 20% compared to the remainder of the population, independent of age, sex, BMI, physical activity and FamRS. On the other hand, having a very strong family risk compared to average was still associated with an OR of 2.78 (p = 0.001), independent of the aforementioned factors and the PGS. The PGS and FamRS were only slightly correlated (r²_Spearman = 0.018). The combined contribution of both factors varied with varying age-groups, though, with decreasing influence of the PGS with increasing age. To conclude, both, genetic information and family history are relevant for the prediction of T2D risk and might be used for identification of high risk groups to personalize prevention measures.

The CCC2000 Birth Cohort Study of Register-Based Family History of Mental Disorders and Psychotic Experiences in Offspring

Psychotic experiences (PE) in individuals of the general population are hypothesized to mark the early expression of the pathology underlying psychosis. This notion of PE as an intermediate phenotype is based on the premise that PE share genetic liability with psychosis. We examined whether PE in childhood was predicted by a family history of mental disorder with psychosis rather than a family history of nonpsychotic mental disorder and whether this association differed by severity of PE. The study examined data on 1632 children from a general population birth cohort assessed at age 11-12 years by use of a semistructured interview covering 22 psychotic symptoms. The Danish national registers were linked to describe the complete family history of hospital-based psychiatric diagnoses. Uni- and multivariable logistic regressions were used to test whether a family history of any mental disorder with psychosis, or of nonpsychotic mental disorder, vs no diagnoses was associated with increased risk of PE in offspring (hierarchical exposure variable). The occurrence of PE in offspring was significantly associated with a history of psychosis among the first-degree relatives (adjusted relative risk [RR] = 3.29, 95% CI: 1.82-5.93). The risk increased for combined hallucinations and delusions (adjusted RR = 5.90, 95% CI: 2.64-13.16). A history of nonpsychotic mental disorders in first-degree relatives did not contribute to the risk of PE in offspring nor did any mental disorder among second-degree relatives. Our findings support the notion of PE as a vulnerability marker of transdiagnostic psychosis. The effect of psychosis in first-degree relatives may operate through shared genetic and environmental factors.

The impact of parental history on children's risk of asthma: a study based on the National Health and Nutrition Examination Survey-III

Purpose

This study aimed to examine the separate effects of maternal and paternal history on the onset of asthma in children and evaluate the relationship between age of asthma onset in parents and risk of asthma in their children.

Methods

We used data from the third National Health and Nutrition Examination Survey. We developed new continuous standardized scores for survey data to quantify parental history that incorporated both the occurrence of asthma and the age at onset, and associated these scores with asthma risk in the children. The association analysis was adjusted for sex and obesity status.

Results

Children with maternal history have elevated asthma risk (hazard ratio of 3.71, 95% CI: 1.19–11.60) than those without, and those whose mothers had earlier age of onset have increased risk of asthma compared to those whose mothers had later age of onset. On the contrary, paternal history had a relatively smaller effect that may be only detectable in larger samples (hazard ratio of 2.17, 95% CI: 0.69–6.79).

Conclusion

Maternal asthma history was strongly associated with the onset of asthma in the second generation, and children whose mother had an earlier age of onset had an increased risk of 3.71. For an approximately 10-year decrease in mother’s age at onset of asthma, the risk of asthma for the offspring increased by 1.37-fold. Using our new risk scores led to smaller standard errors and thus more precise estimates than using a binary indicator.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Genetic and environmental components of family history in type 2 diabetes

Family history of diabetes is a major risk factor for type 2 diabetes (T2D), but whether this association derives from shared genetic or environmental factors is unclear. To address this question, we developed a statistical framework that models four components of variance, including known and unknown genetic and environmental factors, using a liability threshold model. Focusing on parental history, we simulated case-control studies with two first-degree relatives for each individual, assuming 50 % genetic similarity and a range of values of environmental similarity. By comparing the association of parental history with T2D in our simulations to case-control studies of T2D nested in the Nurses' Health Study and Health Professionals Follow-up Study, we estimate that first-degree relatives have a correlation of 23 % (95 % CI 15-27 %) in their environmental contribution to T2D liability and that this shared environment is responsible for 32 % (95 % CI 24-36 %) of the association between parental history and T2D, with the remainder due to shared genetics. Estimates are robust to varying model parameter values and our framework can be extended to different definitions of family history. In conclusion, we find that the association between parental history and T2D derives from predominately genetic but also environmental effects.

Family history of stroke and cardiovascular health in a national cohort

BACKGROUND

We investigated the association between family history of stroke (FHS) and Life's Simple 7 (LS7), a public health metric defined by the American Heart Association.

METHODS

Reasons for Geographic and Racial Differences in Stroke is a national population-based cohort of 30,239 blacks and whites, aged 45 years or older, sampled from the US population between 2003 and 2007. Data were collected by telephone, mail questionnaires, and in-home examinations. FHS was defined as any first-degree relative with stroke. Levels of the LS7 components (total cholesterol, blood pressure, fasting glucose, physical activity, diet, smoking, and body mass index) were each coded as poor (0 points), intermediate (1 point), or ideal (2 points) health. Ordinal logistic regression was used to model the data.

RESULTS

Among 20,567 subjects with complete LS7 and FHS data, there were 7702 (37%) participants with an FHS. The mean age of the participants was 64 years. The mean (± standard deviation) overall LS7 score was lower for blacks (6.5 ± 2.0) than that of whites (7.6 ± 2.1). FHS was associated with poorer levels of physiological factors, particularly high blood pressure (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.07-1.19) and inversely associated with behaviors such as smoking (OR, .92; 95% CI, .85-.99).

CONCLUSIONS

Our results suggest that screening for FHS can provide an opportunity for earlier detection and management of modifiable risk factors.

Quantifying Maternal and Paternal Disease History Using Log-Rank Score with an Application to a National Cohort Study

Both maternal and paternal disease history can be important predictors of the risk of common conditions such as heart disease or cancer because of shared environmental and genetic risk factors. Sometimes maternal and paternal history can have remarkably different effects on offspring's status. The results are often affected by how the maternal and paternal disease histories are quantified. We proposed using the log-rank score (LRS) to investigate the separate effect of maternal and paternal history on diseases, which takes parental disease status and the age of their disease onset into account. Through simulation studies, we compared the performance of the maternal and paternal LRS with simple binary indicators under two different mechanisms of unbalanced parental effects. We applied the LRS to a national cohort study to further segregate family risks for heart diseases. We demonstrated using the LRS rather than binary indicators can improve the prediction of disease risks and better discriminate the paternal and maternal histories. In the real study, we found that the risk for stroke is closely related with maternal history but not with paternal history and that maternal and paternal disease history have similar impact on the onset of myocardial infarction.

A review of family history of cardiovascular disease: risk factor and research tool

Family history (FH) studies have been used to quantify the heritable component of diseases for centuries. Genome-wide association studies (GWAS) in both coronary artery disease (CAD) and stroke have implicated several gene loci in these diseases and have shed light on biological mechanisms, but have not yet yielded fruit in terms of clinical application, partly because of the complexity of gene-gene and gene-environment interactions. Family history studies remain the most accessible way of measuring the inherited component of a disease and they represent the overall interaction between environmental and genetic factors. The current knowledge base for FH of stroke and CAD and disease correlates are evaluated. FH of stroke and CAD are inconsistently recorded in clinical practice, partly because of lack of data regarding family history of stroke and CAD in prospective population studies. Future FH studies are necessary to characterise the role of FH in prognosis and risk prediction of contemporary populations, but also to guide future studies of genetics and epigenetics. In this article, the study design and methodology of family history studies are reviewed. The Oxford Vascular Study (OXVASC) is an ongoing prospective, population-based study of CAD and stroke with very high levels of clinical ascertainment, which allows detailed study of FH, and has already shown important new findings. Such data may help to formulate improved risk prediction tools and to inform future GWAS.

Association between family risk of stroke and myocardial infarction with prevalent risk factors and coexisting diseases

BACKGROUND AND PURPOSE

Familial transmission of stroke and myocardial infarction (MI) is partially mediated by transmission of cerebrovascular and cardiovascular risk factors. We examined relationships between family risk of stroke and MI with risk factors for these phenotypes.

METHODS

A cross-sectional association between the stratified log-rank family score for stroke and MI with prevalent risk factors was assessed in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort.

RESULTS

Individuals in the fourth quartile of stratified log-rank family scores for stroke were more likely to have prevalent risk factors including hypertension (OR, 1.43; 95% CI, 1.30-1.58), left ventricular hypertrophy (OR, 1.42; 95% CI, 1.16-1.42), diabetes (OR, 1.26; 95% CI, 1.12-1.43), and atrial fibrillation (OR, 1.23; 95% CI, 1.03-1.45) compared with individuals in the first quartile. Likewise, individuals in the fourth quartile of stratified log-rank family scores for MI were more likely to have prevalent risk factors including hypertension (OR, 1.57; 95% CI, 1.27-1.94) and diabetes (OR, 1.29; 95% CI, 1.12-1.43) than the first quartile. In contrast to stroke, the family risk score for MI was associated with dyslipidemia (OR, 1.38; 95% CI, 1.23-1.55) and overweight/obesity (OR, 1.22; 95% CI, 1.10-1.37).

CONCLUSIONS

Family risk of stroke and MI is strongly associated with the majority of risk factors associated with each disease. Family history and genetic studies separating nonspecific contributions of intermediate phenotypes from specific contributions to the disease phenotype may lead to a more thorough understanding of transmission for these complex disorders.

Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

For many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation.